Good afternoon everyone and welcome to today's session how one country sought to combat the harm of burning sugarcane. My name is Rocio Ortega and I'm the events associate at Republica and I'll be your host today. Today we'll be testing out a couple new exciting features for accessibility. Closed captioning of the program is available and can be enabled by clicking on the closed caption option on the bar towards the bottom of your screen. This program is also being broadcasted in English and Portuguese courtesy of Hany Sosa. To select your preferred language, please click on the bottom that says interpretation below. It is important that you select that appropriate language that you do not miss any of the conversation. You should either select English or Portuguese and be sure the option is not set to off. We'll get started in just a few moments. We're just waiting for a few more people to sign on. Thank you so much for your patience. Today, ProPublica video journalist Nadia Sassman and Seattle Times reporter Lou Ramadhan will moderate a conversation with experts from Brazil and Florida about alternative approaches to harvesting and what those might mean for the blades for more than half of America's cane sugar is produced. And it looks like we have enough folks on now so let's go ahead and get started. Again, if you're just joining, my name is Rocio Ortega and I'm ProPublica's Events Associate. Welcome to today's session how one country sought to combat the harm of burning sugarcane. Thanks to McKinsey and Company for their support of today's events. This event is being held in partnership with the Palm Beach Post and WLRN. Today we'll be testing out a couple new exciting features for accessibility. I'll share some of the notes that I shared at the start of the session. Closed captioning of the program is available and can be enabled by clicking on the closed caption option on the bar towards the bottom of your screen. This program is also being broadcasted in English and Portuguese courtesy of Hani Soza. To select your preferred language, please click on the button that says interpretation below. It is important that you select that appropriate language that you don't miss any of the conversation. You should either select English or Portuguese and be sure the option is not set to off. For those new to us, ProPublica is a nonprofit newsroom dedicated to investigative journalism. Last year, reporters at the Palm Beach Post and ProPublica investigated the impact of sugarcane boiling in Florida. The harvesting practice helps reduce more than half of America's cane sugar, but it sends smoke and ash into largely low income communities of color in the state's heartland. In their reporting, the journalists learned that other countries have found ways to harvest their crops without those burns. They traveled to Brazil, the world's largest producer of sugarcane to learn how and why they switched to another method. I'd now like to invite our panelists to join us on screen. Thank you so much for being here today. Dr. Christopher Holmes is an associate professor of meteorology at Florida State University. His research examines the global cycles of air pollutants and greenhouse gases and the interactions of both of climate change. Antonio Keros is a technical advisor in the presidency of State of South Pablo Environmental Agency. His work includes analyzing environmental licenses and permits as well as developing policies and procedures on pollution prevention and control permits. Dr. Elena Kibero is a professor of environmental health at University of South Pablo. Her work focuses on the subjects of environmental health, health geography, and air pollution. Dr. Rafaela Rosetto is a scientific researcher at Agronomic Institute of Campinas. She did a post doctorate at the University of Florida in Belgrade, where she worked with crop options for bioenergy. Her work focuses on soil fertility and fertilization as well as use of waste in the cultivation of sugarcane. Our moderators today are for public video journalists, Nadia Sussman, and Seattle Times reporter, Lou Ramadan. As an additional note, the session is being recorded and a LinkedIn video will be emailed to everyone who registered. Thank you all so much again for being here today and I hope you enjoy the conversation. I'll let Nadia and Lou take it from here. Thank you so much, Rocío. We are so pleased to welcome this panel of experts, including two that you saw in the video. And we wanted to start off by talking a bit about what happened in Brazil. Health concerns were one of the factors that led lawmakers to phase out the practice of burning before harvesting. So this first question is for Dr. Elena Kibero. I think your camera is still turned off. Dr. Elena, I cannot start my video because you closed at me. Well, let's see if we can work this out. Oh, there you are. Thank you so much for joining us, Dr. Elena. Sorry about that. So Dr. Elena Kibero, you studied the health effects of sugarcane burning in several cities in São Paulo State. What were your major findings, especially with regard to disease, hospitalizations and mortality? Well, I had started studying air pollution effects on health in the city of São Paulo in the late 80s. But then around 2000, the year 2000, I started to study the effects of biomass fire, especially because of the great expansion of sugarcane plantation in the state of São Paulo due to the development of the flex fuel engine for cars. At that time, mainly small cities had this problem of receiving the effects of the burning of sugarcane. And so we selected some municipalities in the state of São Paulo. The first one was in Espírito Santo do Durvo in the west part of the state. And we studied mainly direct effects because sugarcane burning can have also indirect health effects as climate change caused by greenhouse gases and car accidents due to the reduce of visibility. But our studies focus on direct effects and mainly respiratory diseases. I also advise that around eight dissertations and thesis on the team, on this team of, and I have two large projects financed by the Ministry of Health of Brazil and Ministry of Science Education. The first one was undertaken in 2004 and 2005 in a municipality where 100% of sugarcane plantations were still burned in spite of the law in 2002 because the plantations were not adapted to receive the machinery. At that time, we installed air pollution monitoring equipment at the school, at the city school, and we studied air pollution during harvest time and no harvest time. And we also studied respiratory symptoms in children from 10 to 13 years old. And our findings indicated that in spite of the level of pollutants, they were not over what Brazilian legislation allowed at that time. Now Brazilian legislation is more strict, but at that time it was permitted. The prevalence of respiratory symptoms in children was very high. Among 28 symptoms that we studied, 22 of them were higher than we had found in the metropolitan regions of Sao Paulo. And those symptoms were mainly cough, most days cough without cold, whizzing, bronchitis, asthma, ear infections, and allergies. Those neighborhoods were usually low income neighborhoods where population lived in simple houses without much protection for air pollution. And also sugarcane harvest occurs in winter, where you have the problem of more virus because of the cold that starts and also temperature inversions, which difficult, turns it difficult to dispersion of air pollution. After that first study, we undertook other studies in Araraquara, which is the north part of the state. And we studied children until four years old from zero to four years old and old people 65 years old and older. And we studied using job processing techniques, geographic information systems with data on sugarcane area, number of fires by satellite images or by CETESB data which registered the fires, hospital admissions by age and by classification of disease, and also the social data on the population affected. And we found that in most of the studies, that there was an association with sugarcane fires and respiratory symptoms and hospital admissions higher levels of hospital admissions during sugarcane harvest, and especially during or a few days after the fires were allowed by CETESB, which allowed agricultures to put the fire on sugarcane before harvesting. The other finding was that the population was affected was mainly low income population, which had more difficulties in getting to basic health systems so they stayed more days before getting to the hospital and they would get a worse health situation. And the problem was also worse for children and for old people that were those were the main findings. But since those projects, they lasted for a while, we could see that after 2008, which was five years after the law when part of the sugarcane plantations could not be burnt anymore. And from then on, there was a reduction in the number of fires in the state of Sao Paulo. And there was also a reduction in the admissions of respiratory due to respiratory diseases in the population. We could see that year by year, this reduction. So it was important to see how it happened. I told about Araraquara, but we studied in President Prudente region, and we studied in the northwest region of the state where 25 municipalities had large areas in sugarcane. And in all those areas, we noticed this reduction in fires and reduction in respiratory disease admission in hospitals. We're using data from SUSE, the Brazilian Unified Health System. We also had in Araraquara, we had studied the airborne polycyclic aromatic hydrocarbons to see the cancer risk of cancer due to the pollutants of the sugarcane fires. And we identified that there was a tolerable risk of cancer in the areas where sugarcane was burned. But this risk also decreased along the time. The data with the when we measure pollutants in 2002 was higher than in 2010. So in the meantime, the law brought benefits for the population. Thank you so much. That was very helpful. Similar to what you've just described, Dr. Elena, residents in Florida's Gleads region have raised concerns about negative health impacts from cane burning. That was the driving factor behind the lawsuit that was referenced in the documentary. And now that lawsuit was recently dropped at the agreement of all parties. And the major sugar companies in the region say that the air is good and meets federal clean air standards. But as we've reported, the EPA is currently considering whether those clean air standards are adequate to protect public health. Now, Dr. Christopher Holmes, thank you for being here. Your research examines the global cycles of air pollutants and greenhouse gases. What kinds of pollution does sugarcane burning produce in Florida? And what are some of the challenges in tracking it? My research group has spent some time looking at data collected from surface monitoring sites in South Florida and also from satellites which orbit the earth and observe South Florida as well as the rest of the world. And the first thing we looked at was a respirable particulate matter, which is also called PM 2.5. And this particulate matter can reach deep into the lungs and cause a variety of health problems. There are dozens of monitoring stations in Florida because it's well known that PM 2.5 causes disease. And we looked particularly at several sites that were close to the sugarcane fires. And at two sites near the sugarcane fires, we found that the concentrations of this PM 2.5 were higher during the harvest burn season