Welcome to the session earthquake resistant measures in masonry building, myself Mr. Chetan G. Konapure assistant professor, department of civil engineering, Walshian Institute of Technology, Swalapur. This is the learning outcome means at the end of this session the student will be able to apply the earthquake resistant measures in masonry buildings. Okay, now first measure which shall be taken in the masonry building actually there are various measures of what a few prominent measures we are discussing here. First is that avoid long walls that is most important the sketch is shown here you can see here this is the length of the wall this is more than 1000 mm is 5 meter. So any masonry wall which is unreinforced should not exceed 5 meter length that is very important otherwise it will act as a slender wall. So the slender wall is very vulnerable to damage during the earthquake so what shall be done for this for this types of configuration or this room either you go for buttress which is provided here so pillar or buttress to reduce the length of 5 meter wall so it must be less than 5 meter so you can intermediate you can provide the pillars or buttress or next important thing is if it is not functionally and bring your use or purpose then you go it must definitely you should go for cross wall or intermediate wall. So this will reduce your 5 meter length also as well as this will make this more region little strong also okay. So depending upon the functional requirements either buttress or cross wall can be used to reduce the unsupported length to reduce the slenderness ratio of the wall. Now let us see this actually the section sorry part of the wall is shown here especially in masonry walls what happens the masonry units stone masonry units these are provided in this way from both the faces of the wall the masonry units are provided and whenever any loading will is coming loading is coming on this wall so it acts as two separate elements and the wall masonry units are separating during the earthquake during the vibration. So this is the failure during the earthquake and this has happened in a greater extent in the Kilari earthquake where non-linear construction work damaged with very severe destruction was carried out for during Kilari earthquake that is for the non-linear buildings masonry buildings. What shall be done for this what remedial major major is there you should provide through stone this through stone that will act as a completely integral element or part which will cover both the faces and that will the total load above or the through stone that is that will come on this through stone and that distribute this load to the elements below through stone is it must be provided in horizontal direction in vertical direction after several intervals this through stone should be provided to avoid splitting of problem masonry wall through stone should be provided in sufficient numbers. So after one meter the through stone must be provided it may be in horizontal or vertical direction one more major is there for the masonry wall and for the corners long stone shall be provided at the wall junction. So at the corner of the wall you we should provide the long stone in this way in both the direction of the walls. So this long walls if you provide this joint is becoming intact. So separation of the masonry unit that is we are avoiding by providing these long stones at the corners. Now here you can observe one room and this in room plinth is shown lintel level is shown and even root level is shown. Now this one room building let us consider height of the building 2.1 meter up to lintel 1.4 meter is the remaining above lintel okay. Now this room is the masonry unit brick masonry is shown what happens in the earthquake whenever earthquake starts shaking of these walls. So this is short wall and this is next long wall. So these behaviors of these four wall must be same then only it can sustain under the loading under the vibration. The behavior of these four walls means if so this wall is strong in its own direction but weak in another direction. This long wall that is strong in its own direction length direction and weak in along its width. So behavior of four walls there must be integrity in the behavior and how that can be assured that can be assured and that is through height if the from even its from its base up to height it must the integrity in the behavior that is very important as earth against the earthquake action. So plinth band is product band RCC band is provided at the plinth level. So this is like one rubber band and rubber that is rubber band that actually confining all the elements. So here four walls are there that is confined by this plinth band or band at the plinth level next is at lintel level. So band is provided at the lintel level above opening. So door is there windows are there. So lintel door window opening there again that is a change in the general change in the stiffness. So that is why the lintel band so that is one more rubber band at the opening. So and the last is the roof band or the RCC band at the roof level. So at these three levels so band is provided which ensures the integral action of these four walls or this entire building. So the roof band lintel band plinth bands are very important. Most important thing in this is the opening in the walls and these opening how these are provided. So opening of this window must be having minimum distance of 600 from the corner different or sorry distance in two opening door and window must be 600 mm okay opening of the door must be having 600 mm from the corner of the building okay. So this is at least 600 mm means at least two feet. So this much distance must be there in the opening and away from the corners or the wall. Now this is earthquake bands and vertical reinforcement provided for the masonry building again the same one room building is shown same configuration is there there are two openings window and one wall. Earth pick brands at lintel level is provided plinth level is provided roof level is also provided. These brands are provided in horizontal direction so buildings action is ensured in horizontal direction but that integrity it must maintain and there that integrity can be maintained by providing the reinforcement at the corners of the walls at intermediate levels of the walls at the vertical faces of the openings. So this vertical reinforcement is very important in earlier slide the bands are provided peripherally and in the periphery of the walls that is a its integral action is ensured but in horizontal direction that is very important but in vertical direction these vertical reinforcements are required okay. So where these are provided all these brands plinth level band then earth lintel level band roof level band all these are connected to each other by the means of this vertical reinforcement at corner at openings vertical faces okay. So for openings and corner building corners we should provide vertical reinforcement. Now this is my question for you why bands are provided at different levels of the walls there are four options for A is improving stiffness B higher strength C locate to locate the diameter is integral action of the wall you would think of this question and just find out the answer for this question pause the video write down the answer the answer is integral actions of the walls okay. Now the earthquake bands in the timber are shown in the timber was used in the rural area right now also in several areas the timber is used. So this is this is nothing but like reinforcement vertical bar is provided and timber strips are kept and the concrete or mortar is cast. So reinforcement steel is you are going to use the steel then the hooks are used and these bars are arranged in this way and the concrete is cast. So this this will ensure the proper integrity of the walls and within horizontal and vertical directions. So this is the detailing of earthquake bands in the RC with reinforcement steel bars. Now this is the table which will explain you the earthquake strength provisions in masonry buildings. For zone 2 maximum 15 meter for masonry buildings zone 3 4 storey 15 maximum zone 4 zone 5 then again the height how much maximum we can go that is explained here mortar what type of mortar how many bands are required vertical reinforcement diameter of the bar mortar mix and bracing if any required these things are explained for all the zones for masonry buildings. You can refer this table always and according to this table we should provide all those provisions okay. Now this is also the provision for different type of buildings and zones. These are the sections for the RC bands and depth of the RC band that depends upon zone and the length of the wall or span of the wall. You can see these sections again. You can refer all those sections somewhere it is hook somewhere it is a lateral stirrups are required and it is based on the span of the wall most important and the zone do important factors are there. These are the references for this session. Thank you.


