Hello. Water. Water. He's a crazy British man. Be careful. Yeah. This will become abundantly obvious in not very long. Water is one of those things that doesn't take up a huge amount of time at organizations and meetings such as this, which I've always found a little bit odd, because at the core of all of our work, no matter what you do, lies the fundamental molecule. So whether you're an education, so if you're an education and you're trying to make sure that these young minds are growing, if they're suffering from lead poisoning or if they're suffering from chronic diarrhea, you are not having the impact that you could have if they were healthy. If you are in food security, obviously, water is absolutely fundamental to you being able to do the job that you are trying to do. If you are an impact investor, and it's my first time here, but I hear there are two of you in the audience. It's worth remembering that for each of the last five years, water has been selected as one of the top five global risks by the World Economic Forum. But the real kicker for me is that three of the remaining four are fundamentally tied to water. Our ability to deliver water at the right quality, at the right quantity, at the right place, and at the right time is absolutely fundamental to our survival. Now that we've got deep, you're probably wondering who on earth I am. My name is Tom Ferguson. I run the Accelerator at Imagine H20. We are a non-profit organization with the mission to empower people to develop and deploy innovation to solve water challenges globally. We are 10 years old, and we've worked directly with 141 companies and thousands more applicants to solve these kinds of challenges. We are affecting tens of millions of lives daily, and we have raised the companies that we have worked with have raised just under 500 million, 300 million of which has come in the last two years. Things are hotting up, so pay attention. But we are here to talk about building and scaling an impact organization in water, and we are in very good company. There are very few people within the water sector that really have a right to talk about this, but one of those people is sitting to my left. Jennifer Schorsch is the president of water.org, and she has previous in scaling an organization, straight out of business school. She joined a coffee company with 150 stores, and eight years later, Starbucks had turned in to the global brand that we know today. She then took her considerable talents to water.org, where alongside Matt Damon and my celebrity doppelganger, sorry, Matt Damon, who is my celebrity doppelganger, should have got that one right, because my celebrity doppelganger isn't Gary White. It isn't, well, not yet anyway. Alongside Matt Damon and Gary White, they have built an organization that has impacted over 22 million people worldwide. So in terms of building and scaling an impact organization, I think we have the right person on stage with me. Jennifer, how are you? I am well.  Absolutely. So in terms of the big changes, I would like to start at your time at Starbucks. So when you think back at your time working within that change, what are the lessons that you keep on coming back to as you build water.org? Yeah. I mean, in many ways, as Tom said, I feel like I grew up at Starbucks, and so there were lots of lessons I was learning steeply. There's probably a handful that I find that I am regularly deployed. We've got some technical issues, sorry. And so I'll just keep talking as Tom gets fixed. And so I think the first one's probably, it's all about your people. It's all about the people. Most of you probably know about the visionary of Howard Schultz behind Starbucks. But many people don't know about Howard Bihar and Orrin Smith. And Howard Bihar did and still does say it's all about the people. And it's really, it's two things. It's about the people that you work shoulder to shoulder with in the mission and making sure that you're putting in place mechanisms to value them. And it's about the people you serve and being very clear on who they are. So that piece was very much ingrained in me. I think the other piece was about being very clear on your purpose. We had a mission statement and it was posted everywhere to be the premier purveyor of specialty coffee in the world. And we had the great pleasure of working with Jim Collins, whom you in the room and those online will probably remember, wrote, built to last, good to great. And he worked with a group of us and in that process, we realized that actually our core purpose was not about the coffee. It was about the experience we were providing and we shifted to define it as to inspire and nurture the human spirit. So being clear on purpose is something that I think I've carried forward to water.org, aggressively innovating. We always felt frustrated that we just were getting to the top of a mountain and little did we know, but Howard saw that there was another mountain behind that to scale and it was hard. We had to become comfortable with discomfort. And then lastly, I think it was about failing forward. Many of the people in the room have had a frappuccino at some point. But what you might not know is that the precursor beverage was called mazgron. It was an awful tasting carbonated coffee beverage. It really was bad. It was overpriced. It failed. But what came out of mazgron was the coffee extract that made frappuccino in the stores and in bottles scale. So a failure became a multi-billion dollar business. And it was only because we learned and failed forward. So those, you know, it's all about your people. Be clear on your purpose. Aggressively innovate and fail forward. That's probably what I've taken most into water.org. That's super helpful. I'm very disappointed I never got to try mazgron. Yeah, I've got a four pack in my attic. Oh, my God, maybe it's got better with age. That's really helpful. And we're going to come back to a couple of those. But I want to talk now. One of the things we get really excited about in water is people who come into water from outside, right? People who come into the sector. Usually people grow up in it and go all right, but come in with a skill set from outside and bring their knowledge to bear within the water sector. So when you got out of Starbucks and you were thinking about the transition, where you were going to go next? What, how did you, how did you choose water? And how did you choose water.org specifically? Sure. So for me, water really kind of came from two things, an interest and an indignation. On the interest side, I had always had an abiding interest in the environment and in women and girls empowerment. And the more I learned about water, the more indignant I became because girls drop out of school for lack of access to watering sanitation, sanitation especially. Women can't work.   So there was an interest piece and there was a deep and kind of growing indignation and it's what still gets me out of bed in the morning. And then in terms of water.org, it's funny because I don't think I realized it at the time, but I wanted my time to matter and I wanted to make a difference and I wanted to do it in an organization that this is the hindsight part. It turned out I went there for the reasons that I went to Starbucks. So there was a big challenge slash opportunity. There was a fundamental compelling model for scaling that was exciting to me. There was visionary leaders and team and people that I wanted to be around. Like you've presented me as an expert, but I'm learning every day and I have the immense pleasure of working with really great people, not just within the organization, but in partnership across the sector. It strikes me that one of the things that water.org has done best is something that all organizations of all stripes and colors and types is that one of the really difficult things to do is focus, right, is to decide what you're going to do, but actually more importantly what you're not going to do and then think about how you differentiate relative to a customer pain point and then go and deliver that thing. Is that a fair reflection? And do you, is that definitely the case? For us, that's definitely the case. And I think when Tom introduced water, you know, water is just such a big topic and we need big brains and good thinkers and innovation in it and you, Tom, and imagine H2O are doing a great job of that. For us in terms of just the focus, I guess I come back to those lessons at Starbucks a little bit. And if you take Howard Bihar, HB's message about love your people and know your people, know your customer, it might be worthwhile spending just a moment on who that person is for us because that creates a real focusing element for us. So our customer is someone like Lenaritia, whom we met in the Philippines, who pays $60 a month. It's about 25% of her income to access safe water from a water vendor who comes into her village. Now, Lenaritia has a municipal water system that runs not too far from her house, but she can't afford the $200 of upfront capital that she would need to connect to that system. The utility doesn't think she can be a paying customer. The banks don't think water and sanitation is something that can be lent for. And so our insight and what we've really focused our time around is enabling affordable access, affordable finance to someone like Lenaritia. So we work with largely financial institutions. We take our expertise in water and sanitation and a grant. We will grant that to help them build the capacity and the lending portfolio for water and sanitation and they will lend Lenaritia that $200. Now, in about a year's time in her case, she paid the loan back, so her $60 monthly cost is now $5 a month. She has safe water at home. She has more income and she has more time. And so the idea is about taking that philanthropy and using it to give a hand up and not a hand out and to realize that people living in poverty are not equally poor, that they can participate in their solution. So that piece, that focusing piece is really, really important to us. Yeah, absolutely. I mean, just hearing you talk about this, there really is very little difference between the startup founders that we work with kind of day in, day out and the way you're looking at it. You're essentially just an innovation organization, not just an innovation organization. And you're saying, what is the problem you're trying to solve? Exactly. Standing your customer's shoes and say, what is their challenge? And don't try to fix it for them. Help them address their own challenge. It's such an important insight and I think that this is something that people often lose sight of is that actually the trust, I mean, it's as true for your clients as it is for your own people, right? You wouldn't just go and solve your own people's problem. You've got to give them the tools to go and let them get on with it. So you see it as the same thing for your clients. Yeah, we do. And I think that, again, kind of harkening back to those lessons from Starbucks, I think that, what's your purpose? Like what is our purpose? Our purpose is about enabling access to water and sanitation, but we're doing that through affordable finance. So we're focusing on people who live on $6 a day or less and we're focusing on using philanthropy catalytically. So we can use a small amount of donated funds to mobilize significant amounts of commercial capital and that blended approach is what we feel is really important. There's a $90 billion a year gap between what the World Bank estimates is needed to put just in place the infrastructure for water and sanitation and what is available through philanthropy and aid. And so we're focused on that. We're using philanthropy catalytically and in terms of