than they were in the rest of the year. And this was unlike any other sites in South Florida, showing that that the concentrations were higher closer to the fires. This corroborated what other studies have found in the past. Some researchers at Florida International University measured concentrations of PAHs which Dr. Ribeiro just discussed. And those were characteristic of emissions from burning vegetation. And those were many times higher in the sugarcane burning region than elsewhere in South Florida. It's now established from many other studies that these small particles and PAHs cause quite a few different health impacts. And Dr. Ribeiro listed several of them. But I would emphasize that they are associated with cases of asthma, bronchitis, emphysema, other respiratory infections, heart disease, heart attacks, lung cancer. Obviously this list goes on. But also things like premature birth of and low birth weight of babies. Several of these can be fatal. And using these past studies in other regions that relate the concentrations of particulate matter to mortality and health outcomes, we estimate that there's probably about one to six deaths per year as a result of the sugarcane burning emissions in South Florida. For the scale of these emissions, I think it's interesting to compare them to something like motor vehicle emissions. From the area of sugarcane that's burned each year, we think there's about 5,000 tons of particulate matter produced in these fires. And that's pretty close to the emissions from all of the motor vehicles across the state of Florida. But the sugarcane fires, of course, unlike the vehicles are tightly concentrated in just one small part of the state surrounding a few different communities. Thank you so much. That was really illuminating. Environmental concerns also played an important role in the decision in San Paolo State to transition away from burning and to harvesting raw cane. Nearly all the cane in the state in San Paolo is now harvested without burning. So Dr. Rafael José, what were the agricultural challenges and the benefits of switching to raw cane in Brazil? Can you tell us what that period of learning and adaptation was like? The challenges were high. The first challenge was that we had no machinery to harvest sugarcane. Especially the moment the government decided that we had to change things. We have no machinery. For this reason, we have decided to create a timeline and use a 10-year period to help us adapt to the law. And while we were purchasing the machinery, we have learned how to do the work in the new system. First, we just thought that we would no longer hire people and just use the machines. So we were faced with new solutions and lots of learning. So the first challenge was machinery. And the second was hiring people because we knew that we would no longer hire so many people because the machines were there. Luckily, the sugarcane sector was expanding and we have hired people to do work in other areas with sugarcane expansion. Later on, the next challenge was that sugarcane productivity lowered because raw cane has lower productivity than burnt cane. Later on, we understood that the machine would stamp on the cane or even the machine would cut some part of the plant. So we have improved quality of the machinery in the process. Also, we have resorted to GPS and we no longer have the machinery go on top of the sugarcane line. Later on, we had other other challenges linked to pasts. The only advantage of burning is that burning kills off pasts. And as time goes by, the pasts are faced with their natural killers. Basically, we need to be patient. We need to learn and try to equilibrate the system. Also, we have developed some pesticides that we used in the beginning. And nowadays, we are phasing out the use of pesticides. Currently, we only use pesticides where we have problems with certain pasts. Also, we have changed some plants that were harmful to sugarcane. We have developed new techniques to fight those plants. Also, we have changed fertilization because the straw is really nurturing for the soil. We need to be careful when adapting and using new types of fertilizers. We've learned so much over the first 10 years. And I dare say that now the system is still a work in progress. We're still improving our techniques. We're still using new machines. I've told you about the challenges. But now I'll tell you about the benefits. The benefits are we have more protection for the soil. We have better soil humidity now. We have better soil conservation and better physical qualities for the soil as well as biological life for the soil. A large number of microorganisms have developed because of the straw. And all of this has brought more organic material and more life to the soil. And I can tell you that the biggest advantage is to raise environmental awareness in the sugar industry. I think that in Brazil, there's nobody who thinks that sugar cane burning is the best technique. Nobody agrees with that anymore. Our grown, grownomic engineers, they have shown us how good it is to use the new techniques. And whenever we're faced with challenges, they, they help us see that the new method is much better. Thank you. Thank you for listening to me. Thank you. That was really informative. So Brazil's sugar companies seem to have embraced that transition to not burning, as you mentioned. Throughout the course of our reporting, Florida sugar companies have denied that pre harvest burns have a significant impact on air quality or public health. Now prior to this event, ProPublica asked Florida crystals and US sugar, the largest sugar producers in the region, for their perspectives on Brazil and alternative harvesting methods. Florida crystals did not respond. And US sugar provided a statement, but it did not address our questions. The company said US sugar proudly stands behind the safety and integrity of its farming and processing practices. In addition, the men and women of US sugar are also employee owners of our company, with jobs that meet the world's highest standards for worker safety, environmental stewardship, sustainability, and technological innovation. Antonio Keros, this question is for you. You work with the state environmental agency in Sao Paulo that regulates agricultural burning. In Brazil, what was the reaction of the industry when regulators first announced that they were going to end sugarcane burning? And how did that change over time? The question is that sugarcane industry was having a lot of claims of the population nearby due to all the health problems in the cities, all the problems due to the burning of the sugarcane leaves. So it was not a new question. CETESBI is the state company in charge of control pollution sources. Of course, a big burning is a pollution source. So when CETESBI has a question of the justice of the population about a large burning, causing very big problems, so the company needs to take action and please do not burn now because we have a very low humidity in there. We have a very low content of humidity below 20%. You cannot burn now. Justice also start actions against these farmers just asking you cannot burn it. You are causing a big health problem. So it was not a new question to these companies. When in 2002, we got a state law just trying to ban the fires. This law was, as Hapaiela said, really well done because it gives to the companies time to make these reductions. We got a period of time of 20 years to burn the fire in the places where harvest could be made by Mike McNally. And 30 years where the harvest should be manual due to the inflammation of the land. So companies start to ban the fire. In the beginning, it was a big mess because we were working on paper. Just try to imagine large companies bringing maps and paper saying here is the 20% of my area that I'm not going to burn this year. It was almost impossible to monitorize, to give, to communicate. And the company developed a way to make everything by the internet in a system that we got all the maps of the farms. And the farmer just put this is the area, the 20% of the area that I'm not going to burn in these five first years of the law. So what happened was that when the companies start having these machinery, they start improving this mechanical harvesting, and just talk with the environmental agency and talk, hey, we are expected to end this burning in 2020. Let's see, what if I just make it earlier? So the companies just talk with the agency and we got an environmental protocol to anticipate the end of burning. Because when they start to buy this, this machinery, when they start just you need, you need to make big changes in the way you plan. If you are going to use McKinney Harvesting, you cannot have leather crops. You need to make everything flat. So the machinery machinery can pass through the can just making the harvest the better way. So why do not accelerate this process? And also, this company's just and the problem that they had with the municipalities nearby, and the population, always claiming about this fires in the burning, especially in the dry season in Brazil, that is our winter. So the sugarcane sector did not try not to ban the fires. They just had to have their time to make this adaptation. And it was really a success. Meanwhile, in one of the regiments of the state in Zhaou, there was action as this election, and it went to the Supreme Court of Brazil that said that it was forbidden to burn in all that region, right? But a lot before the the date that the law stipulated. So it was very important for the sector to say, hey, wait a little, I am doing my job. I am burning gradually and I'm making it in a in a way that I can maintain my economic activity. And we will have the same result in 2017 was the last year that we got big fires and bigger farms. Today we have only very, very, very little burning in the properties where it's impossible to make the mechanical harvest. So it was a process. It was not very hard. It took a lot of work. But it is done. Yeah, well, yes, you mentioned a lot of work. And in the United States, similarly, we often have conversations about that balance between regulation and the economy. And in Florida, many people are concerned about the economic impact of making that harvesting transition. How did you as a state regulator evaluate that dynamic in Brazil, balancing health and safety with jobs? Let's see it in two ways in two different ways. Well, you have an industry and then you buy a very, very new machinery, and you need to dismiss 200 employees. Oh, I'm so sad, but I importantly, you lose your job, right? What's the problem? No. Okay. Why? Because I just need to have my profit. Okay, it's a way to see it.  And you say, whoa, but I will need to to to spend a lot of money to do it. Yes. But it's for the health of everybody. So I'd like to understand why one can just dismiss people in the name of a better profit. And one cannot have a less profit in behalf of all the population. And it's not a new problem. You see, here in Brazil, we have a licensee process that has a previous license, installation license and operation license. So the first one says, well, what you want to do is possible in this place. The second one says, well, you can build your factory, you can build your plan. And the third one, well, start operating. But you need to renew your operation license in three, five years or two years, it depends upon how dangerous is your your activity for the environment. Well, here in Brazil, there's no such a thing like a quiet right to pollute.  