So thank you everyone. I'm really glad to have so many people around from all over the world. We're going to talk about the SARS-CoV-2 in terms of data and data visualization. It's probably not going to be a surprise to any of you that one of the side effects of the COVID has been a deluge of statistics all over the place. If you just look at a few examples, these are just two examples of official websites. So the three federal office for health on the top, the center for disease control, the bottom. These are the groups that you expect to produce statistics in the case of an epidemic. On top of that, there's been a large number of websites that have produced data that have started to create data visualizations. So you have a few here. You have the John Hopkins website, our world in data, the world of meta. And for the Swiss around you at the bottom right, you have the corona data.ch website. So there's been a lot of these and probably this side, most of them already existed before the COVID. And it's not a surprise that they would handle this data. But one thing that has been new to me is the number of medias that have started creating plots that have started creating dashboards all around the world. You have a few Swiss ones here. You have the New York Times and at the bottom, the two at the bottom are the financial time that we'll come back to. All these newspapers they had, all these medias they had data journalists on board, but it looks like all of them have been working on this topic in the past few months. And this topic, as I mentioned, is not that easy to handle. If some things look easy, like you just count cases, you count death and you plot them. But in fact, there's a lot of subtleties here and that's more as what I wanted to discuss today. But I was not interested just in seeing what people did right wrong, how they did it, but really try to go a bit further and say, okay, when we do data analysis at any other place than with COVID data, do we have the same issues? So we ask ourselves the same question. So how would it help us to have these statistics and data visualizations all around the place? And when I say all around the place, I really mean it, there's all even been some papers. This one was in the New York Times, an opinion wondering if obsessing of a daily coronavirus statistics was counterproductive. I'm sure most of you, many of you went through the same kind of scheme where at some point most people were looking at stats every day to see how we were doing. And indeed it was probably a bit too much. So what can we learn from this? Not the obsession, of course, but the way to manage data. And that's what we're going to be interested in today. Before we really start, I just want to mention this one thing that I was not missing when preparing this webinar. It was things to talk about data IDs, because I have literally I think hundreds of tabs open in my web browser with hints and IDs that I could use. So I didn't manage to put in this talk everything that I wanted, not even everything that I promised, because it filled up pretty quickly. If there's anything you expected or you were wondering about, please don't hesitate to ask him the questions or contact me afterwards because it is really a white topic. Okay, now we can really start and I wanted to start from a well known saying that I'm sure many of you have heard when people say we should let the data speak for itself. And there's something logical, there's something intriguing about that, which is we should not book the data, we should not message the data until it says whatever we wanted to say. But still, I cannot avoid not like this sentence, because this sentence assumes that the data has a voice, as I read on social networks. So we should not expect the data to speak for itself, but it's probably a delusion to think that data ever speaks for itself. Whatever we do, there's always a lot of steps where there's human intervention where we have to make choices with this ways to buy the data, either in good faith or bad faith. In the case of the COVID data, it's never been more apparent than in the past. So we're going to look at a few cases why the data does not speak for itself and in how many ways we can see that the data is different depending on who speaks about it. So yeah, well, there's a few reasons why the COVID data will not speak for itself. And I want to be clear about that. The COVID-19 data is dodgy. Wherever you are, it is not possible to have perfect data. And probably this is an understatement, you know, whatever you're doing, it's not possible to have perfect data, but the COVID-19 situation has shown us how complicated it is to get reliable data. So what's the issue? I can make you a long list, but even if you just look at one country like Switzerland, you'll find that there's different sources for the data and they're not current. There's some incurrency that have been going on for weeks. The data is not always updated timely, so sometimes I can go for days and there's some counts missing. At some point, it will be updated retroactively. So at some points, the numbers from one or two months ago will change because there's been some new data. The reporting will change. The methodology is used to count and we'll see a few examples of this rowing in just a few slides. But the methodology is changed. I'll just give you one example. Some countries like France or UK at some point, they were not counting the people who died in nursing homes or old people who were in nursing homes. And at some point when they were convinced that the data was of good enough quality, they started including them in the count. And suddenly you have data that has changed because the weight was calculated to change, so it becomes more complicated to compare the prior data with the new data or to make other comparisons. At some time, it's only a matter of bureaucracy. It's simply the way the data is collected is not really good because it's collected in a way that does not make sense, especially for scientists. So for those of you in Switzerland, you've probably heard this being a lot of discussion in March, a trail about how the federal authorities were not able to account for the cases that they had archaic methods, et cetera, et cetera. But I don't want to delve too much into that because most of the issues I just mentioned are not that bad. It's interesting psychologically to see how much the general public and the scientists, but also general public have wanted, have asked for up to the data and have complained that the data was not good enough. But by and large, in terms of having good enough data so that you can make a policy out of it so that you know whether you're in trouble because you have too many cases where you're doing well. We've had this data. So was it perfect? By far it was not. Was it good enough? From what I've seen, most of it was good enough. We see a few plots. And I've never seen specialists complaining that they didn't get access to the data when they needed. They complained because it was not possible to get some of the data, some correct data. We've come to that in a few slides. But these data, this problem of concurrency, you know, some sources are not the same. This may be a plus 10 cases difference. It's not great. It should be better if it was good. But data is never perfect. And here it was, in my opinion, not too bad. However, if you look closely, you'll find some cases where it doesn't go well. And that's the example I'm going to give you here is the on the bureaucracy side. That's something quite new. It was published in the Financial Times last week. They showed this plot that shows the number of new cases in Leicester in the United Kingdom. And you can see number. You don't have to know much about data visualization to see that we had something relatively high here earlier April and we've been going down. But if you look closely, you will see that we're told that this is only Pila one data. And I guess most of you just like myself a week ago have no idea what Pila one data is. And in the UK, the data is split into several sources and Pila one is everything that comes from tests carried out in NHS and each so the state laboratories. Pila two would be the data based on tests carried by commercial partners or private labs. And these were not publicly available for cities. For example, here Leicester, there were only Pila one data and Pila two data was not provided. So when the journalists find out about that and they looked for for the data I managed to follow, they found that the real craft not like this, but like this, because in recent days it's private laboratories that account for more than 90% of new cases in recent days. And of course, these type of cases is bad, because this is the time of type of data that you could use to base a policy on in a city that gives you the fitting. If you look on here that that everything is going well, one in fact everything is going worse. So this is not good, but to me, it is the exception rather than the rule, by and large, and I cannot talk about all countries, but those I've seen the data was good enough that specialists epidemiologist know what's happening, and they can base their, their policy on this on this data. But again, you know, this is broke I say that's not what I really wanted to talk about today. I was more interested in the, what we can learn in general, in terms of how we can we can handle data in in general. And to really understand some of the issues we have here we have to remember what we do when we do statistics. It's often forgotten, but almost every time when you calculate the statistics and every time when you perform a data visualization. What you're doing is a comparative task. You may have, for example, cases and controls. You don't want to know if your cases are different from your controls. And by cases and controls I mean it could be treated versus not treated or knockout versus wild type if you're in, in general. Whatever is this way you have different groups and a good scientific experiment always has groups because you always need controls, or it could be different time points. It's not a calculation that you follow over time, or it could be different cases. In our case here it could be different countries, and you'd like to compare them as different groups to see if they behave in the same way. So we're always doing a comparison. Most of the time, when people do bad statistics or do or create bad data visualization. It comes from the fact that they perform the wrong comparison. These are the examples that are here. When we're doing comparisons, when we're doing comparisons in science, something that we always do, almost always do, is adjust for confronting variables. Or as we often say, we normalize. That's something that we should always ask ourselves when we get some data. And I'll just give you a very simple example. Let's imagine that you're looking at water and you're counting bacteria. There are two samples, and in the first samples you find two bacteria, and in the second one you found eight bacteria, but the second sample is a four ml sample rather than one ml sample. What no one would say about that is there are more bacteria in the four ml sample. This is true, but we do not care because there are more bacteria because there's more water. So what most people would do is say, okay, let's normalize by sample quantity, and each sample has two bacteria per ml, so basically they're the same. Fair enough. So that's the easy one. There's many cases when you have real biological data where it's not been that easy to find out how you should normalize, but I'm pretty sure all of you who have been in a lab, all of your analyzed data will be like, well, that's kind of easy. And that's fair enough. The problem is, how do we do that when we have COVID data? How can we compare the data and how can we normalize this data? I won't keep the suspense too long, but the main answer is, this is real data, this is observational data, this is not in a lab, and by and large there's not that many ways in which we can normalize the data. But let's see how we could do it. The first one we would absolutely