Hello, everyone. Bonjour tout le monde. Thank you, Sakiol. First, I want to again thank President Jung for his warm welcome this week. As two North Pacific nations, as partners and as friends, it's important for us to continue strengthening our relationship in this consequential moment for the world. Today, with President Jung, we talked about clean economy, security in the world, and other issues such as gender equality. It's an honor for me to be here to highlight the 60th anniversary of diplomatic relations between Canada and the Republic of Korea. As I said when I spoke to the National Assembly this morning, our two countries share unique economic, cultural, and historical relations. Our common history has, among other things, been marked by the Korean War. I'd like to hand it over to the Canadian people who fought during this war. Canada and Korea share deep and historic friendship. We also share dynamic ties between our people. Canada has the world's fourth largest Korean diaspora. We welcome thousands of Korean students to our universities every year, and now we want to do even more. Today, we're announcing a new youth mobility arrangement, which will result in more work and travel opportunities for young people in Canada and Korea. This will help young Canadians and Koreans acquire international experience while strengthening the ties between our people. Another thing we share is our desire to build a cleaner and more prosperous future for our countries. It's never been clearer that economic growth, climate action, and security and stability are all interrelated. Canada and Korea also have strong trade ties now, with eight years of free trade between our economies through the Canada-Korea Free Trade Agreement. President Yoon and I both agree that we must continue growing these ties to create even more business opportunities on both sides of the Pacific. Today, our two countries signed an agreement on cooperation in critical mineral supply chains, the clean energy transition, and energy security. This will strengthen and secure supply chains between Canada and Korea. For Canadians, it'll mean more investment, more trade, and more research and development in our country. Together, it'll mean benefiting our workers to develop clean and reliable solutions like next-generation electric vehicles to power our net-zero future. One of our key measures to fight climate change in Canada is our price on pollution. In 2021, at COP26, I launched the World War on Carbon Tarification. Today, I welcome President Yoon's agreement to join the Global Carbon Pricing Challenge. Korea is the first country in Asia to join the challenge, and this demonstrates leadership on climate change and energy security in the region and around the world. Of course, President Yoon and I have also talked about security in the region and the world. If we want to build a more prosperous future for our populations, we must continue to defend peace, human rights, and the international order based on rules. As two democracies, we continue to condemn the brutal war on Russia in Ukraine. Here in the region, Canada is committed to supporting the Republic of Korea's efforts towards a denuclearized, peaceful, and prosperous Korean peninsula. In March, Canada announced that we would extend Operation NEON until 2026. Our contribution to support the implementation of UN Security Council sanctions imposed against North Korea. We will also continue our work together to help organizations supporting human rights in North Korea. Stability in the Indo-Pacific and in the North Pacific is essential to global stability. We will certainly be discussing these issues and many others together at the G7 this week in Japan. Sukyeol, the friendship between our countries is a special friendship. This has been true for the past 60 years, and it will continue to be true for years and decades to come. So together, as friends, as allies, let us continue our work to create good jobs and a more prosperous and secure future for Canadians and Koreans. Thank you again for your warm welcome.


Doge City and in the territory on west there's just one way to handle the killers and the spoilers and that's with the US Marshall and the smell of gun smoke and the story of a man who moved with it. The United States Marshall, the first man they look for and the last they want to meet. It's a chancey job and it makes a man watchful and a little lonely you've been eating here a whole paid a cent of course not what do you mean of course not he ain't supposed to pay is he now you look I'm broken you are look at this place I ain't got enough customers to pay for the water that's the whole trouble you've been eating so that's that well now Hank I'll take over for a week how's that you take over what cooking yeah be some improvement what only one way to find out but you're not the one who has to see it the first good food we've had until Monica was in years he times a day for a week and it's getting better and better look at yours was a real fine night I'm gonna put him on pay after today and I'm gonna pay him well too are you sure the trailer is starting to arrive you're gonna get rich with him out in the kitchen Hank they say the word done clean detectives already I think there's a short change around here I hear you put a leg on him at night and chain him to the bed I think I'll start doing that very ready to leave Chester I don't know whether I can move or not maybe you better try Chester Hank's got other customers waiting oh that's all right Miss Kitty we have to go anyway and we'll be back this evening come early it's sundown good good good take care of him first yes I'll get somebody else it's about time what's the trouble I told you I wanted these eggs turned over oh so you take him back and you just be quick about ain't gonna set it all day you know I look good things if we've got to eat and you want a never mind what I ordered just take him away and bring him back done right here all right all right no take all they will bring co-focus I don't have enough work to do he's handy the man wants his eggs turned over I ain't got time you're better he's