In this last three years, we have another technology much better.  So the renew of the operation license can ask for the industry to buy different operations, to buy new equipments, and to attend to a street standards. Because in Brazilian constitution, it's granted to all Brazilians their right to a healthy environmental. So I need to grant it. That's my, that's my duty as a government agency. And health is one of the most important things that we must look when we are lucency and activity. So of course, in some activities, you will have some sound pollution, some water pollution, some air pollution. But you cannot compare any pollution from an industrial activity to a burning of hundreds and hundreds of thousands of acres of sugarcane in the free space. So for us, it's really a question of a health environmental. Thank you so much, Antonio. In Florida, the agency, so somewhat analogous to the agency that you work for, but in Florida, the agency that regulates sugar harvesting practices is the State Department of Agriculture and Consumer Services. We invited Florida's Agricultural Commissioner Nikki Freed and her staff to participate in the panel today. They declined, saying they were short staffed in the middle of a legislative session. In the past, Commissioner Freed has says she believes it's possible for quote unquote, green harvesting, or the harvesting of raw unburnt sugarcane to be feasible in Florida. But they still need to examine quote, pest and plant diseases, worker safety, Florida's ecosystem and soil composition, and industrial demand for green harvesting. Dr. Jaffaela Rosetto, you spent a year working in Florida's Glades region. And given these concerns, pests, worker safety, the soil, etc. Do you think a transition to harvesting raw unburnt cane would be possible in Florida? I truly believe, yes. I know agronomists in Florida. I think they are as good or even better than Brazilian professionals. And if Brazilians have managed to overcome the technical problems, I'm sure American agronomic engineers are also going to make it. I think that you have to start, you don't need to start with 100% of the areas, you could start with 20% of the areas, you could, you know, learn from the process maybe in five, six years, 10 years, then you overcome the burning, and not having problems with pests or infestation, you need to take action now. You need to take action and do work little by little and you're going to overcome the challenges as they come. And you can learn from us here in Brazil. We know that issues are regionals, but you know, we offer a case study for Florida in any way. I think that it's important to take the first step. And then you tackle the problems. The problems are not that hard, because the hardest problems are offering people work. And we know that in the US, you have machine harvesting already. So what's the problem of stop burning? Maybe the machinery is going to be slower to do the harvesting. You know, it's going to take some hours more to harvest raw cane than it would with burnt cane. There will be some pests, but it's something temporary that you can overcome in some years. Natural, natural enemies are gonna come up. But I think that you have to get started. Florida can lead the way and show people clean techniques and eco-friendly practices. Here in Brazil, one thing that was really good is that the plants are proud of saying they have modern ecologic practices that they do socially responsible work, environmentally right work and they use this to showcase their success. It's so good that the largest industries, the largest plants in Brazil, they have embraced it and they like to show they protect nature. Thank you so much for that perspective, Dr. Hafaela. That was really insightful. We are coming up on the 20th anniversary of Sao Paulo's original law to phase out the burns. Meanwhile, in the US, we've never had any laws banning sugar cane burning. Lawmakers have taken an interest in air quality. However, last month, three US representatives introduced a bill that would create a pilot program for air monitoring and low income communities and communities of color. Among the sponsors of the bill is Florida Representative Kathy Caster. In Florida's sugar growing region, there is only one government run air monitor. Dr. Holmes, as an expert in air monitoring and modeling, you've said that the government should take a closer look at the air in Florida's sugar growing region. What should additional government monitoring look like? Given the short duration of those burns, and what might that monitoring tell us? Right. So in urban areas across the US, we have typically an urban area will have multiple monitors, and that allows you to map out the concentrations of particulate matter and other pollutants in various neighborhoods. The area of Florida where sugar cane is burned has multiple different communities. And the one official monitor there is not in the center of any of those communities. And so I think if you want to know what the concentrations of these pollutants are, obviously you want to have a measurement in the center of each of the communities or somewhere within the general population area. There is a rising availability of low cost sensors, which I know the ProPublica worked on deploying several of those in the area. And those are a tool that can be used to supplement the regulatory monitors. They aren't as accurate, but they are where they can be reliable. So they have to be checked carefully, but they can be used for mapping things out. And that's being used routinely in a variety of other states and cities to map out air pollution on fine spatial scales. We also use satellites because satellites are peering down from space all the time. And what we've seen from space reinforces what we saw from just the couple of monitors that are in the area where sugar cane is burned. The thing that I mentioned earlier was that the concentrations of particulate matter are higher at the two sites that are near the sugar cane fires. And the satellites further showed that the area of that elevated particulate matter coincides very closely with the boundaries of the sugar cane fields. So the satellites are providing spatial information that we can't get from current sensors on the ground. But it really does, again, support the conclusion that there is a source of particulate matter in the sugar cane burning region. Thank you so much for that. I'm not in the first beginning to take us through that discussion. We're now going to turn it over to our audience Q&A. But before doing that, I'd like to just share a quick link to our event survey in the chat box that I'm dropping in now. We really appreciate your feedback. Again, if you'd like to ask a question, click that Q&A icon at the bottom of your screen to submit it to us. We already have a few in so I'm going to go ahead and pass it back to Nina to go over that first question. Thank you so much to all of our panelists. Thank you, Maceo. And thanks to everybody who asked questions. So I'm going to start with one. It's a bit of a double question. Is the reluctance of the Florida sugar industry to invest in machinery, thus costing them money a part of the problem? And did the price of Brazil's sugar increase after the burn stopped? And so for the first question about the machinery, I just want to say that Florida's sugar is already harvested by machine. So in Brazil, you saw the manual cutters in the video. That practice has been replaced by machine cutting in Florida already. Antonio, Keras, maybe you can answer the question about the price of Brazil's sugar and if it went up after the burn stopped? I don't know if you have a rise in the price with the stop of the burnies. But the truth is that the industry, sugar cane industry in Brazil, look forward a green protocol just to be able to sell to markets where these worries with environmental subjects were really important just to close, man, just to open markets and not to close down. So when we talk about the green protocol that industry just assigned with the environmental departments of some policy state, this brought a very, very good marketing for these companies. So these companies could say I am a green company, I have my sugar without burning, I have my sugar in a very, very strict law observance. So that was the improvement in the to the company profile. I don't know exactly about the comportment of the prices. But I heard it of many of this big contours that they say no, I want to have a green seal on my sugar because Europe wants it, because all the markets want it. I want to be a green product. And it's a good way. Thank you. And thank you to all of the attendees for the excellent questions. We got one here for you, Dr. Holmes. The attendee asks, anytime that health outcomes and the relationship between burning and negative health health outcomes are mentioned, they're told that the data does not support a negative health outcome. You mentioned this when you were speaking earlier. Can you speak a little bit more about that link between mortality and morbidity and burning cycles? We know from lots of studies in urban areas across, especially in the US, but also in Europe and many other countries that inhaling particulate matter is harmful to your health. And Dr. Rivera has done some of those studies and she spoke directly about that. You can think of this as analogous to smoking cigarettes, which are also sources of particles. And tobacco is, of course, a plant. And so there's some, not a tight analogy, but there's some analogy to burning, to inhaling smoke from a cigarette, which is well known to be toxic, and inhaling smoke from other burning vegetation, whether that be a wildfire or an agricultural fire.  We have now established, or the scientific community has now established that there are harmful effects of burning, excuse me, of inhaling particulate matter down to very low concentrations, at least concentrations of five micrograms per cubic meter. And I bring up that specific number because the way that air quality is generally regulated in the US is based on a threshold system called the National Ambient Air Quality Standards. And that's set by the US EPA. And they pick a threshold above which there are consequences and below which there are not. And there's a lot of factors that go into setting those standards. But the current standard is that the air quality should have concentrations of 12 micrograms per cubic meter, or less of PM 2.5. So the scientific literature shows that there are harmful effects of inhaling particulate matter at concentrations that are below what the ambient air quality standards allow. So when I have said that, you know, we expect that there are impacts of inhaling smoke from sugarcane and from other burning sources, even though the concentrations in the sugarcane burning region are indeed below 12 micrograms per cubic meter, we still do expect that there are going to be health impacts from those. Thank you. And just to throw that question to Dr. Hebero as well, you mentioned that that threshold for what's healthy and what's allowed under the law in Brazil also changed over the course of the discussions. So what does that look like? What did that transition look like when research and new information emerged in Brazil? Well, Brazil now is in the process of adopting World Health Organization thresholds, which is slower, a little lower than the American EPA standards. And but we have a time like we did for the sugarcane harvesting and using fire. We have a period of adaptation for the cities especially