need would be to adjust, for example, number of cases according to the number of tests that have been performed. Most of you have probably heard Donald Trump, the US president last week, who said something along the lines of, yeah, but our numbers are high because we test a lot, and the solution is that we should test less so we look better. It's not clear. There's been a few people saying he was joking. He said he was not joking. That's beyond the point. Problem is, indeed, if you do more tests, you're going to have larger numbers. The problem is, there's no way we can compare. There's no meaningful way in which we can compare results, depending on the number of tests, because the number of tests is not the only variable of interest. More than that, it's the policy on testing. For example, in Switzerland, in early April, when there was a shortage of tests, tests were used only for people who were at risk. So it means they were at risk, or they had really strong symptoms. So there was a good chance if they had symptoms that they would be positive. So we would get a large number of positive tests. If you do something more akin to what countries like South Korea have done, which is just test everyone as soon as you have a small symptom or as soon as you want it, you're going to do many, many tests. I've heard numbers such as in South Korea, for every single positive case, they've done 8,000 tests, meaning that they do a lot of testing, but it's not just a matter of number of testing. It's a matter of policy, of why you do the test and who do you test. And sorry to be blunt here, but I don't think anyone has a good way of adjusting for these matters. The other issue we have is adjusting for the infected population. And that's quite a big deal. For example, you would like to know the mortality rate of this virus. You'd like to know if it's deadly. So we'd like to know among all the people who coat the virus, how many have died. And there were a lot of discussion at the beginning of the epidemic, saying it's 100 falls more deadly than the common flu or 10 times, etc. And again, no one knows really this number so far. It's not the number of tests that will help us find out about the infected population, because as I just said, it depends on the policy. But in the future, if we're not in a hurry, we may get surgical data that will tell us how many people have antibodies. And so we should be able to get an idea of how many people coat it. This comes with its own issues. But my main point here is, at some point, we must be humble and realize that we just do not have the data. And I'll just give you two examples. So one thing that is more reliable than tests is the number of death, of course, because you may miss someone who have coronavirus, because you may not test him or her and don't know about this person. Stages, but it's harder to miss a death. Still, not all deaths are recorded, not all deaths are assigned to coronavirus, but you can see there's quite a few countries that have started creating this plot or always did, but they've become permanent. This plot here for Switzerland, that's the platform yesterday, shows the green curve is the normal range of death week in Switzerland. So we start at the beginning of the year, that's the end of the year. You see that in winter, the normal range is a bit higher than in summer. This is due to the flu and other diseases like this. And you can see here this black curve, which shows an increased number of deaths that were not accounted. There's about 30% more deaths at that period than what we counted due to the coronavirus. The problem with these numbers is they're much better than anything else, but they're definitely not perfect. They're definitely not perfect because, right, someone had an issue in the plot. I hope it works. Now I've tried moving back and forth. It's not perfect because the authorities have admitted that we don't know why these people died, maybe some of them died because they were confined because of some other reasons. And we do our best, statisticians do their best here, but we'll never have the data. So what is important here to remember is getting data is complicated. In many cases here is going to be impossible, and making comparisons is going to be hard because of that. So we'll have a quick look afterwards at what we can compare, what works or does not work, but there's many things that do not work. One trick is I'm just showing you another graph here. This is data from Florida. One trick is you should not rely on a single number alone. For example, you should not just rely on the number of positive tests. You should not just rely on the number of tests. For example, here you see this is not one of my favorite graphs. I'm not a big fan when there's two data sets on the same graph. So you can see the light ground bars show the number of tests that were performed among several weeks. The dark bars at the bottom show the positive tests and you have a line that shows the percentage of positive tests. You can see that since March, the number of tests that are performed has increased, which is a good thing. We're better at finding whether people have a virus. The number of positive tests has increased as well, as you can see at the bottom. But if you just have this information, these two pieces of data, it's hard to say whether the number of positive tests increased just because the number of tests increased. Of course, if you do more tests, you can only find more positive people. That's where, for example, a second piece of information like the percentage of positive tests will give you an insight. In this case, it was high at the beginning. In March, it went down and then it started going up again. And that's the worrying part. Okay, you're screening more, but in proportion, each test brings you more cases. So that shows that it's not only because you're testing more, that you get more cases. It's also because there are slightly more cases. And if you want to lie with statistics, it's very easy to pick only one number or just one part of the graph and say, look, it's not going up, it's going up before this reason or it's going this way, etc. You have to look at several numbers to really get a good feel of the situation. And even then, it's not possible to do anything perfect. Another type of normalization that has been discussed a lot is population size. And I think this is a really important one. If you're looking at cases of cancer in the country, it's going to be very natural to consider the number of inhabitants in the country. So you don't want to say, okay, we have 1000 cases of cancer because 1000 cases of cancer in Switzerland or in the US or in China is going to be something very different. And in this case, there's no reason why cancer would probably come not randomly. We perfectly know that it's not random. But within the country, depending on some of the general policies of the country like screening, like the way the health system and a lot of things. It's natural to think that the number of cases of cancers, that depends on the number of inhabitants. If you have 10 times more people in the country, you should naturally have 10 times more cancer. Even that is not a given. For example, you have often a lot of heterogeneity within the country where some part of the country will have more cases for reason or another. In a sense, there's no reason in many cases why about borders, official borders are the natural way to compare diseases. Of course, when you're looking at the effect of a policy, for example, if the Swiss government decides that everyone should get a vaccination against the disease, it makes sense to see what effect it has globally in the country. But in many cases, there's no reason why it's the country level that is important. And in the case of the spread of a virus, by and large, it does not depend on the number of inhabitants. So when you have a country that has no one who has COVID, that's Switzerland in mid-February, for example, when you have one case, the first case, this first case is going to spread in the direct neighborhood of the first person infected. And whether the country has 100 people, 100 people, 100 people, 1 million people or 1 billion people will not make a difference. What's going to make a difference is the number of hotspots. It's the number of people spreading the virus. But at least at the beginning, having a large country or small country will not change much the spread of the virus. Of course, after a long time, when there's almost no one left to infect, the curve will stop and the number of people who spread the virus will depend on the number of inhabitants. But contrary to other measures, it mostly does not make sense to calculate the relative infection of people in the country. What you get if you do that, if small countries will proportionately seem badly affected, because you need only one person, you take a small country like Luxembourg, you need only one person to come and by itself, this person will give you a really high rate and it will transmit the virus at exactly the same speed that a single person would do it in the US or in China. So depending on policies, of course, but at first when we don't know that the person has symptoms, it will be the same. So that's the reason why if you look at many graphs, if you look at many data sets about COVID, they will mostly show you absolute numbers. And even though that may seem a little bit strange, because that's not what we usually do in science, it's probably the right thing to do. If you go to some websites, I'll come back to this one, but the Financial Times is one of my favorites. You will see, if you look at the roll numbers, it's a quite nice plot, I've put four countries here. You can see Luxembourg is quite high compared to other models, because despite having a small population, the number of cases does not really depend on the population. What you can see is that if you change that to a number of cases per million in a bit, you can see that Luxembourg, which is the light blue curve here, is much higher than everyone else, at least at the beginning. Why? Because you just need a few infections and you infect a certain number of people of a low population, so a high rate, while other countries will have a low number of people of a much larger population. I'm not saying one of them is better than the other one, but you can see it's always a bad sign when changing the way your present data changes the story. With Luxembourg being here at the top, but very quickly at the bottom of the four countries, and here being over all of them and then crossing again. So it's a good idea to look at different ways of looking at the data and compare them, otherwise we may get bad data. Or bad conclusions, at least. Now, let me go quickly before we talk about data visualization of different things that can change your interpretation of the data. Your data may have different starting points. For example, not all countries had the first case at the same time. So how do you compare them? How do you compare countries where the epidemic started maybe a month apart? I did a quick survey of several websites that show COVID data. Then I found, for example, these four ways of representing data, some fact aligned data on the actual date. So there will be a shift if it's a country that got its first case last year or in March. Some of them look at the day since 10 daily cases were first recorded, or the day since the 100th case was recorded, or the day during which the number of cases reached one per million inhabitants. Each of these may give you a slightly different way of representing the data. And they're all arbitrary. There's no right reason, there's no perfect way to align the data. And if we go back to what we were looking at before, you'll see, for example, on the road numbers, this is the French time. So they use number of days since 10 daily cases first recorded. And you can see here the scale. And you can see on this one, if you compare Luxembourg and the United States, they're quite far apart. And most importantly, which one did I want? Luxembourg or Belgium are more the same here. But I see my