just results, the $30 million that we've deployed for this model, which we call water credit, has helped mobilize $1.8 billion in capital and that's money that's going to the hands of people living at $6 a day or less who otherwise would not have access to water and sanitation. And it took us 25 years as an organization to reach our first million people and seven years later, it's now 22 million people. So there's a real scaling effect when you can use philanthropy in a way to crowd in more capital and scale the impact. For all you fans of hockey sticks in the room, there's one for you. This conversation would not be complete without talking about where everyone always walks into a wall, right? There are always challenges and failures. Can you talk a little bit about the fail forward element and how you've seen that? Yeah, I can. So I think the biggest failure where we had to fail forward was around the initial iteration of water credit. We tried to make loans directly and we loaned to an institution then on lent to emerging community-based organizations that were emerging utilities. And what we didn't realize is that they didn't have the business skills to understand how to build a sustainable utility in that case. And there wasn't a culture of repaying repayment. So we failed forward to India where there was an infrastructure for microfinance. There was an infrastructure for repayment and we were able to pilot it there and scale it to 12 more countries globally. So this doesn't work there but where is actually the structures that we can move it over? And we think we have a better chance of... It's fascinating rather than just pulling the plug completely which would have been some people's approaches. So we've got two minutes left. Oh boy. All of these faces out here have already benefited from your expertise but if you want them to take two or three things away with them from once we're done, what would those be? What do you think that really lies at the core of this scaling question? I would maybe leave folks with three things to take away. So one is choose water. You know, we have to put in a plug for the essential molecule. But you know, we've talked about the need and I guess I just wanna touch on the opportunity for a second and maybe it gets back to that indignation I talked about but it's estimated that women and children spend about 200 million hours a day and you heard me right, a day in collecting water. And I guess would ask you all to think about what if we unleashed that power and that potential that we could help those individuals transform that cycle of poverty into a cycle of opportunity? And I just can't even imagine what 200 million hours a day for women and girls around the world could result in.  We need scalable business models. There are models that exist that can be funded and that there are others that can be creative, created. And so we need, as I said, big brains and good thinkers on this issue. And then I think I'd say bring capital. We didn't and won't have a chance to talk about how we think about capital but we need philanthropy, we need impact investment which we also have a sister organization that we piloted and stood up called Water Equity that is an impact investment fund, focusing fund manager, focusing on water and sanitation for people in poverty. And we need balance sheets. We have a global credit enhancement facility that we're launching this year.  I told you she was the right choice. We are out of time. So if you want to know more about this, Jennifer's going to be around for a bit. I know one of her board members, Tony Stainer, is here. There are various people wandering around to do with water. There is Tony. I didn't know that. Hi, Tony. I thought you might do that. And then we have a session on Thursday at one o'clock about investing in the best or you've never heard of, water, so all come and crush the room. But it leaves me to say this has been an absolute pleasure. And please welcome me. Please join me in welcoming Jennifer Shaw. Thank you. Thank you.


Come on, Pussy.  How do I get knocked down?


Micro-ionase, myrnas, are small non-coding ionase that play important roles in the regulation of gene expression. They are involved in the regulation of numerous biological and pathological processes, including the formation and development of cancer. Cancer chemotherapy is often ineffective due to drug resistance, which can be caused by multiple mechanisms. Recent research has indicated that myrnas are involved in the drug resistance of tumors by targeting genes related to cell proliferation, cell cycle, and apoptosis. Furthermore, they can affect the expression of genes associated with drug resistance by modulating the expression levels of proteins involved in the process. This review summarizes the current knowledge on how myrnas influence the drug resistance of tumor cells and discusses potential therapeutic applications for myrnas in cancer treatment. This article was authored by Wengong Sai, Juraingxian, Huilinxian, and others.


And now, tonight's presentation of Radio's outstanding theatre of thrills. Suspense.  We call it, When the Bow Breaks. So now, starring Virginia Gregg with Stacey Harris, here is tonight's suspense play, When the Bow Breaks. I remember when I was little. I remember how you used to swing me in the garden. The big maple tree in the rusty squeak as I went up and down. Up and down. Grandfather, don't let me fall.  I'm afraid because I'm dying. Just the way you died, Grandfather. Why should I die? How did it all happen? This is it, baby. Oh, it looks very nice. Harry, please be careful of my coat. You're dragging it. Uh-huh, sorry. Here, you hold it while I get the key out, will you? I'll get the suitcases like that. Well now, wait a minute. Come on, let's do this right. Harry, put me down. Let him watch. We're married. This is the way it's done. You've got a lot to learn. All right now, put me down, Harry. Please, Harry. I asked you to put me. All right, all right, baby. I'm sorry. You're sweet. I'm tired. I didn't mean to be angry. I'm tired, that's all. Sure. Wow. You like it? It's a very nice house, dear. I'm sure we'll be comfortable. Oh, it's not bad for a short-order deal. Take a look around. Come here. Open this door. It's a closet. Not bad. Other all like that. Nice and roomy. Living room, dining room through the kitchen. Harry, please. Can I sit down for a minute? My head's splitting. Oh, sure, baby. Sure, come here. It's not bad, huh? Come on, off with the shoes. Here, put your feet up. Thank you, dear. It all looks very nice. You must have spent a lot of money, though. Money? Nothing. Soon as the probate judge turns the real money loose, we'll get something that'll knock your eye out. I don't want to talk about that, Harry. I don't even want to think about it. Never again. Of course not, baby. I don't know what's the matter with me. Of course you don't. I don't want to think about it, grandfather. But I have to talk about it to you. There'll be lawyers to pay and the inheritance tax. I never realized how very much you were worth. But you never spoke much about money. It was always there, ever since I can remember. Funny, I never thought about it until after you'd gone. Harry did, though. I lied to you about Harry. I'm sorry. I didn't meet him the way I said I did. I picked him up, grandfather, on the beach. I went looking for a man and I found him. The men you chose for me was so weak.  Like Harry. It wasn't respectable for a woman in her late 30s to do that, was it? I'm sorry. Honey, I guess you didn't know I had a feel for interior decorating, did you? No, I didn't. Well, maybe when we get the dough with... I'm sorry, Evie. Just that I was thinking maybe I'd go in the business. Harry, how did you know they'd let me go? How did you know all this wouldn't be wasted? Well, maybe you'd be here with another girl. Now, you're the girl, Marcy. Only you. Besides, they couldn't have pinned it on you. Now, I watched that jury. Your high class. Now, they believed you. Yesterday. I didn't think I'd be married now and in a new house.  You've been acquitted and that's it from here on in. Oh, Harry! Baby. Baby, you're jumping. Oh, yes, I'm jumping. Well, now, relax. Relax. Only married couples have colors. It's probably the next-door neighbors. You stay right there. I'll get rid of them. Mr. Stryker, fancy meeting you here. What do you want, Corcoran? Oh, he doesn't say glad to see. He doesn't say come in and have a drink. All he says is, what do you want, Corcoran? We're busy. No statements. No statements? You just got married, didn't you? You moved into a new house.  No statement.  Now, make him go away. Harry, please. Mrs. Stryker, well, well, congratulations on all counts, marriage, murder and money. All right, Corcoran, get out. Are you kidding? You and Evie are good for five or six more front pages. You're my babies, my story of the year. Look, I'm going to bounce you or you can beat me to it by walking out, but fast. Why didn't you leave us alone? The trial's over. The state is finished with us. Why can't you... Finished? Evie, you were acquitted, if that's what you mean. Now, you can never be tried again for the same murder. Not even if you said to me it might amuse your readers to know, Mr. Corcoran, that I really did murder my grandfather that he can't touch you. Now, look, boy, I'm going to work you over so you'll never chew a steak again. Oh, I wouldn't. Well, then get out of here. Okay. But first, I came here to tell you something. Evie, go on in the next room. No. What do you want, Mr. Corcoran? Nothing much, Mrs. Stryker. I just want to tell you, I'm going to do a Sunday feature on you.  No, forget about us. Please. Harry, give him some money. Tell him to stop. Oh, you couldn't pay me enough, Evie. The story of why you hold this guy's life in your hands is too good. Now, what do you mean by that crack? I knew your wife had beat the case. A minute I laid eyes on her, Harry, that holier-than-thou look grandfather known by one and all to be a wretched old man, Myerson as a lawyer? You've already written about those things, Mr. Corcoran. Now, wait a minute. I'm leading up to it. The autopsy showed your grandfather had a disease that had killed him anyway in a few months. Anyway, so, verdict suicide, huh? Why can't you understand that? He knew. He knew he was going to die. I don't buy it, but I buy this. You were never brought to trial, Harry, because they couldn't tie you into it. You had an alibi for the night the old man drank that poison. That's right. I wasn't there. The DA knew that you're a photographer, among other things, and had access to cyanide, but he couldn't hang anything on you, so you weren't tried for murder. It was. She can't be again. Not for that one. Harry, I'm tired of this. Mr. Corcoran's trying to frighten us. That's all. Will you ask him to leave? Oh, hella, baby. Go on, Corcoran. Get to the point. Sure. Like I say, you weren't indicted, but it could be if your wife talked. Talked? But there's nothing for me to talk about. What? What could I say? Oh, you could say that Harry got the cyanide for you when you worked it out together to get the old man's money.  