and for car engines to adapt to the new standards. The state of Sao Paulo has a progressive process with intermediate phase and phase, intermediate phase one, two and three until we get to the World Health Organization thresholds for controlling air pollution. But as I said, it's not only the pollution at the ground level. It's also the greenhouse gases that the burning produces. And this is not being discussed. The forest fires in the Amazon are so much discussed around the world and why a volunteer fire like that is not discussed because it produces methane, CO2, CO and ozone, which are greenhouse gases. And this may affect climate and increased temperatures around the world. So I guess we need to have a more encompassing discussion regarding the indirect effects also. Dr. Elena Jivero, thank you so much for that. So, you know, in the short time we have, we probably won't get to the fully encompassing discussion that you're talking about, but it's definitely something we should continue to discuss in the future. We have a question that I think would be good for Dr. Rafael Rosetto. Have the transformation processes away from burning been published in English and is it available? So what resources are out there based on the experience of Brazilian agronomists, engineers and others who work in the sugarcane industry about transitioning from using prescribed burns to having a no burning harvest? Yes, most of the papers are in Portuguese. Yes, most of scholar work is written in Portuguese, but we have plenty of material in English too, especially as regards to straw, you know, nutrient cycles based on straw, the changes in terms of fertilization, all of those agronomic processes are documented in technical journals. You know, if you Google information, lots of scholar work is going to show up for you. We have plenty of scientific material on that. Thank you. There are a couple of questions here about employment and some of the repercussions of making that transition in Brazil. Perhaps this question is best for you and Tonya Keras. What happened? What was the feedback like from those who potentially lost jobs because of that transition? And what did the employment trends look like overall with the sugar industry when they made that transition away from burning? As I said, it was a slow transition. So we have something like 15 years before the total ban of big fires in São Paulo state. And this job of cutting raw sugar cane, it was not a good job. Right. People came from northeast Brazil, the poorest region in Brazil, came to São Paulo state and to the southwest just to cut sugar cane and then can go back to northeast Brazil. This job requires people to stay in a very reclined position and managing this sharp tool to cut the sugar cane and was expected from one person to cut something like eight or 10 tons of canned per day. Not a good job, not a healthy job. So this transition was not of a good job to a no job. It was from a not so good job, a job that only provides you with some money. As Rafaela said, some of these people were employed in sugar cane industry from this 2002 to 2007. We got a big growth of sugar cane industry in São Paulo state. So we got sugar cane going over the west of the state. So some of these people went to these activities and the others were just absorbed by the market. Right. It was the question of do it in the time to give time for people to find jobs. But it's completely different from this question you have in Florida. You do not have people who are going to lose no jobs if you stop burning sugar cane. Then you will have only the good part. You will have the other monocle benefits that Rafaela said. And we will not have these health risks that the Terrell Lena said. So I see no problem in this and this question of jobs for Florida. Right. I would, if you permit me, I look at it in the chat and I saw a question about somebody asking if there is in our constitution the protection of the health of the people. Yes, with this. We have a very, very large constitution and it has 220 25 article. We have this protection of environment. If you allow me, I will read it to you. Everyone has the right to an ecological balanced environment. A good for common use by the people and essential to a healthy quality of life and posing on the public power and the community, the duty to defend and preserve it for the present and to two generations. So this is a constitutional command. Right. Environment is a good of common use and we need to protect and we need to grant a health environment. So article 225 of our constitution. Thank you so much, Antonio, get us. I saw that there was a question. Have there been health studies in Florida and how large is the exposed population? So I wanted to ask Dr. Christopher Holmes about that. And then I wanted to follow up afterwards for some of our Brazilian panelists to learn a little bit more about how their studies came about. So let's start with Dr. Holmes, please. So my research group has done some of that and we have articles that are in peer review at a journal. So I yes, the studies have been done, but they haven't been published yet. So I will be able to share more about that hopefully soon. In terms of the population that's exposed, the most heavily exposed population are the people that live in the in the cities of Belgrade and Cluiston and Pahokie. And I believe there are about 40,000 people that altogether live in those communities. The smoke, however, is not confined to those communities and it does drift both east and west. And so we do expect that even though the concentrations of smoke are low over the coastal cities and further to the west into Glades and Henry County, we do expect that there would be some health impacts in those regions. And I noticed reading through the questions that, you know, some people are interested in having more specific details. And I expect that within the next few weeks, I would be able to share some figures that that would address some of those questions that I'm noticing in the chat. Thank you so much, Dr. Holmes. And Dr. Helena Hiberro, you mentioned this a little bit, but you did a number of studies related to sugarcane burning and health effects specifically. And I think you had mentioned that some you had done in partnership with or funded by government agencies. So I just wanted to ask at the state level or the federal level, to what extent is this something that government is trying to pursue as a research matter in Brazil? Well, I had mainly funding from federal agencies, like the Ministry of Health has a health surveillance secretary, which it has a secretary, it's called Vigiar, the Air Surveillance. And in this program, it had it opened bids for studies regarding air quality. And I enter in one of those bids for financing part of the study. Other parts of the study were financed by CNPK, which is the research Brazilian Institute, which belongs to the Ministry of Technology of Brazil. In some of the students, the PhD or master's students, they got financing from the Ministry of Education, CAPIS, or from FAPESP, which is the state agency. But during this time, we had partnership with CETESBI, sometimes which allow or gave us the data, or with even the sugar industry, which allow us to put monitoring equipment in the sugar cane plantations. And we had all the freedom to publish the results. And one thing I wanted to mention is regarding the labor, we had one of our research, we interviewed workers from the sugar cane plantations, because there are many small towns in the state of São Paulo that have, like a rural population that work in sugar cane plantations. And we did study in one of these villages, and we interviewed them. And they were at the same time, we lived at the manual harvest and was finishing because that was a very stressful labor. But they were anxious about finding new jobs. And what we found was that the unions of sugar cane growers provided courses for them at night on weekends, they could learn to use computers, or they could learn agricultural practices. So they were prepared for other jobs when they were expecting to lose their jobs. And many of them got employed, as Antonia said, in sugar cane plantations in other parts of the state for planting sugar cane. Or they went to soybean plantations or to work in tree planting, you know, reforestation areas in the state of São Paulo. And those who came from the northeast of Brazil, I guess they stayed there because tourists was developed during those years also in the northeast. So it was easier to find jobs in the northeast area of the country. So we don't see in the cities of the state of São Paulo a process of impoverishment because of lack of jobs, of formation of favelas or things like that. On the contrary, we see that there is an increased dynamism in those cities, middle-sized cities that have sugar cane mills around them. Thank you. Thank you so much for that. So we have one last question. We'll try to keep it brief if you can. But several questions have been posed that mention the political donations and sort of the power of the sugar industry and their opposition to shifting away from burning. And perhaps, Antonio, you can speak to this. But was there a large-scale resistance to cane burning being phased out in Brazil? And how did you counteract any difficulties that you did that you were confronted with? And if you could keep that short, we're closing out here. But there was no such resistance, just no resistance at all. Because industry had time to do it. It was something that everybody knows it should have an end. Everybody was seeing the problem. You cannot have an idea of the area where sugar cane in São Paulo is made. We're talking about a large, a really large area. So when you were traveling in the countryside in June or May, it was possible for you to just drive in a cloud of smoke in the road. It was a big problem. Apart from this, everywhere you went in the countryside, you could see the ashes in the house, in the swimming pools, in the gardens, in the whole city. And everybody could think, well, these ashes that are in the ground, maybe part of them are in my lungs, too. It was so obvious that this was not a good thing to be done, that there was no opposition. The question was other, how do you make a good monitoring on it? How can I prove that? This sugar cane that burned was not burned by my fault. Someone just threw a cigarette from the car and it started burning. And you see this is a sugar cane planted in a place able to receive the harvest, the mechanical harvest. For me, it's not good that it burns now. I do not have equipment to wash these stouts before milling them. So these were the big problems. The planters said, well, you are charging me with a penalty because I burned in a place that I was not authorized to burn, but it was not me. I could say this was the biggest problem. So we started a question of this fact, my sugar cane burned, but it was not me. That was the biggest problem we have. Also, we got some problems as to how each one give us to the agency, the place as well. Here is my 20%, I will not burn. Here are my 30%, here are my 50% how we make it in the maps. It was a little hard to do, but it went, but never we had an opposition. I want to continue burning. We have no judicial claim. I want to burn because I have the right to burn. It was almost impossible for one to win a civil election with this, but we have no had no. So we got no opposition. It was a case of success. All this is, I believe some policy must be very proud. So the agency as the sugar cane sector, that we do the right thing. What a note to undone. That is our time for today. And I want to thank our panelists. We are so honored by your president, by your presence, such incredible insight, just knowledge that you've shared here today. And of course, our moderator is Nadia Sussman and Lulu Ramadan. Thank you so much for navigating us through this complex discussion. I'd like to give a special thank you to McKinsey and Company for its support of today's event. And of course, to our partners upon Beach Post and WLRN. Thank you to our audience for joining us and all of your thoughtful questions that you've submitted today. Again, this event has been recorded. So you'll receive an email with the full video of today's events. We'll also be posting the recording on the ProPublica YouTube channel. And from all of us at ProPublica, thank you so much for joining us this evening. Have a great rest of your night and we hope to see you at our next event. Thank you. Take care, everyone.