arrow has changed a little bit since I created it. And you can see that the United States is at a different time point than Belgium or Luxembourg, which are themselves at two different time points. But if you look at the other graph, per million inhabitants in here, the rule of the French time is they aligned the data on the number of days since 0.1 daily cases per million were first recorded. You see that the fourth whole country we're looking at here, but mostly the three were interested in the United States, Luxembourg and Belgium. They all at the same time point in the epidemic. So that gives you an idea. Here we have maybe 20 days difference and here they're aligned. I'm not going to say which one is best because there's no best. But again, we have to be careful that these little changes in how we normalize the data and how we decide on the starting point will change your story. It will change what the data looks like. It will make them look like they are the same point in the epidemic or it will make it look like some of them are way beyond. Again, that's slightly wrong. That's a good sign that the data is hard to interpret and we should be really careful about it. What you should do, in my opinion, it's quite hard to compare countries for all the reasons we decided discussed. What you should do is compare each country with itself and you can compare the shapes of the curves. For example, I go back to the exact same graph. You can see that these four countries follow more or less three different trajectories. Luxembourg went a little bit high and then down quite strongly and then they're going up again in some way. Belgium and the United Kingdom had the same trajectory. They went up and then they're going down like a firework. They're going down very slowly. And it looked like the United States was following more or less the same pattern except that they seem to be going through a second wave when the first one is not finished. So this is a comparison we can make. Comparing which curve is highest, which curve shows the largest number of people is a bit harder to do. It makes more and more sense as time goes by because the differences between small countries and large countries will tend to disappear as the epidemic spreads. But it's hard to make any conclusion from vastly different data. Okay, I'll just skip that because we don't have that much more time. With little things before I give you the final words on data visualization, some people have been looking at cumulative cases, other ones have been looking at new cases. Here I'm quite confident we should look at new cases. Why? If you look at this plot that was shown by Donald Trump in the White House that shows the number of tests that were performed. And you can see this is a cumulative curve. You can see that the number of tests increases. And it can only increase because it's a cumulative curve. So whatever you do, each bar contains all the previous bars and it's only going to increase. And the goal here was to show that the number of tests done by COVID increases. But of course it increases. It cannot decrease. If you plot it that was done by someone look at this plot and you plot the number of new tests, you see that the number of new tests does not increase. What has changed is at some point here, it's not read but probably in March, the number of tests increased from very low to a bit higher. And then it was quite stable. So while the first graph is the impression that the testing capacities have increased, it's just a total number of tests that have increased. The number of testing capacities has stayed the same. And that gives a very different message. Now I will argue that what we're mostly interested in is not the cumulative number between what happens on a day to day basis. If I go back to my curve here, we don't care about the fact that Luxembourg went up and is going up at a certain scale. What we care about is the fact that it was up and then down and then up again. We'll see it again. There's just one thing missing to be able to understand really the data. And that's probably the most important question of all is whether we should use a log scale or a linear scale. And in science in general, there's many good reasons to log data. One of them is when the data spans several orders of magnitude. That happens quite often when you have genomic data, for example, you have some counts that are very small, 10, 100, and some others that are in the order of a million. That's a good reason to use logs. When the data changes by a multiplicative factor rather than an additive one, often these two reasons are linked. And in the case of an epidemic, it's clearly what's happening because every person who has the disease is likely to infect maybe another two people depending on the current values of the propagation variables. So that means every person infects another two who themselves may infect another two. So we have a multiplicative factor here. And when you do this, you get curves that will be easier to interpret on the log scale. So in the case of the exponential process that we have here, both reasons apply. If you look at the cases in Switzerland, the cumulative curve, you can see we went up we have this curve that is shaped like an S. And it's hard really to see what's happening in the last few days, while it's a bit easier on the new daily cases. But even on the new daily cases, because the curve went up to more than 1000 cases a day, the scale does not allow us to see the little things that happened in the in the recent days. I didn't mention it earlier. The flight has skipped. We're about smoothing the data. So what you see in gray in the background is the actual data per day. And the blue curve is the smooth data. It's quite important to smooth the data, because there's a lot of viability from one day to another from a weekend to a day, a week, day, etc. And these curves are much more really good. So new daily cases is a bit easier to interpret the cumulative cases. But most importantly, if you plot the data on the log scale, the cumulative curve is not that interesting. But the new daily cases curve comes back to what we are seeing in the financial times curve earlier. You can really see not only what is happening when we have high numbers, one 1000, but also you can see this trend, this unfortunate trend going upward here. To me, what is really important is to realize that when you add 15 new cases within a day, it is huge in Switzerland. 15 new cases is about what we get at the moment. So if you're telling me that we are doing another 50, we get to 100, it's a big deal. It means the epitome is out of control. If you add 15 new cases in the US, it's completely negligible because they already have three orders of magnitude louder than that new cases per day. And your data and the way you look at it should reflect that. So in Switzerland at this point, adding 15 new cases is a big deal and we should see it on the plot. In March, when we were at really high levels, adding 15 new cases would make no difference and the log curve will not show anything here because these are additive effects. So in terms of multiplicative effect, when you add 50 to 50, it's multiplying by two. So it's a large effect and you see it on the log curve. When you have 1000 cases and you add 50, you're multiplying by 1.05, I think 5% so you don't see it and you should not see it on the curve. If you see it, it means you're not be able to see other things. So I have little doubt and that's the reason why most graphs favor the log data and the FT curve that I've showed here, I've showed a few other versions before. They use the log data and you can see exactly what's happening in Switzerland. You can see France going up and down. You can see the United States. This date from two weeks ago, you can see the United States being flat and you can see all of these, you can compare the trend, which one is going up and down, even though the order of magnitude are very different. If this was done on a linear scale, Switzerland would just be flat and you wouldn't see anything and definitely not the shape of the data. Just a warning, data on the log scale that may be counter-intuitive, most of us are not used to interpret it and even if we are, it's harder to interpret. I'll just give you one example. Imagine that this is your curve on the log scale, so you've got days and you've got a curve that shows a number of cases going up. Then most people would look at that and say, look, we can see that this curve is flattening. We can see that it's not as steep as it used to be. Well, if you look at it on a linear scale, you'll see that the curve is not flattening at all. The problem is this curve that I made up is not linear, so it's not linear on the bottom plot. It goes up with a slope that is ever-increasing, but it's not exponentially either. It's in between. It's a power curve. So it's less than exponential, so the log curve will go down, but it's more than linear, so the curve is not linear and go up. So that's the kind of counter-intuitive information you could get and one of the reasons why you should always look at data in several ways. You cannot just trust one way and forget about it. As a kind of conclusion, I just wanted to say a few words about data visualization in general. We've talked a lot about data visualization. Log scales are very useful for data visualization. As I said in the beginning, a lot of people have been doing data visualization with the COVID data to the point that some people, as the writer wrote articles, and that was only mid-March to say it's nice if you do data visualization, but it may not be a good idea to publish it, especially if it doesn't bring anything new because you may not know the caveats of the data, you may not know the constraints, etc., and you should be careful about that. This being said, we're not going to complain about the fact that there's been a lot of data, there's been a lot of useful data there. So what are some of the issues? First, some people as often have used data to live. There's not so many, but this is one of the rear graphs that I suspect was done in bad faith. It's the data of different counties in Georgia, in the US, and you can see that there's a clear trend, you have dates here, and there's a clear trend that the number of cases is going down with time. Except that if you look at the dates, they jump from 20th of April to 27th, 2029 to May to April, etc., and that more or less it looks like there were orders so that the bars would be descending. After this was noted online, they did a new plot, it's the exact same one, the colors change, you can see the big blue bar here is the big green bar here, and you can see that the story when the ordering is right is a little bit different. We go up at the beginning, I mean it's already April, at the beginning of the curve, we go down and then we seem to have a U-shaped curve. The final data points here are not complete because that was a plot made in around 10th of May, but you can see that the message and the policy you may decide to follow depending on this data is different depending on whether you see this or this. And you can compare them, they don't give the same message, even though it's the exact same data. One problem when you do data visualization is many people create puzzles, they don't create tools that are useful for transferring knowledge to create puzzles. So this is a case from the White House, let me just zoom in on it, you get all 50 US states. So it's the cumulative cases, we've discussed the fact that it may not be the ideal measure, but you can see that basically you see nothing here. The blue state that is at the top, it's a blue curve, so basically you have a list of blue states here which could be then. If you have domain knowledge, you may remember that the most, the worst affected states in the US was New York, so it's likely that's one. And this one may be New Jersey, but by and large we have spaghetties here and no way to get much out of it. It was also the case on the Swiss corner data to see it website. It's a fantastic website created by a PhD student in bioinformatics, who took the task of