I won't listen. I won't. All right, Evie. Hey, okay. Come on, Corcoran. Come on. Sure, sure, don't get sore. If you didn't do it, you haven't got a thing to worry about, Harry. But if you did, then you better be nice to Mrs. Stryker because maybe someday she'll get mad, and if she talks, it's bye-bye, Harry. Come on, Corcoran. So long. Yeah, you got him in the pub here. All right, Corcoran. And, Evie, I can't touch you for it. When Harry came back from the door, I knew what he was thinking. But that horrible newspaper reporter said he wouldn't look at me. He just went to the bar and poured a drink. I was afraid of him. I wanted him to hold me and tell me it was all right, like you used to, but he didn't. And then I wanted to run away. Anywhere. Get on a boat. Fly. Never see Harry Stryker again. Run, run, run. Where are you going, baby? I need some air. I thought I'd... Don't be silly. You don't want to go out there. Corcoran's probably still hanging around. Harry, Harry. What? What is it, Nancy? Harry, it's all right, isn't it? I mean, you don't believe what he said. Of course not. Of course not. He's just a snooper. It wasn't the money, was it, Harry? You didn't want me just because of the money. I'm not pretty. I'm not clever. You don't love me, do you? Why should I attract a man like you? It was the money, wasn't it? Wasn't it? Look, baby, you're upset.  It's okay. Everything's okay. Say it! You think I'm pretty? Harry, say nice things to me. Tell me you love me, Harry! Don't keep your voice down, Evie. Listen, Corcoran's still outside. He's listening. Keep my voice. Evie, you're all upset. I'll get you a drink. No! You don't even think it. Not to yourself. You are listening to When the Bow Breaks. Tonight's presentation in Radio's Outstanding Theatre of Thrills Suspense. Have you some friends who are hard to please at Christmas? How'd you like to give that musician friend a couple of tickets for the New York Philharmonic Symphony that sports fan, too, for the Orange Bowl game and that teenager all the record she wants? How'd you like to do all this on your budget? It's as easy as buying a radio set and giving it for Christmas. And now, we bring back to our Hollywood soundstage Virginia Gregg, starring with Stacey Harris in tonight's production When the Bow Breaks. A tale well calculated to keep you in Suspense. I remember the scent of our garden. I remember when you used to swing me in the evening before supper in the honeysuckle. Sweet, drifting about us. We were happy, weren't we? Not like the sadness that came afterwards and the bitter taste that isn't the taste of honeysuckle. Why am I dying? Grandfather, where are you? Hold my hand then I shan't be afraid. I'm afraid of Harry standing there so close. This dark, angry face looking down at me. Evie. Evie, you've got to get a hold of yourself. Do you hear me? Yes, sir. Yes, all right. Now, this is no good. Evie, don't you see what Corker wants if he can get us at each other's throats he'll have us and then his lousy story. But I wouldn't do anything to hurt you. How can you think such a thing?  I won't, Harry, honestly. I'll never say a word. I won't. I just want to be happy with you. I want you to love me. Sure, baby. Sure, but you've got to understand. It's like Corker and says it's my neck now, not yours. You've got to be careful. Harry, maybe if I went away somewhere alone, maybe I could rest and forget the trial and just go somewhere and rest. I'd come back. You wouldn't have to worry about me saying anything. Evie, stop. Everything would be all right when I came back. You'll let me go, won't you, Harry? You wouldn't do anything to me, Harry. Honey, honey, I didn't mean to do that. You've got to believe me. I didn't. But you see what you like? I've got to watch you even take care of you. It's all right, Mouse. Don't. Now do you see? Look, look, all of a sudden you're afraid of me and just because of that Corker and guy. Mouse, you mustn't be afraid of me. We're in the clear, don't you see that? It's all downhill now, Evie. Your grandfather was already dying, only we didn't know it. So when we did what we did, luck was with us. We're in the clear that they couldn't prove anything against you, then they can't. I've always been afraid of you. I've always done what you wanted me to do. Part of it was because I loved you, but most of it because I'm afraid. You never let anything get in your way. Grandfather was in your way. Now maybe it's me. What are you saying? It was all my idea? Are you saying you never asked me to help you get rid of the old man? I don't know what I'm saying anymore. I'm tired. You're tired? Sure. Surely that's it. You're tired. I'll get the bags out of the car and then we'll fix something to eat, huh? You gotta start getting domestic, Mosse. You're an old married woman now. He went out to get the suitcases, but he left the front door open. It was too cold to leave the door open. He wanted it that way so that he could see me, and care me if I picked up the telephone.  When you used to put me in the hall closet and make me stand in the dark with the door closed.  Is that why I killed your grandfather? That night I had a dream. It was a funny dream. There was an enormous alarm clock in the middle of a field. The sun was shining and you and I were dancing in time to the clicking of the clock. Then Harry came along and he tried to dance too, but he was clumsy and tripped and fell down. He waved his arms and his mouth went open and shut, but we couldn't hear him because of the ticking. We laughed so hard. What are you laughing at? What's so funny? Harry, nothing. It was just a dream. Yeah? Tell me about it. I can't. I don't remember. It was just funny. You didn't sound like you were asleep. I was. Really, I was. Maybe you figured out a way to tip off coconut about me, huh? Maybe that's what's so funny. Oh, Harry, it was a dream. No, look, you haven't been kidding me. Why do you think I've watched every minute? I know what's on your mind. You're wrong. I thought we were happy. With what you're holding over my head, with what you can do to me, are you kidding? Listen to me. I haven't slept since the trial. And do you know why? Because I'm afraid of you. Every time we go to the store, every time we go out anywhere, I'm waiting, waiting for you to run calling the cops. But I wouldn't. I've never even thought of it. Not since yesterday, huh? You thought of it plenty then, didn't you? Didn't you? Yes, no. Yes, yes. And that's why you were laughing because you figured you can get rid of me now. No. No, no.  And look, don't, don't, don't do kid yourself. I figured out the whole thing. I'm the one that got us in the clear, and now you want to mess it up. Evie, I'm not going to let you. You're going to do too much. That depends. That depends on whether or not you can keep your mouth shut. Because you wanted the money. No! I understand. I wanted you. And I wanted to get away from him. Not away. Marcy, look. It's all right. We, we've done it. Now we've got to follow it through. You didn't want me without the money. You didn't want me at all. Just the money. You don't love me. It happens, I do. That, that's something you can't understand. I've tried to make you believe me. And now something like this comes up and I'm afraid of you. I keep remembering that if you want to, you can send me to the chair. I couldn't hurt you. Well, don't worry. You won't. I'm going to watch and see that you don't. Now go on back to sleep. The next day he didn't talk to me at all. He didn't say a word. We just sat around the house and I felt ugly. I prayed that I would suddenly be beautiful because then he'd trust me and he'd know that I wouldn't hurt him. It was after lunch that he went into the kitchen. I wanted to watch him now just as he was watching me. I couldn't bear to have him out of my sight. Supposing he went out the back door, came around to the front and quietly crept up behind me. I got up and stood behind the dining room door looking through the crack. He was taking down a vial from the closet where he kept his photographic things. I couldn't see a label but I knew what it was. He put the vial in his jacket pocket and closed the closet. I was going to be murdered with cyanide the way you were, grandfather. You stay in here, the inn. Keep quiet. I'll enter the door and I don't want to hear a sound out of you. Sure, sure. OK, son. Some other time. As a service to you so that you may keep abreast of world events, your newspaper... Look, buddy, we don't need a paper.  I'm sorry, kid. We're not interested.  I should have known. I'd get back in the living room and you're crazy. You! Amy, I ought to kill you for trying that.  That's what he was going to do. I had to get away or use the phone. Call the police. I needed you, grandfather. I needed you to help me. Slowly back into the living room and took off his jacket. I thought how funny it was. Just like a man before a fight.  What is it like to die, grandfather? I used to ask you when I was little. When daddy died. It's hard in here. I'm going to have a drink. I fixed the phone so you can forget that. I'll only be in the kitchen so I can hear you. Don't try anything. How was he going to use it? The cyanide. He hadn't taken it out of his jacket pocket. It was still there. I knew it was. I heard him open the refrigerator and get out some ice. I didn't even have to get up. I could reach his jacket from where I sat. I was very careful. Used my handkerchief to get the vial out. I'd learned about fingerprints during the trial. I knew just how much to put in a drink so that it wouldn't taste. Harry had taught me that. I was reaching for the cork in the whiskey bottle when I heard him coming back. Why don't you keep some glasses in here? I took the cork out of the whiskey bottle and emptied the vial of cyanide into it. I was sitting in my chair when Harry came back with the glasses. What's the matter with you? Nothing. Are you sick? No. No, I'm all right. You don't look all right. You better have a drink. I don't want any. Well, you better. We've got to talk. Don't bother to pour it. I won't drink it. I'm not going to. He'll fix you up. Here. Drink it straight. Come on here. No, Harry. I don't want it. I can't stand the taste of it. Don't go getting hysterical again. Come on. It'll settle you down. I won't. I won't. Come here. Will you drink it? Go on me. It'll make me sick.   Come on. Here. Drink it. Go on. Drink it. I tried to fight, but I couldn't. And I saw Harry fade away further and further until he was miles above me leaning down and calling to me. I couldn't see his face. Then there was nothing. It was very... except for this wing. Quiet. You won't let me fall, will you? But you're there. My hand grabbed fire. I don't want to be alone. She's gone. Surprise. You held on so long. Cyanide kisses are usually faster. Cyanide? Wait a minute. No telling. Okay. Come on, Stryker. We're going downtown. What are you talking about? What's this about cyanide? Didn't you know? There was an empty bottle in your jacket pocket. Your prints all over it. On the glass she drank from you. Well, sure there are. She was having hysterics. I made her drink from it. Cyanide never cured hysterics, Harry. I didn't do it.  Like you didn't have anything to do with a grandfather's murder. I know. It was a matter. She threatened to talk. I didn't do it. I tell you, I didn't. Okay, so you didn't do it. Come on.  I put that cyanide in my pocket because I was afraid she'd use it on me. I wasn't going to kill her. I was trying to quiet her down. Brother, when you quiet him down, they stay quiet. Let's go. Spence, in which Virginia Gregg starred with Stacey Harris in tonight's presentation of When the Bow Breaks. Next week, the story of Christmas shopping that went astray. We call it A Present for Benny. That's next week on Suspense. Suspense is produced and directed by Anthony Ellis. Tonight's script was written by Sheldon Leonard. The music was composed by Rene Garagang and conducted by Wilbur Hatch. Featured in the cast were Joseph Kearns, Richard Beals, and Barney Phillips. On CBS Radio, it's authentic, literate, and dramatic. It's gun smoke every Saturday and Sunday. Stay tuned for five minutes of CBS News to be followed on most of these stations by The Jack Carson Show. Friday nights, join us in the 21st Precinct on the CBS Radio Network.