insights from Precise Asymptotics by Takahashi Takahashi from Tokyo. Please, the floor is yours and welcome. Okay, thank you for the introduction. I'm Takahashi Takahashi from the University of Tokyo. As I've already introduced, today I'd like to talk about a mean field of nurses for ensemble running. Okay, let me start. This is a train. First, I'll describe some background and motivations for this talk. And then I'll introduce a framework for analyzing the bargain by using the replic method of statistical running method physics. After that, I'll talk about sparse ligation and imbalanced data as an application. I should put one disclaimer here because I'm not a good ingredient, so I'm happy if you could speak slowly when you're asking questions. Okay, let me start with explaining the motivations and settings. This talk is basically about statistical running. We are given a data consisting of pairs of input X and output Y. Usually, these data points are independently generated from some unknown distribution. So the aim is to run a model to make a good prediction for new input X. In this talk, we consider the running of linear models. I mean that the output for input X is a linear combination of the weight and the bias. To run the parameters, I'd like to consider bargain, which is a classical method for ensemble running. In bargain, we consider minimizing this kind of randomized cost function. This is an empirical risk measured on the resampled data set, which is obtained by randomly sampling each data point from the original data set. So the resampled data set D stir looks like this. Here, this red color integer, C mu, represents the number of times the data point X mu and Y mu appears in the resampled data set. There are two popular ways to construct a resampled data set. One method is a bootstrap, which is a very traditional method of computational statistics. In this case, each data point is obtained by sampling with replacement from the data set. So each data set that point may appear several times like this. In this case, C mu follows basically independent person distribution with mu mu V. This parameter specifies the size of the third data set D stir. And the other one is resampled subsampling. In this case, each data point is sampled with result replacement. So the size of the resampled data set is strictly smaller than the original data set. In this case, obviously, C mu follows a simple perinoid distribution. In bargain, the final smitter is obtained by taking the others with respect to the resampling variable C. It is usually used to reduce the variance. So it is often combined with less bias, but with high variance models. For example, decision trees are combined with bargains. So 82 is the famous random force algorithm. What I would like to know is what happens when used with linear models, which is the most simplest variance of the neural networks. Especially, I'm wondering about the difference between just standard digital regularization with random regularization with debugging. Typically, you know, in learning with linear models, we can use other L2 regularization with such kind of regularization to reduce the variance. Are these two different? This is a question I'm basically recent to consider. Actually, there are several research results regarding this question. This paper made by Brezhne and his co-authors, they consider the ordinary squares with subsampled examples, subsampling of rows, and they also consider subsampling of futures, subsampling of columns. What they showed that is that the average of this minimizer, the average of the minimizer of this randomize cost function is equivalent to the least estimated, with some ultimately tuned regularization parameters. Similarly, that's what I found in other settings. For example, in Rust engine, Zori and Kuroho showed that they only consider the subsampling of cute examples, and they showed that the digitalization with subsampled examples is equivalent to the digitalization with other regularization parameters. Similarly, empirical results were found in other maximum like estimated. So, this is a short summary of the story so far. Summary is very popular and theoretically we're motivated. They can deduce the variance of the estimators. But, however, in digitalization, unregularized maximum like estimation, the summary seems to be very similar to L2 regularization, at least if we focus on the generalization performance. This is interesting because in the sense that we can use it, it can provide an alternative way to introduce digitalization. But, on the other hand, I think it's a bit disappointing because it implies that we may not need to consider unassembling in the context of running linear models. So, I wondered if we could find any interesting features or some fruitful gain in other more abit structured settings. For this, we consider the bugging in slightly more structured data settings. I mean the sparse regression. Here, the teacher has some sparse structure and the other one is running a crash-fire from label in branch data. In this case, the data has cluster structures and the number of samples in each cluster is different. Oh, okay, okay, thank you. Sorry. I also described the typical method for analyzing such ensemble running. This is a basic motivation and background, okay? Okay, then let's move on to the replica method for analyzing the bugging. This is an optimization problem used in ensemble running of linear models. Here, the estimated W hat for one randomized cost function depends on both C, the sampling variable, and also obtained data set D. The goal is to clarify how this estimator depends on C and D separately. Why separate it? Because the sampling bugging is made on a fixed data set D, so this average estimator depends on condition made by condition on D. But of course, because the average estimator still depends on D, so we also want to know the dependence or data of this average estimator. So we need to develop some analytical framework for this kind of problem. To describe the replica analysis for bugging, I will first, very shortly, review the classical replica analysis for the empirical risk minimization. This is a very traditional replica analysis for the empirical risk minimization. Here, there is no additional randomness C, so the goal is just to clarify how it depends on the obtained data set. For using the replica method, we usually introduce both one distribution like this. Because this distribution concentrates on the minimizer of the loss function in the zero-temperature limit, the bet goes to infinity. So the analysis is replaced by the analysis of this possible distribution at the zero-temperature limit. By evaluating the fluctuation with respect to D of the estimator, we use a replicated system of this form. We can duplicate the both one factors in times and multiply it, multiply them, and finally take the average over the data set. So this is a normal distribution that does not depend on the data. One silent feature of the replicated system is that the fluctuation regarding the obtained data set can be extracted from inter-replica correlations if we can extrapolate the computation result for finite integer n goes to zero. For example, the two-point correlation on the replicated system is the second moment of the estimator, average over D, and the higher order equivalence can be computed similarly. So fortunately, for simple data structures, we can obtain a very simplified marginal distribution of this replicated system. Usually, the system is independent of the index of the component of the original estimator. This is very mean field-like, and the parameters of this marginal distribution can be determined by the repregatimetric subtle point or the state of addition of the AVR algorithms. In this single-body problem, this minimization problem, expectation with respect to D is approximated by simple Gaussian random variable, Guzi. So because this problem is very simple enough, we can obtain many desired properties by using some numerical integrals or some analytical methods or some blah blah blah. So this is a summary of the crash-curve deprecone analysis. We first define approximate distribution, and then replicate the system and the average over D. We hope that the fluctuation with respect to data can be exhausted from entire replications. And then we simplify the replicated system for integer-valued n. In very simple cases, we can achieve such simplification by using the exact computation. But in other cases, we need to use approximate inference methods as well done in the late 2000s by Mazan and Opa. I think this kind of topic will be treated in winter morning. And finally, we extrapolate n code as 0 to obtain a simple FFT problem. How can we extend this analysis to the bargain? Obviously, the key point is the second step. Because we have, in the crash-curve case, there is only one source of random variable, the authented set. But in the case of bargain, there are two kinds of randomness, C and D. One way maybe to consider this kind of replicated system just replicates the randomized Boltzmann factor and the average over C and also D. But this is not correct. We cannot separate the contribution of C and D from this replicated system. Actually, the correct way is to use this kind of replicated system. This is a bit complicated, but the idea is very simple. The fluctuation with respect to C, conditioned on D, would be obtained by this replicated system. We just replicate the Boltzmann factors and average over C. From this replicated system, we would be able to extract the fluctuation over C conditioned on D. But we need to finally obtain the fluctuation over D of the average estimated. To obtain such D fluctuation, we should further replicate this abrasive replicated system. Then use this nested replicated system. Actually, we can extract some Boltzmann fluctuations from the inter replicated correlations of this nested replicated system. This is an example. In the first term, only the replicated indices inside of the replicated are changed. A1 and these two red colored replicated indices are the same, but the next blue colored replicated indices are different. This is the second moment of the estimated average over C and D. And in the second term, we change the first loop of the new A1 and B1. And then we obtain the second moment of the average estimated. So we can obtain the variance of the estimated. Maybe we can visually show this randomized nested Boltzmann replicated system. Inside each public box, the Boltzmann factors share the same random value of C1. And