collecting data from different official sources and making it available and does a really good job about it. I'm not that keen on the data visualizations. These are the different Swiss contents, 26 of them, a long time. And frankly, it's very hard to get much out of that. But if you use interactive visualizations as he provides, it may be easier to just pick whatever you want. But on this one, it's a hard to get much information. So if I talk about the puzzle, what I really mean is, you have one piece of the data that is, for example, the yellow parts here and the other piece which is the legend and you have to please them together and it's a hard stuff. I'm getting to the end. I skip this part. What I wanted to say is that there's been some issues in scientific papers as well that don't use so much. So great data visualizations. And the problem is these papers have been read not only by people are used to read them and to look at the details but by people who look at them and make wrong interpretation because it's the first one they see some of the graphs. So this really needs to be careful about that. It's not all bad. I've shown you a few bad graphs here. One of the main articles this one is in the Washington Post explaining how graphs may have saved millions of American lives. Of course, it's the flattening the curve graph. It's interesting because it's not a true graph. It's kind of a makeup graph that shows that if we flatten the curve we may avoid having too many people in hospitals. This has been everywhere in March it's been quoted it's been printed it's been shown on TV and quite a few people believe that it has made a difference. So that's quite positive. I really believe that it's a good thing to have all this data around my favorite data set my favorite visualization as you've guessed is from the financial times. There's a lot to learn in how you can do plots in the science science when you have your own data, because these people have thought a lot about sending messages. And as a last graph, for example, this is that take on the number of excess death, you can see the Swiss graph that I showed you earlier. To me this is a fantastic graph because they have a really clear legend that is explained in the text. And even without looking at the legend you see that there is something special in red something behind in black, and there's really a lot of attention in the product makes it much easier to read and many. So we didn't have that much time but we covered quite a bit of ground. It's never enough to rise data using a single number. We all want to do that we all want to have just a number of positive tests and compare countries or similar data data point. And it's always complicated. It's always much more complicated than than that. As a final conclusion, two things. Statisticians like to quote from French writer Gustave Flaubert from the 19th century. He was really complaining about something he called the rage to want to conclude the rage of always wanted to conclude so of always meaning that you need to have an opinion you need to have a conclusion and not being able to say, I don't know. And the problem is with such data. This is really good example but in many cases when I have real world data, we cannot conclude we don't have the information and we have to say, I don't know. It's not all bad. For example, this is not science but it's an economy. I've seen these news that the United Kingdom, for example, have asked have started to provide economic data like GDP with uncertainty, not just one number, which is gospel but to say okay, this is a prediction and I know we may be wrong between this number is this number. This is what most people do in science. We love error balance, we show uncertainty, not always, but it's, it's something we commonly do. But if this pandemic has told us one thing, which is that everything is uncertain. And we use that when we do data analysis, it's going to be at least one positive outcome. I wanted to say today, so thanks a lot for your attention. Those of you have asked for the slides, you will have access to the recording and you've seen that I've put references to most of my slides so you will get that as well. If you want to go further. And as I said earlier, do not hesitate to ask me if you have any other question or you want to have references or anything. Thank you for your attention.


Good day, fellow investors! These 20 lessons from Hedge Fund billionaire Seth Klarman, a person, a value investor that achieved 16% to 18% yearly annualized returns over 30 years from 1983. Yes, when value investing was proclaimed dead over and over and over again. So he destroyed the market and he shares 20 extremely valuable lessons in his 2009 annual letter that I came across and here are those letters, you can also find them online, but they are so important that I wanted to make a video so that we can listen to them constantly no matter where you are because if you listen to this you will be an investment success in your lifetime. Let's go! 1. Things that have never happened before are bound to occur with some regularity. You must always be prepared for the unexpected, including sudden, sharp downward swings in markets and the economy. Whatever adverse scenario you can contemplate, reality can be far worse. 2. When excesses such as lax lending, standards become widespread and persist for some time, people are ruled into a false sense of security, creating an even more dangerous situation. Nowhere does it say that investors should strive to make every last dollar of potential profit. Consideration of risk must never take a backseat to return. Conservative positioning entering a crisis is crucial. It enables one to maintain long-term oriented clear thinking and to focus on new opportunities while others are distracted or even forced to sell. Portfolio hedges must be in place before a crisis hits. One cannot reliably or affordably increase or replace hedges that are rolling off during a financial crisis. So it is okay to have cash and that is why I have about 70% cash at the moment in my model portfolio. So, conservative, first, don't lose money, protect your principle and the returns will come. Thank you, Seth. 4. Risk is not inherent in an investment. It is always relative to the price paid. Uncertainty is not the same as risk. Indeed, when great uncertainty such as in the fall of 2008 drives securities prices to especially low levels, they often become less risky investments. As always, buy low, sell high. When you buy low, you are at low risk. Low risk, high return. That's it. 5. Do not trust financial market risk models. Reality is always too complex to be accurately modeled. Attention to risk must be 24, 7, 365 days obsession, with people, not computers, assessing and reassessing the risk environment in real time. Despite the predilection of some analysts to model the financial markets using sophisticated mathematics, the markets are governed by behavioral science, not physical science. So run away from Greeks, run away from models and use common sense. 6. Do not accept principal risk while investing short-term cash. The greedy effort to earn a few extra basis points of yield inevitably leads to the occurrence of greater risk, which increases the likelihood of losses and severe illiquidity at precisely the moment when cash is needed to cover expenses, to meet commitments or to make compelling long-term investments. So be careful when chasing yields, be careful when chasing those dollar high yields if you are a European investor that you see now in treasuries. 7. The latest trade of a security creates a dangerous illusion that its market price approximate its true value. This mirage is especially dangerous during periods of market exuberance. 8. A broad and flexible investment approach is essential during a crisis. Opportunities can be vast, ephemeral and dispersed through various sectors and markets. Rigid silos can be an enormous disadvantage at such times. 9. You must buy on the way down. There is far more volume on the way down than on the way back up and far less competition among buyers. It is almost always better to be too early than too late, but you must be prepared for price markdowns on what you buy. So when you find quality, when you find great businesses, be ready to buy when it is at a fair value and an average down if you are blessed with lower prices. 10. Financial innovation can be highly dangerous, though almost no one will tell you this. New financial products are typically created for sunny days and are almost never stress tested for stormy weather. Be careful of new financial products, especially ETFs that haven't seen stormy weather yet. Ratings agencies are highly conflicted, unimaginative dupes. They are blissfully unaware of adverse selection and moral hazard. Investors should never trust them. 12. Be sure that you are well compensated for illiquidity, especially illiquidity without control, because it can create particularly high opportunity costs. Further, at equal returns, public investments are generally superior to private investments not only because they are more liquid, but also because amidst distress, public markets are more likely than private ones to offer attractive opportunities to average down, because the averaging down is the key to long-term successes.  14. Beware leverage in all its forms. Borrowers, individual, corporate or government should always match fund their liabilities against the duration of their assets. Borrowers must always remember that the capital markets can be extremely fickle and that it is never safe to assume a maturing loan can be rolled over. Many LBO's leverage buyouts are man-made disasters. When the price paid is excessive, the equity portion of a leveraged buyout is really an out-of-the-money call option. Many fiduciaries placed large amounts of the capital under their stewardship into such options in 2006 and 2007. 16. Financial stocks are particularly risky. Banking in particular is a highly leveraged, extremely competitive and challenging business. Put it into the too hard pile to analyze. 17. Having clients with a long-term orientation is crucial. Nothing else is as important to the success of an investment firm. So, thank you all my clients for understanding my current 70% cash position in the model portfolio and your long-term orientation. We are building something great here, so thank you, thank you, thank you. 18. When a government official says a problem has been contained, pay no attention to that. 19. The government, the ultimate short-term oriented player, cannot withstand much pain in the economy or financial markets. Bailouts and rescues are likely to occur, though not with sufficient predictability for investors to comfortably take advantage. The government will take enormous risks in such interventions, especially if the expenses can be conveniently deferred to the future. Some of the price tag is in the form of backstops and guarantees whose cost is almost impossible to determine. What will be the cost of the current global deficits, budget deficits in the future for future generations? We will see. 20. Almost no one will accept responsibility for his or her role in precipitating a crisis. Not leveraged speculators, not willfully blind leaders of financial institutions, and certainly not regulators, government officials, rating agencies or politicians. So, 20 amazing lessons from Seth Klarman. Thank you, Seth, for sharing this with us. I firmly believe that if you follow these 20 rules, and I will try to incorporate these 20 rules into this channel for the long-term, for the long-term success of all of you that are subscribed here, and all of you that are working with me on the stock market platform to make amazing long-term returns, that will be amazing if we keep to these principles shared by Seth Klarman. Investing is easy in the long-term if you just adhere to simple long-term things that worked out in history, and stay away from the leveraged crazy investments that didn't work out in history, and will probably not work out in the future. Thank you for watching, looking forward to your comments, and I'll see you in the next video.