Hi, thanks for viewing another 12pm tutorial. This tutorial will teach you how to install and use JAP, the John Doe Anonymous Proxy. The primary purpose of this program is to hide your identity from the websites you visit, your internet service provider, and anyone who is watching your internet connection. JAP does this by using a system of mixes to scramble your information with the information of other JAP users before it reaches the internet. When you turn on the JAP anonymity service and then request information from the internet, your request is encrypted and then filtered through a number of mixes called a MixCastGate, which rearranges the identity of your connection by mixing it with bits and pieces of other users' identities. The final mix in the MixCastGate then decrypts your request so that you can view the information you wanted anonymously. Now that you know how it works, let's go get the software. The address for the website is kind of long, so just open up a web browser and go to your favorite search engine. Now type in J-A-P and look for a site called JAP, anonymity and privacy. Once you find it, click on the link. On JAP's website you can read about the software and how it works. Go ahead and explore the website before we move on with the tutorial. Once you're done exploring, take a look at the right hand side of the page. See the link under download that says stable version? Click on that link. Now you have to choose the stable version you want. Since we're using Windows for the tutorial, let's click on the Windows link. Now you have the option of downloading JAPSETUP.EXE or the complete setup program. Just download JAPSETUP, which will take a look at your computer and download what you need for the program. Go ahead and click on JAPSETUP.EXE and then save it to your computer. After the file has finished downloading, close your browser, go to the folder where it's saved and double click the JAPSETUP.EXE program. Click Run. Here JAP asks you what you would like to install. JAP uses Java in order to run the anonymizing service, so either check the box for Java 1.3 or get the most up-to-date version of Java from www.java.com. If you have Java, all you need to do is check the box marked JAP and then click Next. Choose where you want the program to install and then click Next. Choose the name of the start menu folder and then click Install and wait for the program to finish installing. The speed of the installation process will depend on your internet connection speed because JAP will be downloading necessary components. Once the installation is completed, click Close. Now go to Start, Programs, JAP, and click the JAP program. When you run JAP for the first time, a window will pop up called Installation Assistant. This will walk you through making sure that JAP is working properly on your computer. The first thing I'll ask you to do is choose a language. Go ahead and choose a language from the menu and click Next. Now you get to choose whether you want a simple view for the user interface or the extended view. Let's stick with the simplified view for now and click Next. This page tells you that you need to configure your web browser to connect to the JAP service. I'll walk you through how you do that now. First, open up your web browser. We're going to use Internet Explorer. Now go to Tools, and then to Internet Options, and then click on the Connections tab. Click the Land Settings button. Check the box that says Use a proxy server from your land, and then enter Local Host in the address box and 4001 in the port box, and then click OK. Click OK again, minimize your browser, and go back to the installation assistant. Click Next. Now JAP wants to run a test to see if the service is working correctly. Move the installation assistant out of the way and turn off the anonymity on the John Dew user interface. Now go back to your browser. Try to visit a different website. I'm going to try www.nytimes.com. If JAP has configured correctly, you should see a warning box alerting you that you are not anonymous. When the box pops up, click Yes. If the website shows up with no problem, go back to the installation assistant and check the option that says the warning is show, and then click Next. Now JAP wants to switch the anonymity back on and surf the web of it. If you can access the Internet without any difficulty, click the option that says Connection established, web surfing is fine, and then click Next. Then click Finish. Take a look at the JAP user interface. As you use the Internet, it will keep track of your response time, the number of JAP users, and the speed that you are accessing the Internet at, and the amount of encrypted data you have transferred. As an added bonus, if you live in an area where the Internet isn't censored, you can allow people from areas that censor the Internet to use your connection to circumvent that censorship by clicking the box marked Anti-Censorship Service. To turn the JAP service off, simply click Exit. Remember that the browser you configured to work with JAP will not work without the JAP program running. You'll need to delete the LAN setting from earlier in order to use it on its own.


You've also given presentations on best author preparation strategies. You've worked closely with authors, working with them, incorporate quality leads or address inconsistencies. What points should authors keep in mind when submitting their manuscripts to journals, like your repeated journals, to avoid inefficiencies and have better manuscript quality? Great. I think when an author is considering a journal to which to submit his or her manuscript, I think the person should try to get a good sense of what that journal publishes. What's sort of the scope of the article types that they publish? What type of articles do they publish? And can that author see their manuscript fitting in to that journal? Does it fit? So that's the first thing. And then I think an author really needs to look at the instructions for authors carefully. This can definitely be a daunting task because instructions for authors, including our own, can be quite lengthy. So they can be quite dense and difficult to get through. But if the journal has some sort of checklist that accompanies it, that can often be helpful. But it is worthwhile to maybe read through those instructions and maybe make some notes because that will just help you create a cleaner manuscript and one that's more apt to get through that initial screen. So I think that's what people are really looking for at the outset. By people, I mean ourselves in the editorial office, we're looking for, at the very basic level, does it have a cover letter? Does it have a title page? Does it have all of those big elements that we've asked for in the cover letter? And then it's the editor's job to determine whether or not he or she thinks that the manuscript is within the scope of the journal. But I think authors can help themselves by having knowledge of that journal and getting an understanding of what they publish and getting a feel for it and then accompanying that with a good review of the instructions for authors. And I think that can be very helpful.