then these Boltzmann factors are multiplied and averaged over C1. And the other public boxes are the same. And then we multiply these average Boltzmann factors and finally take the average over D. As you can see, this figure is very similar to the three diagram of the one step of depiccassimitive breaking analysis. So we can associate the order parameters for each hierarchy of the this nested replicated system. At first step, two Boltzmann factors are picked from different public box and then it is described with order parameters. This is the average estimated, square of the average estimated and finally taken the average with the data. And in the second layer, we pick two Boltzmann factors from the same public box and then it is described with order parameters. And the final step is just pick up the same Boltzmann factor twice. So we can depict this kind of order parameter structure in the paradigmatics in the form of the paradigmatics. So order parameter is the second moment of the average estimated and the variance with respect to the resampling and the rest one is the fluctuation with respect to the Boltzmann distribution. So the analysis of this nested replicated system is essentially the same with the 1SB analysis. But here the break in two which corresponds to the break in a positive parameter in the 1SB analysis is also taken to the zero limit. So this is like a 1SB analysis but very close to depiccassimitic solution. Yeah? So could you use a microphone one? Okay. I want to ask what types of matrix is this? Is it a convolution matrix? This matrix just summarized in a product of the order parameters. Each component, if we define this matrix of Q and then Q, E1, E2 and E1B2 nested replicated system in the computation of the position function of the nested replicated system this kind of parameter appears and I summarize these parameters. So I think this is not the same with the confusion matrix. Okay? So just clarification on the notation. So for the second term is the variance of the Gibbs product WI but the C is but conditional on the D. So the first two terms are fully conditional on the D is the things on the inside. Yeah, yeah, yeah. The average of C is taken by condition on D. And the last one is just over the randomness of both simultaneously. That's the notation. Okay, thank you. Okay, no question anymore? Okay, ready? Proceed. Is it evident that you should have this kind of 1SB structure or could you think about other structures that... Is it convenient for the computation to take this 1SB structure kind of matrices or is there another fundamental reason? I think I couldn't get the point. You take this kind of structure which are reminiscent from the Parisi approach for replic asymmetry breaking. Is it because analytically it is convenient or is there a fundamental reason for taking that structure also for that specific analysis? It's just because in the case of analytically solvable case using this matrix is just very convenient. I don't know if there is no further fundamental reason. So is it expected to be exact? The computation, the solution obtained from this type of NSATs is expected to be exact? Yes, okay. Okay, this is a summary of the replic analysis for assembling. The first step is exactly the same with the traditional analysis. And the next step we need to construct a nested replicated system. We first replicate the system in one time and average over C and then replicate the system in two times and finally take the average over D. We hope that fluctuation of C and D can be separately extracted by carefully adjusting the replic indices. And then again, simply for the replicated system by exact computation or some approximate inference. This step can be made by, this step is basically similar to the 1SB recognizes. So if you can compute the replic asymmetric solution for one problem then extending it to the resampling problem is very easy. And finally we need to take the limit in one and two goes to zero. Actually this kind of analysis has been used several times. For example, the derivation of the state of addition or the replic analysis of the approximate resampling algorithm these two papers use the same kind of analysis. And recently, Brunner-Lorado and his co-authors also considered the similar kind of formulas for analyzing the random future ensemble. They also derived the proposal rigorous analysis for this kind of problem. Okay, so let's move on to the applications. If there is no more questions. Then let's move on to the application. The first problem is the sparse regression. This is a setup for the sparse regression. We have a pair of input and output. And in this talk, I do not show any structure on the input obtained data set, sorry, input. I mean XMIR is generated from a spherical Gaussian distribution. But the output Y is generated from a linear teacher with some sparse coefficient. It is sparse is since that each component of the parameter is generated from Gaussian distribution. Here raw specifies the sparsity of the parameter. Of course the goal is to run nice predictor for new input X or equivalently to run this sparse coefficient. Since one popular method for estimating such a sparse vector is to use Russell. So let's consider minimizing this kind of randomize cost function. This is a combination of the randomize squared error and the L1 regularization. In this talk, we consider the boot sub case. So XMIR follows the person distribution with mean mu B. Because here XMIR is normalized by the mean of the person distribution it contains a non-randomize case in the limit of large mu B limit. Because in this case the average goes to 1 and the variance goes to 0. So there is no randomization. We consider boot sub case for the predictor for new input X. Then by using very simple computation we obtain the formula for the generalization error. Except that the estimator is based with the average estimator this formula is completely same with the traditional linear regression problem. There are two questions that I'd like to address. The first point about the improvement of the generalization error. Of course the bridging procedure will deduce the variance of the estimator to some extent but why is this significant? Or is this different from the L2 regression? This is the first point. Someone is not muted. The second question that may be a bit more interesting is the choice of the best hyperparameters. There are two hyperparameters that we can control. The first one is the size of the boot sub case. If mu B is made less there is almost no randomness so the bugging is not so efficient. But when mu B is very small then the variety of the estimator for each randomness cost function is very less so assembly will be very significant. Of course there is one more parameter, the regularization parameter. I'm interested how does the optimal parameter depend on the dataset I mean the sparsity of the W0 and the size of the dataset. This problem is a bit simple but I think it's a nice good start point. As already explained we can obtain a simple effective single body problem by using 1SV like analysis. In the end of the day we can obtain this kind of effective single body problem. But here the local field, H, has two sources can be written by the superposition of two Gaussian variables. The first one comes from the obtained dataset. This is very traditional one and here we have another one, red colored eta. This Gaussian random variable effectively represents the randomness that comes from the resampling. So the macroscopic quantities that appear in the generalization error this guy and this guy can be used, can be replaced by using the solution of this effective single body problem. Here the average with respect to the resampling can be replaced by the Gaussian random variable eta and the average over the, in this empirical average or the average over the dataset can be replaced by the Gaussian W0. And here is the result for the performance comparison. By using the subtle point condition of the 1SV analysis I numerically computed the generalization error for each row and alpha and I numerically optimized the generalization error This figure shows the ratio of the generalization errors with the bugging case and with the bugging case. The horizontal line shows the sparsity of the parameter and the vertical line represents the size of the dataset normalized by the input dimension. And as you can see the generalization error is uniquely reduced when W0 is not so sparse but when W0 is very sparse there are sufficient large number of data set, data point, the improvement is not so significant. So if we only focus on the generalization error then the bugging is very similar to error to regularization. This point is more clear if we consider the elastic net case which is a combination of the L1 degressization and the L2 degressization. In this case, almost no gain was obtained because there is a slight gain so in strict sense bugging seems not to be the L2 degressization but almost similar to that. This is the optimal choice of the hyperparameters but in this case something interesting happens when W0 is very sparse or there are sufficiently large number of data points then the best choice here the optimal parameter is plotted in the log scale. When the situation is very nice then the optimal choice is to use a strictly positive and relatively large regularization parameter. This is because in a good situation the L2 degressization meter without the nanomization is already very strong but when W0 is not so sparse or the data set is not so very large then the best regularization parameter seems to tend to be infinitesimalism more. What's happening in the public region for confirming this point I also plotted whether the number of the unique data points in each sample data set is greater than the parameter dimension or not. In the public region the determined situation is achieved in each randomized data set. The region below the red line is very trivial because the data set is already smaller than the parameter dimension but what's interesting is the upper right region in this case in this region even though there are very many data points the best choice is to use small mu B so that for each randomized data set under determined situation is achieved. To summarize when the parameter is not so sparse then the regularization parameter tends to be infinitesimalism more and the number of unique data points in the sample data set is smaller than the input dimension so it says that the best choice in the not good situation is to use the ensemble of interpolators this is the minimization of the error norm by keeping the input-output relation exactly. Schematically speaking this is a phase function if sparse regularization cannot find the sparse structure of delta 0 sufficiently then the best choice is to give up to find the best sparse estimator but instead best choice is to maximally increase the variety of the estimators but keeping non-trivial regularization which is the ensemble of interpolators. Actually increasing the variety of the weak runners which is the estimator for each randomized data set is a very well known strategy of ensemble running but I think the appearance of the phase function in the hyperparameter space is not so trivial and this is a short summary of the sparse regularization sparse estimation packing is a special use in point of view 0 is not so sparse but this property is just very similar to the digital regularization so almost users in the elastic net case but what's happening inside is very different from the digital