So, what am I going to talk about today? Well, I'm going to talk about a technique that's inspired by pseudo-random generators that allows us to use a small number of group elements that are random to replace a much larger number of elements that are in the public parameters of certain functional encryption schemes that employ the dual system proof technique of Bishop and Waters. So, the particular application that we're going to look at is attribute-based encryption, and this is the same setting as the last two talks, where we want to encrypt messages under a set of attributes or tags, and we're going to associate keys with policies that take in sets of attributes and either grant or deny access to the underlying plain text. And we want our security model, again, to include collusions, so that we want no colluding set of unauthorized adversaries should be able to decrypt the message. So, the particular construction we're going to look at is, it uses bilinear groups, and so these contain just a prime-order source group, target group, GT of the same order, and a bilinear map, which takes in two source group elements and returns a target group element. And this satisfies the property that E applied to G to the A and G to the B returns the target group element EGG raised to the AB. Furthermore, we want this property that there's no efficiently computable isomorphism from the target group back to the source group, so what the bilinear map gets you is effectively a one-time only multiplication in the exponent. On either side of this, you can do the normal group operations that give you normal addition in the exponent. So the first scheme I want to look at is the classic scheme of Goyal, Pandy, Sahay, and Waters for attribute-based encryption. So in this scheme, the public parameter is you're given a single target group element, EGG to the alpha, where the alpha is secret.  And so to make a ciphertext in the scheme, you take the public EGG to the alpha and you raise it to your secret S that you choose, which is a random exponent, and you use that to blind the message. Then you additionally give out G to the S and G to the S AI for each attribute that you'd like to associate the ciphertext to. Remember, we're encrypting ciphertext under sets of attributes. So for keys, the key generator will take the secret alpha and generate a linear secret sharing of alpha under the desired policy. And so this creates shares, which I'll call lambda i. And these lambda i are each associated with a given attribute. And they have the property that if you have a set of lambda i that correspond to a set of attributes that are authorized by the given policy, you should be able to recreate alpha linearly from them. And without such a set, you should be unable to reconstruct alpha. So for the key, what we give you is G to the lambda i for all these lambda i. And masked kind of by this AIRI, where AI is the corresponding attribute to the lambda i and RI is a random exponent chosen. And you also give out G to the RI. So the idea to decrypt what you'd like to do is somehow come up with EGG to the S alpha, I mean, yeah, EGG to the S alpha so you can divide it off of the ciphertext and get the message in the clear. So what you're going to be able to do, how you get that is by using the lambda i in your key. But you can't really use the lambda i as it's given to you because it has this AIRI that's going to mess things up.  So specifically what we do for decryption is you first pair these two elements together which remember gives you the one time multiplication in the exponent. And so the S of the exponent in the ciphertext gets multiplied by the exponent of the key element and you get the target element, target group element EGG raised to the S lambda i minus S AIRI. And then you pair these two elements together and you again get the same multiplication in the exponent to get EGG to the S AIRI. And now you can just do the normal group operation here to remove the S AIRI and just get EGG to the S lambda i for all the attributes i that are in the ciphertext. And so assuming these attributes are an authorized set for your policy then using the properties of the linear secret sharing scheme you can linearly recombine these using the group operation to get EGG to the S alpha. And then divide off and get the message. So that's how decryption works for GPSW and if you stare at GPSW for a while you'll notice that the full security proof for it requires that the public parameters due to the AI are independent random elements. And not only for each attribute but also for every attribute use in policies. So if you want a policy that uses attributes more than one time, you need an actual copy of fresh new G to the AI for each use. So the point is that these public parameters can get very large if we have a large set of attributes that we want to support in many uses. So it brings up the natural question, can we get away with publishing less than one attribute, one element per attribute use in the public parameters? So this is a question that Akamoto and Takashima achieved a smaller version of public parameters in 2012 for ABE using different methods. But we're interested in trying to see if there's some small tweak that we can apply to an existing scheme like GPSW that might also be usable in other instances. So what we want to do is take, somehow maybe publish a smaller set of group elements that we might use to replace the independent random lots of GI that currently are in GPSW. And so the first thing that might come to mind is using a pseudo random generator. So one idea that you might have is to say that we're going to use the output of a pseudo random function to generate random looking attribute elements that are going to replace the original independent random elements at G to the AI. So if we look at an Orion Gold, which is a classic PRF for prime order fields, how this works is you have a small number C1 through CK of random exponents and the output of the function is defined to be a subset product of these exponents in the exponent of G, where the exact subset is determined by the input X. And we want to try to create as many possible random elements as possible, so we're going to consider evaluating our PRF on all possible inputs. So if we were going to adapt this to our situation, what we'd want to do is take all possible subset products of the exponents. So for instance, my public parameters now might be a much smaller set G1, G to the C1 through G to the CK, and to replace the old G to the AI, which I'm now calling G to the A K, I would just take all possible subset products of these in the exponent, which would be cool. But the problem is that we can't do multiplication and the exponent of group elements, we can only do addition with the normal group operation. You can, with bilinear maps, let you do one multiplication, but then it takes you to the target group and you can't do any more multiplications past that. So if we want, we definitely can't get all the possible products that we'd want to this method. So what we can do is use the normal group operation, which would just give us subset sums in the exponent instead of subset products. So if I was the same thing as before, I'd publish the same G to the C1 through G to the CK, and I would replace my G to the A K with subset sums of these G to the C1 through G to the CK. And so that's cool, but on their own the problem is that these elements don't look random anymore. You can, I can't just say that if I gave you G to the A K for all possible K that you can't tell whether I made these through the subset sum construction or if I chose each A K randomly and independently. You can tell, because note that if they're structured, then G to the A sub one times G to the A sub two is equal to G to the A sub one two, just because you have this relation. And this is something that you could check, so you could easily tell whether I gave you a structured version of G to the A K or a random version. If it was random, it would not satisfy this relation with that. But the thing is that the G to the A K don't show up on their own in GPSW ciphertext and secret keys. If you recall the ciphertext and secret keys look like this. So in ciphertext, the G to the A K is kind of masked by this S that shows up somewhere else as G to the S. And the secret key is kind of masked by this R K that shows up elsewhere as G to the R K. So you might think that maybe this is enough, like this might be able to hide the subset structure a little bit more. We have more to play with here. And so that kind of turns out to be the case, so I'll talk more about it. But first, I want to consider a simplification. So imagine that the GPSW ciphertext and secret keys, they only ever use G to the A K masked by a random exponent. And by that, I mean they only ever occur like they do in the secret key. So here the A K are masked by this R K for every K. And so what I'm asking you to ignore is the fact that in the ciphertext, the A K is masked by the same S for all K. So in particular, I want you to consider a version of the scheme that, imagine that it somehow makes sense to change this to be S K's instead of S's. So now each A K is masked by an independent random S K for all K. This change is going to make it easier for the rest of this proof. And it still will retain the same idea. So in particular, the way that A K is used in the ciphertext and secret key is now the same. It's this general form G to the Y K A K and G to the Y K, where Y K is an independent random element exponent per K. So if I gave you these and asked you to tell me whether I gave you the A K as a subset some structured versions or independent random exponents. You can't do the same trivial attack that we described earlier, where