Hello everyone, welcome to the NPTEL course on Remote Sensing and GIS for Rural Development. This is week 9, lecture 1. This is an important course for understanding or getting an introduction to the potential of remote sensing data for rural development. Initially, we had looked at remote sensing applications for rural development by defining what is rural development and then we had gone through each and every specifics of the remote sensing and GIS aspects. Now, we have jumped into the application part. Let us do a recap of week 8 and then see how it is linked to week 9. In week 8, we had discussed LULC where LU is the land use and LC is a land cover. We define land cover as the cover on the bare skin of the earth. What is on top of the earth, which is the bare like rocks and lithology and top of it, what is there, that is the land cover. Suppose there is nothing else, barren land is also used as land cover in some classification methods. Then we looked at land use, where you have a land cover, how is it being used? For example, one may have a forest and converted to a conservation area. Another person can convert it into a school, monastery, etc. And the last person can convert it to a resort as usual. So, there is one land cover, but the land use is different. And then the last point was we discussed land use, land cover change. How does it change for a particular time period? Let us say decade, 10 years once, 5 years once, etc. This is important because we need to understand what is the land and resources available for rural development. And since it's a large scale and a large time span, you cannot keep collecting data, rather you would be using remote sensing and GIS data. Then we looked at how LULC contributes to water and land management. We looked at what are the data issues specifically for estimating land use and land cover and then especially the change. As I explained, the spatial coverage is big and temporal analysis is there. There are two types of temporal analysis. One is over a period of 10 years and it's not once every year, right? You have to take during the seasons. There's rubby, curry, zyth. So, at least three times a year you take and then 10 years to look at some quantitative data for assessing LULC change. So, data is expensive and there are a lot of issues. And then we looked at a lot of proxy data that can be used for LULC. Some sources were given of which the ISRO's Indian government's database was used and we showed a lot of maps, a lot of attributes and how to use it, how to document the land use and cover and add overlay options. We also looked at on-the-fly remote sensing data for LULC using Google Earth Probe. So, we did a couple of things in one time frame like we looked at the Boone's 2015-2016, we looked at USGS 2015-2016, which is the NASA's and we also looked at Google Earth's 2015-2016, which is a college of data from Sentinel, ESA, which is European Space Agency and NASA. So, so much of data. What we have done is we have compared and then beautifully we saw that how the forest has been converted to rubber plantation in one part of India. And then the other part we saw the Ganges water, a lot of erosion, land being submerged and the land was not treated properly because of extensive agricultural activities. So, now we are continuing week line as I promised, we will jump into the hands-on tutorial this week. In the next class, we will add tutorials so that you can download a particular data set and do a very minimal land use, land cover classification. Again, these are like master's projects 10 years ago. Now, because of the clicker button and quick download of data, you can do it within 30 minutes, right? So, we'll show you how to do it and then with good accuracy and stuff. You jump back and forth between multiple data sets. So, in this course, I'm not using one, only one single data set. We jump back and forth to different data sets because the beauty of addressing these complex issues is you have access to multi-source. And multi-dimensional data, you just don't trust only on observation data. You don't just trust only on satellite data, we merge them together and then somewhere we make a data product. So, in week nine, what we'll be doing is we'll look at the types of LULC classification. We now know that land use, land cover classification is very important. However, we don't know like what are the different types. So, let's go through the types. And depending on your time, your data availability and cost, you can pick and choose which scheme you want to do. I will mostly focus on open-source data, open-source software so that you can do it and everyone can do it just with access of internet and a computer. So, this is what we call as giving everyone equal advantage. Initially, those who had the software only could make these maps. But now through this NPTEL course, we are propagating into the area that everyone can map. Everyone can contribute to remote sensing and GIS analysis, especially for rural development. So, as I said, in this week, we'll have one hands-on land use, land cover tutorial. Please go through it carefully and then do it yourself. One recommendation is please first do what was discussed in class and then you can do extra data and analysis. Always, this is the way you should learn. You should first reproduce what is produced in class because we spend a lot of time. We make sure that the data is correct. We make sure that the steps are correct. So, please reproduce our results. Then you can do some, then now you know how to do it and you will be able to do it by yourself. Issues in water availability for crop irrigation, we will discuss a little bit, especially remote sensing for abhi irrigation. As I said, one of the dominant features in this course is that it is for rural development. And rural development is around mostly agriculture. India is still an agrarian nation. Most of the population depends on agriculture for livelihood. So, if we give them good access to improve the agriculture productivity, lessen the losses, then rural development will occur. So, on that note, we will look at some issues and availability of data and then how remote sensing can help. Remote sensing for crop growth, acreage, help and some indicator like NDVI we will use. We will look at moving NDVI and then we will also look at the new need directions, future research directions that are needed to identify crop type, crop acreage and crop yield. Slowly, we will be getting more into applications because the tools are now introduced to you. It's the basic tools that we will be using. But what kind of applications we can do, what kind of research we can do is of utmost importance. So, let's discuss the first types of land use, land cover classification. Mostly, there are three types, depends on which book or reference you use, but mostly it is three types. In some books that we read just two types, which is unsupervised classification and supervised classification, we will be teaching the three types. So, unsupervised means no classification details are there, which means you have an imagery and now you have to classify. Let's draw an example. You have a four by four, two rows, two rows, two rows and two columns, four pixels. So, suppose you have four pixels, this is the image and now you have to classify each pixel, each grid. So, this is what we are going to look at, how do you classify? So, the basic methodology can be divided into three types. One is unsupervised where you do not know what green is. What yellow is, what blue is, what red is. We don't know. Blue could be ocean, river, lake or even in Mumbai, if you fly during the monsoon, you will see half of Mumbai having blue sheets on top. It's not water. What is it? It is a tarp that prevents water from entering the house. So, most of the houses, even IIT Bombay, we have a lot of these blue sheets on top. So, these have to be not marked as water. It's not water, correct? So, then we have the green color, it could be golf course, it could be an irrigation field, sugarcane or it can also be a park. So, all these are not the same for agricultural development and demand, you need to identify what it is. So, what is the step is, as the diagram says, there is generate ISO clusters, which means ISO clusters is same color. ISO is same. So, depending on the color, you will differentiate it. So, now I've given four colors. Suppose my computer can only understand the four colors, then it will say this is green, blue, yellow, red. But it doesn't know what these are, because I'm not telling it and the computer cannot miraculously find it as green as vegetation or not. So, this is unsupervised classification. No classification detail is available. I do not know what green is. Green could be, okay, let's say green is plant, but what plant is classification? Please remember in the previous class, where I shared that there is multiple greens and one is broadleaf trees. One is pine trees, one is plantation, one is rubber plantation. So, all these are green, okay, but you should tell, like, which green exactly represents this deers forest and rubber plantation. So, then pure clustering approach. So, ISO same colors are clustered by the computer. Once you click the tool for this, okay, what it does is the computer, it will take all the green of a particular wavelength, a particular color. It's not green means just green, there are at least 200, 300 shades of green. So, it will take each shade and then club it or it will tell you the histogram, the frequency of colors. And based on your telling the computer how many classes you want, it will give you four. So, let's say here I say four, it will give me four classifications. If I say three, what it will do is because every color is a function of the primary colors, it will mix and match the pixel and then give you three, suppose two, black and white, okay. So, pure clustering approach is applied by the computer. It will take all the greens and cluster it into one group, take all the blues, cluster it into one group and then give you the output. Properties labels are superimposed. So, the labels we don't know, once the color comes, then we think about it and say, oh, there cannot be, for example, as I said, the Bombay or Mumbai sky view application, we definitely know that it is not water. Then I'll say, okay, that blue is tap because that is my field experience. A personal experience from the field you can share. Same thing, I work a lot in Tamil Nadu and Maharashtra, the Ganges regions. I know, for example, in a particular area, there cannot be blue suddenly. It cannot be just water suddenly, it could be a farm pond. Same thing, the green color, extra green color that comes cannot be in Chennai, it cannot be agriculture, it is mostly golf or a park. So, moving on, we have the next part is the unsupervised classification is done. Now, we'll go to the next part, which is, let me bring my pointer, unsupervised classification. So, in supervised classification, we give the labeled input data. For example, I say these greens club it together and call it agriculture, especially paddy. So, once I tell that these greens club together, all these greens will be clubbed together and they will be used as green for paddy in the classification. So, what will be the output? The output will be a generation of clusters with some labels, which are the same as I have given. So, suppose I have given five labels, only those five labels will come, not more than that. Sometimes, it will be less because suppose that color is not available, it won't come. For example, you have water river flowing, I give blue for the classification and classifying, classifying, suddenly the non monsoon summer peak summertime, there is no water in the river. So, it will be brown. So, there will be no water in that class. So, you can always be below the number of classifications, it's not above. So, here as I said, we start with create a training set, which is I will say green is equal to agriculture, blue is equal to water, red is equal to damage crop. So, I create that bank or training set, then I give it to the computer or the program GIS program QGIS and then develop the signature file. So, this is called the signature file or a file that has what color represents what. See, in the first aspect, I'm surprised that is not there because we don't know. We don't know the labels, we don't know the colorings, so we did not give it. But in the second step, we will give the color plus the name as a code and give it to the computer, which is developing signature file. Now, using that file, the computer will run the classification. Basically, all the greens, it will cluster and then go to this file, the signature file and say what is the green? It is agriculture, it will label it agriculture, very, very accurately. So, this is uses label input data, generates cluster with same labels. One example is you are able to give the coloring by yourself to the computer or you can go to the image and then say I know that this