regularization optimal choice of new B and random shows the phase function when W0 is based sparse or alpha is large using, packing is not so efficient but in other case there is a reason that the best choice is to use the ensemble of the interpolators also this problem is very simple but something non-trivial thing appears from the sparse structure this point is very different from just considering the digital regularization which is measured this is the result for the sparse estimation if there is no question then let's move on to the next application running crash failure from the imbalance data set this is the setup for the classification problem there are two components of the Ducatiian big distribution the first one is for the positive examples blue one and the other one is for negative samples the goal is to run a crash failure that can generalize the way to both positive and negative samples we want a high accuracy rate for inputs from either classes in particular we focus on the case of imbalance setting the point is that the intercept of the classification plane which is depicted by the black dashed line is strongly affected by the imbalance in the left case the number of each cluster is same so the intercept of this classification plane lies on the close to the origin but in the right case positive samples are very small compared to the negative samples so the intercept of this classification plane is strongly biased towards the minority class in the limit the majority over frame the minorities then the best if we do not take into account of the imbalance the usually the best classification plane lies around there so we cannot detect positive samples there are many methods for treating such imbalance the first kind of type method is at the data level and among them down sampling is I think this is most popular way in this case we minimize this kind of cost function the data points in the minority majority class is down sample which is represented by this C new the key point is that for minority class this sampling is not used because in this randomized dataset the number of samples in each classes are the same so this method should have low bias but because the number of total data points used in this randomized cost function is small so this method should have large bias and there is also another way which is a bit computationally demanding this is usually called under bugging which is obtained by taking the average of this method with respect to C this method is usually used to reduce the variance there is also another method which is at cost level this is a very simple one among them which only considers weighted cross entropy which uses different weight on each classes usually in minus for minority class majority class is taken to be very large in this case all data points are simultaneous use so the computation is very easy and you may think of it as a very naive method but this is very popular and standard and even implemented in circuit run so many methods have been proposed including the ensemble method actually there are too many methods proposed so the choosing one good method is very difficult so what I want to ask is that do we really need to aggregate or do we really need to aggregate because aggregation is computationally very expensive because we need to obtain the estimator for many realizations of the sample data set if this bugging procedure is very similar to digitalization maybe we can obtain a nice estimator just using one realization of the sample estimator and by choosing the good realization I also want to know if the sampling procedure is better than the cost based one or not I think when there are many data points in the classical asymptotic regime the sampling approach and cost based approach will not so different but things in high dimensional settings may be very different so I would like to consider this kind of proportional limit again we can construct a single body problem for this kind of problem in this case we cannot obtain the generalization error by using simple L2 Miscule error or the parameters but we can obtain the rebound logic of the output which is defined by the linear combination of the parameters for excluded data points we can show that the empirical distribution of D1 out estimator can be written like this again this is the superposition of the two Gaussian randomnesses the mean body depends on the input class but the fluctuation can be only Gaussian the first one comes from again data and the other one is against the sampling so the average estimator should behave like this if we take a very large number of sample datasets the red color tongue would vanish so variance would be reduced and the sampling case corresponds to the k equals to b1 this is a simple classic of the theoretical prediction this is the empirical distribution of the rebound logic is compared with the theoretical prediction theoretical prediction is just a Gaussian and it seems that the two distributions seem to be very in good agreement this agreement can be even very apparent if we use the quantile-quantile plot here the size of the input dimension is about 8 k and the ratio of the minority and majority class is about a quarter so here this is a very biased, imbalanced case but the prediction is very accurate and here is the performance comparison let's see first the fixed realisation case the left panel shows the F2 score which is the harmonic mean of the accuracy for both minority class and majority class as you can see weighted cross entropy method, weighting method performs very bad in high dimensional settings but the sampling approach, this is a time sampling case and this is a debugging case these two guys perform very well even in high dimensions this thing right here comes from the phase transition from linear separable region to linear and separable region because the bias term of the weighted method is very strongly affected by this phase transition it cannot perform well in high dimension setting and also we can see that under debugging debugging can make a significant improvement against down sampling and what happens if we optimise the regularisation parameter round in this case even for the optimising regularisation parameter the bias term for the weighting method is not so good it still has bias powers and minority class but on the other hand again the cross entropy method down sampling and under debugging method is very better than the weighting method and the under debugging method is still better than the down sampling this may come as a surprise because in the previous cases the generalisation error is the same with the optimised transregist method but in this case because the total amount of data used in the training is much larger than the down sampling case so we can't do some performance gain and we can also see that under debugging seems very insensitive to the choice of the regularisation parameter so it seems that in the high dimension case which would be relevant to the running neural networks the sampling method is very nicely performed well and this is a short summary for the running classifier from imbalanced data under debugging as I said is very better than the down sampling and regularisation this is because the total amount of data point used in the training is different this is the correct characteristics of the imbalanced data and under debugging is based on insensitive to the choice of the regularisation parameter and the existence of the phase function and the cost sensitive method is much better than the sampling method so you need to special care for such cases but training a special cost function is a bit difficult so I think the sampling method is very useful the sampling method is the best recommended way this is the summary of this talk the motivation was that we want to know the difference between the L2 or L1 regularisation and the assembling and the interesting phenomena around the assembling method and we can introduce a different method for the assembly running and this analysis can be easily made by using the one L3 like analysis and we related to applications and in the simpler cases the generalisation error cannot be improved but there is one phase function which may be interesting and the other case which is more structured case under debugging method is much better than the regularisation method so possibly these two applications are very simple but possibly the assembly running in more structured data may provide a very fruitful phenomenon so I think it's nice to further proceed this kind of direction that's all, thank you for your attention thank you very much Takashi is there any question from the audience? Takashi, thank you so much for the great talk I have a minor clarification so in the first part of your talk when you were kind of assembling and sort of adding the CMU variables to your cost function right in that case your estimator the one you analysed Y hat was still taking an expectation over the C variables right so could you say a bit more about that I thought that in this case you would just analyse the predictor which is X transpose W hat C without averaging over C so could you say a bit more why you average over C? why average over C? yeah because it seems that in the cost function you get this additional randomness because the randomness from C is not averaged out so if that's the case then I thought even for your predictor you should just analyse X transpose W hat with the C being quenched disorder in the system similar to the just in terms of something so you mean that why I do not analyse the fixed random variable C exactly because this estimator the estimator for this random variable cost function has additional randomness so the variance should be much larger than the original problem I guess so so I think such estimator is not so useful because just it has a very large fluctuation of course we can characterise its variance but I just think it's not so useful if you have some applications please but it can be done if once yeah yeah of course it's very easy thank you okay thank you for good presentation I'm just wondering when we were comparing the under bagging and down sampling now looking at the fact that you know there will be more data used in under bagging than down sampling I'm just wondering will we be justified saying that from this scenario that under bagging is performing better than down sampling looking at that you have more data training for under bagging than the down sampling so you mean that you need a clarification more that because under bagging down sampling is only one realisation of C is used right so in this realisation there are many zeroes in so in such data point the data point with C new that with C new is zero such data point is not used so it's not never used in the training but if we consider many realisation of C all of the data point may appear in the training then use the data point is different I have one question in the first if you go back a few slides back where you just show the simplest bootstrap technique I think you call that bootstrap where you just sample the data set you have this Cmuse you have this Poisson variables you are sampling each time you get a different estimator for a different data set and you are averaging over these estimators it makes me think about the kind of Bayesian estimator you are averaging over population of estimators my question is is there a kind of equivalent Bayesian estimator which would take all the data into account but you have an effective temperature instead of solving the minimisation