we're just adding the sets together to see if they add up. And so you might hope that this means that they're really hidden. And that turns out to be kind of the case. So this is the main technical limit of our paper. The simplified version, obviously that applies to the simplified version of the scheme I just mentioned. So I give you a small number of random exponents, C 1 through C K. And well, I don't give you them, but I choose the small number. And for each non-empty subset, I'm gonna choose a random exponent Y K. And then I give you G to the Y K and G to the Y K R K. And you need to tell me whether R K is equal to this subset sum for all the K, or if it's independently randomly chosen for each K. So if we had this, what we'd like to do is just publish these G to the C 1 through G to the C K as public parameters now. And use subset sums in the construction of our ciphertext and secret keys and then use this lemma to transfer to the version of the scheme where we're no longer using subset sums, we're using random elements. And which is just like the old GPSW and then proceed with the rest of the GPSW security proof. So we prove this using a hard assumption, obviously. So we use the decisional linear problem, which is the same assumption that GPSW is proven under. And so it's the following. Given the following elements where Y 1, Y 2, C 1 and C 2 are chosen at random, you're given a challenge term T. Where T equals G to the C 1 plus C 2 plus R. And R is either randomly chosen exponent or it's zero. And so if it's randomly chosen exponent, then T is random. And if it's zero, then T is related to the previous elements that were given because of the C 1 and C 2 that are repeated. Yeah, so if we look at the base case of our dilemma, where K equals two and by this I mean there's only two random exponents that are chosen, so this is what it looks like. We give you G to the Y 1, G to the Y 2, G to the Y 3, G to the Y 1, C 1, G to the Y 2, C 2 and G to the Y 3 times C 1 plus C 2 plus R.  So if R is zero, then note that this is subset sum structured. Because the Y 1, C 1 is the singleton set one, the Y 2, C 2 is the singleton set two. And the Y 3 is attached to the full set, C 1 plus C 2, the subset sum. But if R is random, then this whole term is random. And these three are just independent random values. And so we're in the random case of the lemma. So it's written very suggestively here. It's an easy reduction from a decision linear problem. Because most of these terms are actually even given to you directly through the decision linear problem statement. Like you have all these four terms. And the only thing you don't have is this G to the Y 3. So you can just choose your own Y 3 and give out G to the Y 3. And then take your target term T, your challenge term T, which is G to the Y, C 1 plus C 2 plus R. And then raise it to that Y 3 to make that element. And so it's a direct reduction and you enjoy the same advantage deciding the deal in problem as you do in deciding this problem. So this is the base case of the lemma when K equals 2. And so for the general K, we do this by induction, which I'm gonna skip because I don't want people to fall asleep. And okay, so now, how do we embed this once we have it assuming for all K into GPSW, the actual, the original scheme? So in the dual system proof of security for GPSW, we change the security game. The security games first change to one where the secret keys and the ciphertext contain like a parallel copy of themselves in a new subgroup. So if you recall the secret key and the ciphertext in the simplified version that I asked you to consider, all have these elements G to the RI AI where RI is per attribute. So the first thing that happens in the security proof is we switch to a version where we make secret keys like this where we have an extra subgroup element G to the RI tilde, AI tilde, which the extra subgroup element I've written in red here. And the important part is that these RI tilde and AI tilde are independent and random. So the AI tilde in particular aren't tied to the public parameters G to the AI that these, like these are. So the randomness of these untethered AI tilde is then used in an information theoretic subargument to argue that an unauthorized adversary can't decrypt the challenge ciphertext. So what we're going to do now is we publish this really small number of subgroup elements G to the C1 through G to the CK and use subset sums to replace the old AI and GPSW. When we do this, when we do this subset, when we expand into the new subgroup, the subset sum structure extends and we get these subset sums of these untethered CI tilde. And this is where we plug in the bilinear entropy expansion. Because now that these CI tilde are no longer tied to the public parameters G to the C1 through CK, we can just plug the lemma indirectly and then change from a game where these CI tilde are subset sum structured to one where they're independently randomly chosen for each K. And this is the same as the old version of GPSW where the AI tilde were independently random. And so the same information theoretic subargument can still be run and the rest of the proof proceeds just like the same. So, I've shown how this bilinear entropy expansion lemma can be used in a really modular way to improve the fully secure ABE scheme of GPSW and achieve exponentially smaller public parameters. And because of this modularity, how it just sticks in this one part and changes to a point where we can follow the rest of the proof through. We're hoping that it might be similarly useful in improving other functional encryption primitives that use the dual system methodology in a similar way. So if anybody has any ideas, I'm definitely interested in hearing. So feel free to talk to me. That's it, thank you.


Yo what's poppin people welcome to another video if you guys clicked on this one you guys are getting a treat today You're freaking lucky if you clicked on this video because you're about to see something you've never seen before I got the boat behind me right here. I'm hoppin in my truck because we're wondering what's in this back. What? Huh? Say hey to the vlog Guys we got a treat today I mean a treat so I'll fill you guys in a little bit more but if you guys are wondering what's in this backpack I got a drone for my birthday my birthday and Christmas long story short my birthday's New Year's Eve December 31st of December So my parents got me a drone for my birthday and Christmas big shout out to them think them so much for that I've been waiting to buy one But they ended up getting me one. I got it in this little backpack right here So we're gonna take it out today, but we are doing more than just that I actually have a guy that's coming I'm not even gonna spoil it. Let's go ahead and hop in the truck drop the boat in the water You guys are gonna see some crazy stuff today. I also ended up buying myself for my birthday or Christmas I bought myself a new camera if you guys like the quality a lot better Let me know it's actually the same camera I had but it just looks so much better the other one I had for years and there's just a bunch of problems happening with it So it's like you know what I need to buy myself a new camera So let me know about that but if you guys are new to the channel smash that for you can subscribe button Hit the notification bell and also you've been enjoying the content pop that like button You guys are about to see something that you've never seen before let's get So we just got to the lake here folks We're about to put it in right now or I'm putting by myself my dad's actually coming out with me But I don't know if you guys know but my dad has a little pontoon He's gonna take it out here if you guys are wondering why we got a guy that's coming out with us today That actually races drones.  So stay tuned I'm gonna do a little bit of fishing while I'm out here I actually texted the guy to see where he's at and I might stop by a little spot That's right here by the ramp and try to catch a big fish before we go and pick him up from the ramp But this is gonna be a good one I'm about to put it in the boat by myself I actually ended up getting that drone like I was talking about I might try my first-ever drone shot for a fishing video Right now by myself while I'm putting the boat in by myself. So that's gonna be interesting Let's go ahead and give it a shot. So boys. Here is my drone right here beautiful little drone My first time ever flying it is yesterday. So but I have to feel confident with it I don't I feel like I feel like I can do it. I feel like I got this. I don't have Any doubt in my mind, I'm not gonna be able to get this So we're gonna go ahead and turn this thing on I'm gonna set it at a good angle probably like right up there and then the boat ramps over there And you're you guys are gonna get to watch me like back down and that whole thing So first we're gonna turn the drone on one click hold All right, there we go Drone is on I'm gonna set you guys right here. All right now. Let's turn the remote on. Oh, man I'm so excited guys. If you guys want to see more drone footage smash that like button All right, here we go. Here goes nothing. You got in tripod mode. I'm not going sport mode. There we go She's about to take off There she is here Actually forgot one crucial step. I meant to put my phone in this so I can record this Newbie mistake my first time give me a break So this is pretty neat so you can actually watch everything from the controller so your phone fits right inside of this DJI go 4.0 Drap this phone in let me show you guys this I don't know how well this is going to do but All right, as you guys can tell that is right where the drone is pointing It is time to fly right here. We're gonna hit record now. You guys should be watching from the drone There you go. You can see everything on the screen All right, it's time drones up there. You guys should be able to see it now. Let's back the boat in there You go so you guys can probably see that a lot better. That is exactly where the drone is pointing That's the angle that we're looking for drone is right up there now. Let's back her in drone shot I'm kicking the best TV pretty much. Let's get it. All right now. Let's go Maybe try to catch a fish. I told him to let me know he's 15 minutes out So now I can make it over to the ramp. So he hasn't texted me yet Looks like we might have a few minutes to go try to catch us to launch. All right people. Well long story short about the Drone thing So yes, I did get a drone you guys saw the footage but as I said there was gonna be An individual meeting us out here and some things just happened and we sadly didn't get to do it today Hopefully we'll get to do it another day But uh, yeah, so pretty much fish for about five minutes went and picked him up some things happened and then Now we're fishing again. So I don't have too long to fish, but I'm hoping that like Maybe we could put something together.  