is barren urban land in the barren class under the barren classification. So, what you could say is you can take this color. So, what you are doing? You are saying I'm creating a training sample, I am taking this color. So, within that box, whatever color comes, all the colors with that small box, the average color and the wavelengths will be used as urban and for that urban give red color. So, now the paint is going to be applied, all those black and white you could see which is roads or a urban cement coverage, all that will be classified as urban and then coloring will be given and 278. So, number of pixels having that color is 278, everything will be color coded. So, this is suprides. Now, the semi suprides comes where there is a mix of labels and unlabeled classes. For example, you don't know all the pixels color in your image, you know sure that okay, I know in Chennai, this is cement road, this is barren land, this is a golf field, but I don't know some green colors because Chennai, Bangalore, Mumbai still have a lot of trees. So, the tree could be a green or it could be a paint that is applied on top of a rooftop. So, what will happen is you will apply some labels and some labels you will not touch, let the computer run and give it to you as a cluster unlabeled cluster and you will label it. So, this is mix of labels and unlabeled classes is semi suprides. So, now the question comes is which one is more accurate? Yes, the more accurate one will be the suprides version. However, it is also time consuming because you need to have high level of confidence in your data, collect the data for all the labels and give the computer. There is also cost involved, you have to go collect. So, the collecting cost, the training cost, everything is there. So, all this has to be input. So, always the most expensive is a high accurate in this case. Then the semi suprides is good also because at least half of the labels is correct. The others are not, but once the clusters happen, then you will apply it or go to the field and then correct it and say, okay, this is not plantation, it is forest or suppose it is a forest, it is a rubber. So, there is a difference between all these three types of classification, which one is accurate is more suprides classification. But it also means that all the classes have to be labeled and your computer may take longer time for running the analysis. However, in rural development scenarios and real life applications, you do not have to be very, very accurate because accurate data takes time and cost. So, some errors are okay. So, that is why you see a lot of papers, scientific articles coming with semi suprides classification and unsupervised classification. You could say that semi suprides is part of suprides where some labels are missing. So, it is data gaps. So, that is why in some books, it is only two types unsupervised and suprised. In the suprides, there is semi suprides where not all labels are known, only some labels are known. So, let us look at a schematic of the land use, land cover types. So, types of LULC classification, we have unsupervised, suprised and semi suprised in hyperspectral data, which is multiple spectral, very, very fine wavelengths. So, for example, a green will be divided into 200 greens, that is hyperspectral. And you have unsupervised and then you run the unsupervised tool, it comes clusters. So, that is one classification. In the supervised, you give abundant references, references are labels, you let the computer run and give you the clusters, then you apply the labels or you apply the labels and then it runs it, then you have a supervised classification with labels that is called supervised. In the first part, you do not know the labels. So, you will have to figure out what the label is after the clustering. Here, the clustering comes with labels. In the semi suprised, few references are there. So, half of the labels are being populated, whereas half are not populated, there will be some errors. So, this may be full of errors, this may be least of errors, this is the least least of errors because it is all with labels, it is more expensive. I would say unsupervised is not errors, I would say it as no labels. So, data gaps. So, you will have to put the data later and then say what class it is. Let us look at some examples. In this study, we see that Ismail has taken some, so the grid is there, the image is there, the image has been gridded each pixel and each pixel you do not take ground clothing. So, this is the references. As I said, you have to go to the field, collect spectral signatures. So, they will go to that single grid in the center they will stand and then in their phone or an app, they will say what is present in that box. So, in that grid, they know the size 30 by 30 meters, for example. So, they will go to the 30 by 30 meter grid after they print this. First step is they print this image, grid it and then go to the same location using the map, stand in the center of the grid and then like my pointer is in the center and then take a note of what color and what is there. For example, that is green color, I will say green is some type of vegetation which is vegetables. Let us say that is vegetables and here is a forest. So, they are growing vegetables on the side because the color is not the same. You have the dark green and then you have light green there, some type of cropping. Then you take number 20, it is on the border. Number 28 is more urban. You could see that it is urban land. So, this is how you randomize the grids. It is not a particular pattern. You cannot say that one after two, another one and then after two, another one. No. It is random based on the spectral signatures. Based on your data, you have to say how many samples you need to collect. Suppose this is only black and white and you just have two colors, you just go to the most black and then most white, take two samples and then somewhere in the gray three third sample you are done. But here there is multiple colors and within the colors there are different shades. So, you will have to pick each shade, pick each color. So, just look at how many samples there has been taken. The best is taking each grid a sample, but then it is too expensive, time consuming, it is not needed. That is why you have a computer to do the classification. So, you would take representative samples. For example, this grid will be giving the colors of all the sides. This 8, 9 and 14 can give colors of all this forest. You can take duplicates also. So, 8 and 9 would be the same, but you need multiple samples. You could clearly see in this forest, they have taken a center value, they have taken a middle value and then the boundary values to document the forest. So, using this data, they have, this study has done a supervised classification which you see on the left and also ran an unsupervised classification which you see on the right. They have been very particular about the number of classes, only five classes have been given of which vegetation. So, vegetation could include forest, crops, vegetables, anything. All green plants are vegetation. Even golf comes somewhere under as vegetation because you put in the golf field. Then you have urban buildings, grassland, water bodies and then barren. So, the grassland can include natural grass or artificial turf or golf. So, you have all these different classes and what has happened here is you could see that your unsupervised classification is almost perfect. It has classified all the colors together. It did not have the labels. So, this label would not come in this output, but looking at this and going to the field quickly, you can put the labels. So, for example, now you know all the green. You just go to one green.  Just go to one of the block, write it as vegetation, go to one of the pink and then write it as one pink is representative of all the pinks. You say that is urban, you can take another sample if you want, grassland, water bodies and barren. So, this is the beauty of using supervised and unsupervised classification. There is, so this is much, much more perfect. You could see that more grasses are here, some more green is there or the blue is bigger, etc. But this can also be helpful. Overall, the errors are very, very small when you convert this into soil management, water management, agricultural productivity, all these things. So, where does this data come from? You go to the field and I have also taught you a trick that you could use in your classes, which is your Google Earth. So, if you go to the same image satellite, go in, zoom in and just look at it over a time frame, you will know that is that green and agricultural green. See, this image was a one-time snapshot. One-time snapshot is difficult to understand what it is.  Oh, then you know it is an agricultural field. Same way, if it does not change, it is a forest. And if it is well maintained, it is a golf. So, all these things you can kind of assume by going to Google Earth and extracting the labels. So, in the advanced level, we have multiple, multiple classifications. Let me go through some of them as per this paper. You have different types of techniques in LULC classification. There are manual classifications where you go to the field and draw it on a map like the Britishers did, that is one type. There is a numerical and digital technologies, which is GIS, hybrid technologies, which is both manual plus digital. And then there is classification methods based on indicators. This is also GIS. All the right-hand side block is GIS. We will leave it out for now. We will only look at the advanced levels. So, in the advanced techniques, you have artificial intelligence in LULC. So, those people who like to have big data AI, ML in their profile, GIS can give you that because all the data that comes into from remote sensing is really, really at a high, high volume and frequency. So, you get really good data at a bigger frequency. So, then you have hard classification and soft classification. Soft is, it is empirical, it runs through models, whereas hard classification is traditional machine learning, unsupervised, semi-supervised and supervised, which we discussed in the previous slide. And then the recent machine learning trends and the basic KDD or KAK, knowledge discovery approaches. You have knowledge-based classification, rule-based classification, data-driven products, reinforcement learning approaches and ensemble of methods. Ensemble is combining all the methods. In the recent machine learning, there are multiple, multiple developments, where hard classification is done by giving some input. It is kind of a semi-supervised input and the computer is smart enough, it learns from the data, it trains the data and then projects it. So, as a case study, I will discuss one paper from our group, which got published in this journal, Journal of Agrometrology. It is an Indian journal mostly through the association of agri-metrologist. Almost all agriculture universities are subscribing to this because it is a government journal, association's journal. And you could see that it talks a lot on the application. And one key application is the GIS application. And the paper we will be discussing just quickly on the advancement of data is the village-level identification of sugarcane and sangly Maharashtra using open source data. Why sugarcane in Maharashtra? Sugarcane belts are there in Maharashtra. A lot of land is under sugarcane cultivation, especially in sangly. And a lot of farmer depths, suicides are linked to these kind of crop management issues in this area. And so we would like to give some input. So, if you know the yield, if you know the acreage, you can manage the water, you can manage the soil resources and reduce the burden on the farmers by reducing the burden on the farmers, you reduce suicide. Farmer suicides happen because their crop is lost, they put a lot of money in the crop and the yield is not coming. So now if we tell them that the crop is not growing correctly, the acreage is not, the yield is not going to come, they will stop investing. They can at least save a lot of money and they can definitely work the next year and then come out. But if they don't and then they skip on pouring money in terms of fertilizers, water, labor and not get the money back, then they are pushed to the brink of debt. So that is what was one of the biggest reasons farmer depths for committing suicide. So in this study what has happened is we had a need statement. We wanted to map the farmers data using open source software, open source data so that everyone can run it. And there are a lot of advancements in classification. So we wanted to take AI and ML methods and then see which one is best. So we had census data for accuracy assessment, census means census from the ground and also the field data where we did field visits and collected data. And then we did two types of satellite data, one is Landsat 8 and Sentinel. Why? Because both of them have higher spatial and temporal