problem for different some sample data sets you go over them you take all the data into account you do a Monte Carlo you get a finite temperature estimator but from the whole data right away is there an equivalence between the two or is it really different? That's very interesting actually there is a similar argument made in the last century by the F1 which is the creator of the Poisson method but the exact relation has not been obtained and maybe we can delay the Bayesian estimator and the Poisson one but still I haven't tried it but it's very similar and I think it's worth tackling so if I find something interesting I do it Okay, so it's an open question we don't know if there is really a connection Okay, thanks Hi, thanks for the very interesting talk I was just wondering if you can clarify maybe a little bit why you expect this one RSV structure to be exact and maybe the more specific question is if this is equivalent to some if averaging over the sea is indeed equivalent to some more explicit procedure then I guess what you're claiming is that procedure replica symmetric prediction should be exact in that and I was just wondering if you can clarify Sorry, I couldn't get the point Do you mean the exactness? I just wanted to get your insights on why you believe that this one RSV kind of analysis should be exact This is just a little bit these two slides are just explaining the intuitive picture but we can actually compute exactly compute the partial function of this random nested duplicate system and then exactly the same structure appears so I just use it in the exact computation So maybe you expect replica symmetry because the problem is convex or? Yeah, yeah, yeah because for each example dataset the problem is almost convex so I think the problem should be exact in the RSV Okay, thank you In your talk I'm a little bit confused when you were talking about bagging because I have used bagging for example do you look at comparing the assemble itself with it base learners because I don't know if you look at that area as well like when you have the assemble you have the base learner for example if you have example let's say for example you choose like three five seven and nine for example that is the assemble and for each example it has its own base learners and these base learners are the weak learners who compare the assemble itself and it base and I don't know if you look at that area in the classical tradition of machine learning because what I can see from your presentation is logistic regression and other service machine learning algorithm Actually there is no so deep reason I chose the bagging just as a start point Because this is why I'm saying this is because from your presentation you said the issues that bagging is under bagging is performing better this under bagging you were talking about is it different from the normal bagging techniques in assemble learning I have no short answers I will discuss it later Okay maybe I will talk to you later Sorry Alright is there a last question Alright if not let's take a show again Thanks


What's good Josh you're both back again with another video So I was able to check out a couple clips from Monday Night Raw once again You guys know I don't really watch raw too much not even like the full show or even have to show I usually just watch the important clips to kind of keep up a little bit what was going on storyline wise But um, that's pretty much what I did here. I checked out a few clips. Actually, um, I checked out Goldberg and Drew McIntyre coming back and I also checked out the Alexa bliss segment with Randy Orton and Probably the most notable thing that's uh in my opinion that happened on the show is Edge confirming his return to the Royal Grumble. So Let's start with the drew McIntyre returning back after a couple weeks of you know dealing with the COVID situation he Cut a really emotional promo very heartfelt. It's good to see him back Healthy, you know doing what he loves and all the people that sent him, you know get well wishes That's that's amazing. You know I'm saying like like I said in the video where I pretty much talked about it It's COVID is a real thing, you know, one of my close family members just recently caught it So it's it's it's something serious and you know, you definitely got to take it seriously So it's good to see that he's able to compete for the Royal Rumble so he can go against. No, no, there's an old bird I really don't want to see this match. I don't I don't think this match should happen I think this is a waste of a potential opportunity for somebody else to get the spotlight But instead you give it to old bird. Don't know why if Drew loses this match WWE they just don't care about their current talent. Hi, Drew loses this match I just give up on WWE when it comes to the the main roster side, especially on the raw side of things that Vince doesn't know what he's doing. So keep my fingers crossed that old bird does not win But you never know what this company now the savings that I didn't really get is when Miz and Morrison Came out and Miz still had the money in the bank briefcase Now correct me if I'm wrong, but I could have sworn the Miz cashed in his money in the briefcase at TLC and he lost the match So how? Let me know. I don't watch raw much. Let me know how he was able to get the money in the briefcase back But that doesn't make sense Like it doesn't matter what match you cash it in on if you lose that match you lose the opportunity So how did he cash it in at TLC? But still has the briefcase somebody let me know I don't feel like doing a research to even figure that out because to me that doesn't even make sense But the money in the bank is not what it used to be anyway The money in bank briefcase is it doesn't have that that much weight like it used to so that's me here nor there So that kind of confused me but other than that it was good to see Drew back But I don't care for the match not looking forward to this match if it's not a squash match. It's a waste of time Next thing I want to talk about is Alexa Bliss and her character like character transformation I know she's in line with the fiend She's been doing some crazy stuff past couple weeks Apparently I did see the clip where she blew some fire at Randy Orton and it messed up his face Like I thought that was quite comical So she's going through her little phase and I believe she had a match with Oscar for the raw women's championship and it was like she was Transforming mid-match one like at the beginning of the match She's the current character that she is and then in the middle of the match It's like she transformed into her old Alexa bliss gimmick and then she transformed into Towards the end of the match like this fiend hybrid of Alexa bliss like it was it's like she's no-selling moves She's powering our moves. She's using the mandible claw. I don't know. It was just Kind of a little hokey to me once again. It's just like I don't know about that But then Randy Orton pops up from behind He's quote-unquote still disfigured from the fire.  Like trust and believe if he literally got his face set on fire He would not be wrestling but nevertheless. He comes out there from behind and he hits Alexa bliss with RKL It's not too many times Randy Orton hitting women with RKLs, but when he does it's pretty devious But it's like can you blame him? She set the nigga face on fire I don't condone women being hit or hit with RKL I'm just saying any who point is that segment was kind of weird to me as well I don't know. I just the hoakiness of wrestling is it's always been there You know, I think it works better in the the 80s and the 90s, but Since social media is so prevalent as like everything's just you can get at your fingertips on your phone It's just like that that whole segment with Alexa bliss changing characters mid-match It was just kind of cringe to me But comment down below let me know if you like that like boy her character is kind of changing and obviously in my opinion the highlight of The show or the clips that I did see in my opinion It's none other than edge returning back to the Royal Rumble one year You know run one year removed from when he returned when he came back To the Royal Rumble last year in 2020 So it's it's kind of crazy that he's coming back again in 2021 This is dope this add a little bit of more dynamic to who could possibly win the Royal Rumble this year Do I think they're just gonna win it? There's a good possible chance edge could win the Royal Rumble I'm not even gonna lie to you Not even gonna lie to you I'm still riding with Daniel Ryan in my opinion I think Daniel Ryan and Potentially him going against Roman Reigns would be the better story But I'm not sure you know I'm saying but I will say this this promo that he delivered was very powerful very impactful and the theme of the promo is life is so like Short and ever-changing one minute you think things are gonna go your way and then the next minute It completely changes your whole Direction and sense of accomplishment changes because of one thing and he was talking about when he won the championship Years ago ten years ago, and then he had to relinquish it Because he has to retire pretty much and then when he comes back and he's you know He's got this momentum and then he ends up, you know Ends up getting injured. He has to sit down on the shelf like that's kind of what life is And you know not giving up from that. It was a very emotional promo Nothing let you know, I expect that from edge, you know, Zanny He's definitely gotten much better on on the Mike skills over the years. So Honestly, I'm very intrigued now even more with the Royal Rumble match because I want to see who can possibly win this Still the match for me that I'm looking forward to the most is of course from Reigns and Kevin Owens I still think that's gonna be the marquee match But the Royal Rumble just got even more interesting just because edge got involved so I would love to see How this is gonna play out. So comment down below. Let me know Do you guys think it's just gonna be the favorite now since he's announced himself in the Royal Rumble this coming Sunday Do you think he is going to be the favorite to win? Or do you guys still have Daniel Bryan? Some of you guys are saying Y'all have I want to say I think a couple you guys were saying Shinsuke could be the The potential winner like I want to get your opinion. Who do y'all think is going to win the Royal Rumble this year? I'm still going with Daniel Bryan, but hey, I wouldn't trip edge wanted, you know, Zanny I think if they're gonna go somewhere with this It has to be somebody that they can build a storyline with into WrestleMania and with edge You can easily do that with edge because he hasn't really faced anybody except Randy Orton And I would love for them to revisit that so they can finish that feud off Potentially or if you want to go Daniel Bryan, you know Daniel Bryan got some history with Roman Reigns I think that would be cool too. So and you know Shinsuke you can go Shinsuke route He got some issues with Roman Reigns like it's it's there's plenty of ways they can take this So comment down below. Let me know y'all thoughts and opinions on who y'all think is gonna win the Royal Rumble this Sunday But I can't wait to check it out for you guys gonna have my thoughts and opinions I'm gonna record it that night and post it that night for you guys. So Be on the lookout for that, but I appreciate our love and support roads 40k. Appreciate y'all kick it It would be nice. See y'all in the next one. Peace