Oh My gosh That's a good one He's running at me. That's a biggin. I Can't move him. That's a biggin guys Like serious, I didn't think he was a fish when I first looks at Felt so big. Oh Jesus some mega. Oh, yeah. Oh, yeah some mega some mega First fish today Broken off earlier. Not too long ago. I'm in fishing that long freaking giant. Holy cow. That's a thank you You're going in the live You're going in the live look at that girl.  That's how big that thing fell On the divine shaky head worm by six cents if you guys are like 10% off I'll drop the coat on the screen It no awesome.   I just got hammered. I Got him. I got him. All right, hang up Look at that boys and 50 foot of water. He ain't big. At least I don't think oh gosh What hold up.   What am I saying? They ain't big. He was big that spot right there I think he's bigger. He ain't as fat as that other one But he that that one's a large spot that I just caught I got a retie after that one spot lockers I literally had to get the net on that. I was gonna boat flip it But that was I haven't landed a fish today yet And I don't have that much time to fish And I was like I was like if I try to boat flip this thing and I think breaks off I will cry and I'm gonna be honest with you with how the hook is kind of frayed. He would have broke off And I would have been crying bro. I would have been crying, bro. Oh Thank the Lord These are the worms that I was using that's what I just caught on It's the divine shaky worm green pumpkin blue, which is probably one of my favorite colors of all time I like my three favorite colors are I would have to say four is like green pumpkin blue green pumpkin green Like a morning dawn color, which those are all be good for clear water and then for like dirty water I love like a June bug or like maybe like a black and blue, but I don't throw that on a shaky I'd like a June bug. I want to shake it would be good, but uh, yeah green pumpkin blue So in the hook that I'm using these are actually the spot removers. I wouldn't recommend using these I'm gonna be I've used these for years, but they've been like They've been like crazy. That fish was fighting so hard to just like been out my hook I wasn't even from a rock or something. I'm gonna be real with you.  Oh my gosh on a crankbait big crankbait first cast Biggin. Oh my god.  Oh my god On the big crankbait, bro. First of all, I don't know why I'm seeing bro so much. Look at that on the C20 first cast Literally next spot from that last.  I'm gonna pick up this cloud nine right here Big crankbait boys.  There's these fish are really smart, but still It could help, you know, if there's more down there, I mean that was first cast you know, that was just Very surprising. He drilled that crankbait to and this color is so perfect for linear right there Look at that beautiful color So guys, we're about to experiment with my drone a little bit more So just a little brief. I know you guys probably don't care about this, but this is kind of like interesting You know, it's just something different to kind of add into the channel so you can got you guys can kind of see You know some of the behind-the-scenes stuff. So the drone is very compact. You pretty much just fold out The legs or the arms whatever you want to call them. Oh shoot. I don't have a card Screwballed myself. Well, I think I'm gonna have to take the card out of this camera, but anyways So you unfold it like that to turn it on I press it once and then hold it it's on and I turn on the Controller I press it once and then hold it it's on and then I fly it up I was going to get the drone up in the air and then use my GoPro and talk to you guys about what I'm doing But what I'm gonna do is get a drone shot first since I only have one SD card and then we're gonna talk about what I'm doing currently So we're throwing this 20 this is the cloud nine by six cents I can't remember the exact name of this color But if you guys are wondering it has some green on top and if you look at it if you kind of turn it It's got a green and a purpleish glow on the side is white with green and then on the bottom It has just a little bit of pinkish orange and this is a beautiful color. This crankbait dies about 20 foot and I believe mine's going a little bit shallower than that. I don't have exactly the right Pound line that I should be throwing on this crankbait usually when I throw crankbaits like this I want to get the maximum depth out of them. So I'm usually throwing, you know Let's say around 12 pound 10 to 12 pound on a crankbait like this But if you guys are wondering what we're fishing right now some rock right here, and it's got a gradual drop-off So we're sitting in about 30 to 35 foot of water up on this rock It's very shallow and what I'm doing with this crankbait is I'm taking it and I'm beaming it right up On those rocks right there. Okay, and then what that drop-offs doing is alright is shallow You see this right here, and it's gonna gradually go like this So what this crankbait is doing is digging in the bottom kicking up all that nasty stuff on the bottom Creating a ruckus, and if it goes past a fish the fish is gonna engulf it for some reason something about a crankbait That's a big rock big rock Falls along false alarm. Oh my gosh Anyways now we're on top of the place, but that doesn't really matter As you guys can tell look at this. This is a perfect example Look at the baitfish right here, and that's why there's fish here right there some baitfish So what I'm really looking for right now since I caught that fish is there's rock to clay transitions The smaller rocky points that have those drop-offs something that I can dig this crankbait down in and Where the fish are gonna be stacked up on so you know there's rock to clay transitions Especially this time of year. I've had a lot of good luck on them You know I haven't ever been able to put a ridiculous pattern on the rock to clay transitions But I will say I have put together some big fish from it. Okay not as many you don't get the numbers I don't really get the best pattern from it But usually when I do catch them they're big fish and something to think about is I'm throwing a crankbait this A lot look how big this thing is in my hand guys I'm throwing a crankbait this large for spotty bass. So we're going for the big ones I mean no down mine and you guys could tell there's some bait on top of that in the shallows And this is exactly what I was talking about So you got that flat that we're throwing up on this flat right here is going to gradually fall off This is it right here. So we're going to throw that crankbait And dig it in down. All right. We're in 16 foot of water right here So that crankbait can go at all this right here It's going to hit bottom at all this and that's very important with the crankbait So you're going to be digging up All that stuff in the bottom and that is the Listen the most important thing if you're going to take anything out of this video or anything out of this little segment With the crankbait you got to be touching bottom. Okay You don't have to be touching bottom, but 99.9 percent of the time you need to be digging the bottom with the crankbait All right, and there's something about a crankbait that just makes the bass want to eat it I don't care how cold it is. I don't care if the water temps like 35 40 degrees For some reason they still eat a crankbait. I have no idea why you know a majority of the time You're throwing some slow-moving baits but something about a crankbait a bass just likes to react on And you'll catch a ton of fish on it and look at this.  Okay. I'm going to leave that 0.1 To the folks that just randomly pull up to these spots and catch fish I have never really caught fish on like a straight like sandy area right there. Like I just haven't I've seen one smaller pattern on one of the lakes that I've been on where I've caught them on stuff like that But rather than that, I just have not caught any fish on those like flat sandy areas I'd really look for that rock and like stuff like that to dig into These spots right here would actually be perfect for that little shaky head that was thrown earlier too But ever since I got that reaction bite and that was first cast on similar spots to this It's just kind of in my head, you know, I'm like I want to catch another crankbait fish And uh, I don't know who knows we might catch one. We might not but we're gonna keep casting I might cast a little bit here. I've never fished this. It doesn't even look that good. But you know what? I'm gonna give it a shot