resolutions compared to the Indian satellites. So we have used them and then only the basic bands were used. Some indicators were made, the classifications were done based on three semi-supervised, supervised classification which is classification and regression tree, support vector machine and random forest. So in this, this is not a traditional method. As I said, these are AI and ML based models. So it doesn't come under the semi-supervised but you have to give labels because you give a lot of data to the computer. You give a lot of labels to the computer. It will make the signature files and then classify. So at the end of the day, the sugar cane pixel was classified. And what we found out that there are multiple models that we use, CART, RF and SVM. So as I said, random forest as support vector machine. But we use also different datasets. So we use two datasets for each algorithm, CART, RF and SVM. And we could see that everything is different. So CART is not the same for Landsat data and Sentinel. Same Landsat 8 for CART and RF is not the same. You could see both are horizontally they are different, vertically also they are different. And the green is sugar cane that we are trying to look at. Grape is also there, some regions in sangly. Sangly is mostly sugar cane belt, lot of sugar cane factories. So what happened is when we looked into the results, we saw that accuracy. So this is where I said we went and collected. So it's mostly like a surprise classification. When we did the accuracy assessment, the type of model used also plays a role and the data that fed into the model also plays a role. So you could see the overall accuracy 78% was high using Sentinel to support vector machine. But support vector machine as an algorithm with Landsat 8 was not good. So 78% was the overall highest accuracy. And the producer's accuracy is there, user accuracy is there. So this model, this paper that I've shared is open source. Please read it and then see how this model has been created. It is an open source system in QGIS. Anyone can run it. It's an advanced level. But I want to introduce the advanced level now so that you could learn some of these techniques and then apply it for your research. So here we can also see that the spectral signatures and the enhanced vegetation index, which is based on the bands, is not the same spatially and temporally. So temporally it differs because the growth period of sugar cane is different. The index is at higher value in November, October November period. And then it is the same. So if you take a sample here, it is almost the same. You cannot differentiate between sugar cane, wheat, grapes and banana. But here you can clearly see that the wheat is differentiated. Then the grapes are differentiated. The sugar cane is much, much less. And in this, so now if I apply a filter in this two months or saying that only 0.2 I want or only zero, only EVIs, zero level I want, all the other crops will be gone because this is 0.8. This is 0.6. This is negative 0.8 banana. So only the green color will remain. Now that is how classification happens. So all the other pixels are gone. First the green is given as a value. This is what AI and ML does. So you could see here beautifully that the best model was, as per the accuracies we had, we had both SVM, the support vector machine model highly accurate. And we had going back to the table is that we had better accuracies for Sentinel-2 and Landsat. So you could see that Sentinel-2, this one was more accurate. And in this box, you could see that some browns are creeping in barren, but actually they are sugar cane. So this is very, very important. So a barren land does not require water, fertilizer, subsidies. So the government will think that why should I give subsidy to farmers. However, the farmers are giving sugar cane in this parcel. So if you do a small parcel, it is very small, but then if you accumulate all these data, it is a big data set. So this is where advanced remote sensing techniques can help in accurately monitoring and mapping the data for land use land classification. The other aspects of land use land classification is also forest cover, biodiversity mapping, and then species mapping, etc. All these are also done by a government of India's biodiversity information system website. I've given the website. It is also having tertiary links to rural development because forest and the areas around the forest are mostly rural. And rural communities have a very, very important engagement with the forest in terms of livelihood options, water resources, soil nutrients, medicine, herbs, everything they depend on forest tribals, I'm saying. So this is also a part of the land use land cover. And those information can be taken from here. For this, I would like to conclude today's lecture. I will see you in the next lecture with a hands-on tutorial. Thank you.


Hey everyone, it's Mike Kramer of Mock Capital with a weekly check-in here before the big FOMC and ECB announcements later this week FOMC obviously Wednesday afternoon ECB Thursday morning, so a lot of stuff to sort of digest and think about as we go through the balance of this week And what it potentially means for the markets We'll start off with the with the odds for a rate hike here market clearly giving the Fed a green light 87% chance of a rate hike tomorrow. This number has been changing a lot lately Odds have been all over the place not so much for May But really what we're looking at and thinking about for the market for June and it's not really surprising I guess that the odds are basically at 0% the market's basically saying it has no clue what the Fed's next move is here because You can see that we had been Risely rising pretty sharply this was obviously the SVB scenario and you've had you know since then clearly markets pricing in more rate hikes coming Potentially one coming in June and then today you had you know the weakness obviously over the weekend with first Republic More weakness in the regional banks today plus the jolts number came in Lower than expected and that basically, you know put push those odds Lower again and the number that pal likes to look at quite a bit is this ratio of Jobs that the total job openings divided by the unemployment the number of people unemployed and That number right now that ratio is about 1.64. So basically about 9.5 million job openings About 6 million unemployed people gives you that ratio and you can see that despite the number coming down There's still a long long way to go For the Fed before it gets to where it needs to be in order to say that You know its mission has been accomplished in terms of you know rebalancing the labor market The other thing obviously ISM prices paid index prices paid component for the manufacturing PMI actually ticked higher when it was released yesterday and That's not really all that unexpected given the fact that we've seen Copper prices tick higher in recent months copper and the ISM prices paid index tend to go hand-in-hand Although usually the prices paid index trials copper by just a little bit So not surprising to see the prices paid index finally beginning to move up And that's going to also put a little emphasis on tomorrow's services PMI prices paid index because that will also be coming out and believe it or not copper has a pretty strong correlation with The services index as well and you can see again Potentially suggesting a little bit of an uptick inflation is due to come for the services index as well. And so You know whether or not you believe the Fed's job is done another question, but certainly It seems and appears to be that the Fed is Unlikely to go through this process of cutting rates as you've been seeing in the Fed fund futures At least if you sort of start thinking about how much work is still left For the the job market and especially, you know, how much how high inflation remains So you can see right now market pricing in about 60 basis point in rate cuts following tomorrow's rate hike And so again, this is sort of where we are and it's really the great Question in terms of where the markets will go next there's been a lot of volatility in the bond markets You can see that we had a very big move up yesterday in rates rising 14 basis points today falling 18 basis points the Fed doesn't want this type of volatility in interest rates It's not really healthy for any market, you know up seven basis points and down not ten basis points in the ten year Then up 15 basis points down 15 basis points the next day and so this type of volatility is not really what the Fed desires and it's really gonna have to message To the market again what its intentions are you can see that, you know Again, if we look at the S&P, here's the you know, here's the 80 day cycle We've been talking about it's been working very well for many many months now well over a year it's currently in jeopardy of No longer working at this point based on this we should have seen a high already And you know, you can argue whether or not that's been made to me This looks just like a giant consolidation phase again If this is going to be a point in time where you're going to see the market move lower It needs to happen soon because otherwise you can say that this 80 day cycle is completely over And there are other reasons why we may think and continue to believe that the S&P 500 is sort of trapped up here We still know the call wall is still around 4,200 That's going to keep the S&P 500 contained and probably not really moving up significantly higher Unless we see that call wall shifting higher from an option standpoint Additionally from an implied volatility standpoint We've seen the spread between the spot VIX and the three-month future contract VIX declined to a low of Around seven just on April 28th, and you can see now we rallied back to around negative five Typically when this spread gets below negative five particularly when it gets towards negative six to negative seven It typically coincides with a peak in the S&P 500 So again from that standpoint if we've seen the low for the VIX for this current 80-day cycle Then there's reason to believe that if implied volatility starts moving up on the front of the curve There's a chance that you could continue to see the VIX begin to move higher And the S&P 500 begin to move down. Here's the NDX. You can see we closed the gap here 13,245 was the high from was the gap it on August 19th You can see we got briefly above it came back down and now we're moving lower again today So again this level here 13, you know This this is the resistance level around 13 to 40 or so very important Obviously as we've been talking about the NASDAQ takes this level out closes above it stays above it for a day or two You know your next likely area comes 13,750 which would be the August 16th highs again, if you break down below This 12,800 region gonna be a very bad sign for the NASDAQ basically the recent breakout that took so much energy To get this index to break out has failed and that's going to signal Lower prices are coming in again the trend in the RSI continues to point to momentum fading and momentum leaving the NASDAQ 100 when you look at the Dow got up to around third got up to 34,300 call it got up there This is a big resistance zone so far we failed so far we came back down You could see it appears we're forming some sort of broadening wedge pattern This is again a key level here for the third for the Dow Jones 34,150


So you should be right to go Rebecca. Okay, thanks Jerry. Excellent, we can hear you. Thank you. Thanks very much Hans-Yoget. It's kind of hard to follow your presentation about such very clear practical training in data management with what I'm about to talk about. We are at a point at Swinburne and we were when we did this particular session where we had a research repository and the ability to describe and promote data sets and send them to Research Data Australia, but we didn't have data storage and we only had a draft data management policy. It's actually very difficult to enforce a data management policy without data storage and it's hard to encourage good data management practices without a policy. So part of the purpose of this session was to discuss with researchers what they would like the policy to look like, what would be practical for them. So the three presenters were me. I'm the research services librarian at Swinburne so I look after all of the services that we provide to researchers from the research repository for publications through your data, copyright advice, all those kinds of things. We also had Terence Bennett who is technically employed by the College of New Jersey in the US but he was on succumbent to us as a research data librarian. And we also had Sonia Petal, a research fellow from the Swinburne Faculty of ICT and the Dean was present as well. The first thing I'd like to point out is that the reason I've been asked to speak to you today is the difference in our presentation that we had a researcher talking with us as well. But Sonia is a researcher with a research interest in data management and policy modelling. So I wouldn't say that the kind of presentation that we did would work with every researcher or any researcher. Sonia's research is also really multidisciplinary so she was able to give examples right through from anthropology to computer science to quite hardcore data and that meant that everyone there was covered no matter which discipline they came from. The reason I want to talk about this style of presentation is that I think researchers opened up far more because we had a researcher stand up and honestly say this is really hard to do and far from perfect. From experience when I've encouraged researchers to openly evaluate library research services like the repository they've been really reticent to criticise anything in front of me in case they hurt my feelings and they probably would if they said anything really mean. But having Sonia facilitating the discussion definitely made a difference to getting people to open up and making it more interactive. This is just a quick discussion about what an agent-based modeler does just in case not everyone knows. What they like to do is they like to model systems and how people interact with them and how policies grow around systems and how these three things interact. The significance of the way that we ran our training workshop slash sessions slash free for all at Swinburne is that it was a workshop not training as we know it. We probably asked more questions than we answered because we didn't have the storage and the policy and everything to point to and say this is how you've got to go about it. We just wanted people to start thinking about how they would look after their research data. We were enabling discussion but probably not really dominating it. We also went with the approach of facilitating rather than leading. So this was a user-centred discussion about benefits. The purpose was, as I mentioned before, to receive input about how we might design a data policy that suited researchers. I think it's unusual perhaps for researchers to contribute to how we think about data policy. I think a lot of that sort of thing is worked out by support areas like the library and IT and research and maybe even the intellectual property office. But it was a great opportunity to say if we designed a data management policy that you needed to comply to, how would you like it to look? So we asked researchers to think a lot about the big questions. We're still right back at that point and to tell us what was getting in their way. It was a two-hour workshop with cupcakes, which was probably the most popular part. I've included a few slides in this presentation that came from our training session so you can see the kinds of things that we discussed with researchers. So we discussed the value of data management in terms of sharing. So sharing with yourself in the future as Anne likes to put it or sharing with another. We also talked about how we can promote research through the repository because that was something we were actively, practically able to do at the time. We asked this question of researchers and a lot of them looked a little bit guilty. When did you last see a computer with a floppy disk drive? Well, I haven't got one and I was willing to stand up and say, and I've got stuff that's sitting on a floppy disk drive that I'll probably never be able to retrieve. And I think that relaxed people a bit. The value of having a researcher in the room was that we could actually give some real-world concrete examples of what data looks like and how we might need to manage it and what the differences between kinds of data are for access and ownership and even the ability to share. So this was one of Sonya's slides about the kinds of data she had collected through one of her projects. It was everything from quite rudimentary things like the BARC reports that have to go in with the project and keeping an eye on the budget and that sort of thing, right down to analysed data, photographs which could never be shared because they were children, server log files, which nobody feels particularly sad about if they are public, but just the breadth of the kinds of different data really opened people's eyes a little bit to what it was that we were thinking about when we talked about research data. We didn't specify who the audience should be. We talked when we promoted the event about getting people along who were interested in data management policy and how we might go about developing one. We did get lots of HDR students, lots of PhD candidates and lots of early career researchers. The project officer in the research office who at the time was drafting the data management policy also came along which was really good because she could be there and hear in person what people's pain points were. But because of the audience that we attracted we decided to choose to focus on promoting data as early in your career we went with the idea that all promotion is good promotion and the ability to promote your research through sharing your research data has to be beneficial and that seemed to work quite well with people. We asked them if they were sharing their data and how they were sharing their data and what we could do to make it easier for them to share their data and we did find out that there are quite a lot of wounds quite close to the surface which was a little bit of a shock for us. We knew it wasn't easy but we didn't know quite as much detail as we found out from this workshop. We didn't for example know about some of the competing interests at work so we realised that there's a really vast literature on data sharing and the impediments to that particularly in certain disciplines. But we hadn't thought so much about what the relationship between sharing data and accessing data is. So when we sat down and said why would you share your data, how would you share your data some of the questions we got were I can't access the data I want, why would I share my data and they gave quite concrete examples of data sets that were held at other universities and actively not shared with them either by email or through a repository. And that was a discussion that was quite hard to move past. They weren't making a distinction between accessing data including paid data and sharing their own it was all part of the continuum to them which was interesting because it's not really the way that our experience with making research data sets available through ANS had told us. Data was also seen as a commodity by many especially the engineering researchers who were present so they were interested in commercial data that they are couldn't get access to as mentioned before or was worth tens of thousands of dollars and they wanted to know why they would share their data freely if it was clearly of some monetary value to them. We were also perhaps not quite as aware as we are now of the issue of commercial and confidence data. So we realised because I come from a publication's repository background I've got that perspective already but we realised that one of the reasons that we sometimes have to embargo these is that there's confidential material in there that can't be shared because it could identify someone. But it never occurred to us that there's research that some of our academics are working on right now with industry that they can't even let their competitors know they're doing let alone sharing the data that's an outcome of that and that was an eye-opening experience for us that idea that they don't even want someone to know that it's happening. We made a point of emphasising the benefits of sharing data either with oneself in the future or with others and the idea that that ties into how you promote the research that's coming out of your work. Some of the PhD candidates were interested in the fact that we might want their data along with their theses. But we also found it helped to be realistic about the obstacles. There's no point pretending that it's easy to share data especially in a situation that I've described before where we had no policy or storage to support our discussion. But also that researchers really appreciated as demonstrating that we understood where they were coming from and that their concerns about sharing their data are legitimate. And we were able to change some perspectives just because we were prepared to talk about the negatives. So it could in the future be a great outcome for us as data trainers or people encouraging people to share data if we are able to address these issues in a public forum and ameliorate them. But we can only do it if we actually do say, well, you know, there are some reasons why you might want to share your data or you might want to restrict your data or those kinds of things. So we put up this slide in the presentation so that we could show that we were aware of some of the problems that, you know, there were time restrictions and the terrible issue of missing incentives as far as data. So it's very clear return on publications. You get points, but nobody rewards you for sharing your data. So we put this slide up merely to show that we understood. We found that tools were really helpful. It actually, in our situation where we don't have a lot of practical tools to help researchers, that having even one or two was really useful. So we have a very active supercomputing function here at Swinvern, which is available to the astrophysicists who run it, but also everyone else at the university. And that's quite long-standing and that's a really useful service. But there's not a lot of thinking at the university other than what's happening in the library about how to manage the data. Our data management checklist, we went through very briefly in the session and talked about how if you complete this checklist then you should be able to have the rudimentary elements required for a data management plan. Without a policy it's very difficult for us to enforce people making those plans, but it does at least allow them to think about the things that they need to think about if they want to look after their data in the future. And our checklist is influenced by Monash and QUT and available from our website. We found that a good example is gold. And especially a local example if possible. And luckily we had one and it just kind of came up in conversation, something that someone said made me think about it. We have this software evolution data set which is prepared by one of our researchers and he makes it available through a website for the project with clear details on how to cite the work and also under a Creative Commons license. So the reuse qualifications are very clear. We've cut a lot of this data set and it's now available through Research Data Australia. But it was nice to be able to show that someone at Swinburne had been able to do this and it wasn't so very hard. And that there was also something to be gained from it. The dean of ICT who was present at the time talked about how in particular in the computer science sphere you can tie the open data movement into other open movements like music and computer science where it was shown over many years that exposure was sometimes of more value than royalties. And that was a particularly good concluding point for those who'd been so concerned about the monetary value of their data. We might have been there to talk about the value of good data management and planning but we still had to be willing with the kind of session that we'd set up to change the tack of the discussion if it was going in a particular direction. So we had to adapt our presentation on the fly to take account of the audience being mostly early career researchers. So they weren't awarded research grants with the ARC and therefore it was difficult for us to say that they were as subject to the responsibility of good data management as say their professors. We would have had to think a bit differently if the senior professors had been present. So the draft data policy was prepared incorporating feedback from this session. It hasn't actually been ratified yet but we made a start. Terence has since returned to the US and Sonya's been teaching but she's returning to research next year. Swinburne has a metadata stores project at the moment and once we have working software installed it would be good to use this as a catalyst for having a follow up session with some more practical examples more like ANU is doing. At the moment the library research data page is the only one in the university that provides advice on where to go for data analysis support, software training in SPSS and R and other statistical software packages and also a list of data repositories in particular subject disciplines that might be useful and the checklist is available there as well to assist with planning. And my advice to you if you're starting out in data management training is talk to someone else who's tried it before you. They're my contact details. Are there any questions?