pretty mean about it I don't care how mean he is what do you order eggs for anyway we ain't serving breakfast he's half room maybe he thinks that's breakfast all right here we're taking back to one out no sandy now you just make him mad he wanted to cook more I turned them over like he asked it but you don't know the difference well maybe not but what if he does then tell him to go eat someplace else okay sonny but I don't think that you can pull him even if he is half drunk one steak with eggs in my face but you could have killed him with that skillet throw some water on him and get him out of here no wait a minute say goodbye to Hank Green for me and a natural born player for word artistry these are the prime assets of one of the great veterans of CBS News mr. Lowell Thomas Monday through Friday evenings Lowell Thomas offers the latest news in detail and depth plus his own witty seasoned comment on each story there's no substitute for experience indeed no substitute for Lowell Thomas when it comes to news in full sound color on CBS radio and this station and come Saturday night what your appetite for inside stories on show business with CBS radio's Mitch Miller show top names of show business beat a well-trodden path every weekend to Mitch Miller's lair you hear their plans their dreams the backstage shop talk that makes this weekly program fascinatingly informative remember on most of these same station Saturday the Mitch Miller show Hank Green's we're gonna miss him so everybody else and dodge for that matter well he's the best cook down on goes ever had maybe just an accident like that way or say it even so just three killed a man he's got a sand trial for it don't get my low can you still see the tracks now they lead toward that hill over there let's move what if we don't find him for dark I'll have to sleep on the prairie and pick up this trail in the morning so long the strongest out bunch of cattle I've saw him quite a spell gonna be much help to us it was a river we can swim across nothing to do but wait don't you can run the man maybe he saw Sandy hello boss of this outfit what are you doing out here Marshal looking for a man but your herd cut right across his trail maybe seen us coming done it on purpose smart enough well always like see man get away nothing personal Marshal understand how's the cattle market and dodge marshal holding up ain't no trail right for the fun of it well I better get on up ahead and tell him start milling the herd it'll be dark for long I don't like to push cattle after dark I appreciate you're not trying to cut through on Marshal we can wait they have I hear there's a good cooking dodge for change I'll come down the trail yesterday told us about it there was when he's still there he's the man we're after oh well the men were kind of looking forward to a good meal Marshal he killed a man pretty sorry to hear that I'll tell the man but ain't gonna like it Marshal I tell him I don't like it either yeah well so you think Sandy King's only good cook in Kansas and you know a better one well I don't get that do you think we lost his trail we ever got across there we might pick it up I have to make camp and wait for daylight we'll get some feed and rest and be on our way what do you want here I ain't gonna find you I don't be scared miss I ain't gonna hurt you close enough how come you put your horse in our barn I had to get him out of the sun didn't I you don't mind if I rest a minute for moving on I guess it's all right you don't need that shotgun must be getting kind of heavy I didn't know who was my name's Sandy what's yours I think that's Jayhorns my father if you heard of him it says a half is he around someplace the road note today we got a water hole up by a shale bluff oh and your mom I never knew my mom if he you got any cool water up the house yes we do could I have some if it's not too much bother I'm terrible thirsty I guess it would be all right I need no bother to it come on you know something I think I don't really need that drink of water you don't I was passing by through the trees there and I see you come out of the house I wanted to say hello that's all I wanted to meet you at the foot forward as you're the prettiest gal I ever seen you must know lots of girls no I'm a lonely man if he might have only man a drifter if he I guess I never found no reason to settle down but that's great for a person I mean everybody should have somebody don't you think I need always easy to find if you know what you ain't I've heard of it happening though just like that when you least expect it right out of a clear blue sky tell me about it all right I'll tell you about it if he come on in the house Sandy it's not what you think I wondered who's horse that wasn't a barn how long you know this boy we just met Paul I'd shoot you right here boy but I don't want to mess up the place I'll get outside you don't want to watch you stay in the house if you can't shoot a man for one kid that's all right if he you can't stop him nobody can stop me outside gonna kill him you don't know me Marshall you don't know nothing about me you mean he's wanted by the law a murder to save me for a hanging he's back in town five minutes and they were saying to he's a good cook also gives them a chance to shoot they're always wanting to do that I think it forced me to turn to Sandy Lewis they'd have a free hand of this because he killed a breaking most of mine are here Marshall I didn't expect to find a man like you taking part in a mob they tell me that cook face Marshall that's no reason to kill a man well a cook did hit him but they say he didn't mean to kill him you'll get a trial you're Marshall the men want him out back nobody can't get in there without us here for that shotgun well if there's gonna be a shooting I'm gonna get left out of it this is a part of the game you don't have to sit in on Chester all right I ain't got nothing to do you much I'm doing I am I'm just desert Marshall Texas Rangers can I come in come on in I just pulled into town what's a mob for trail has tried to show up the law what can I do for you I got a warrant here Marshall good man was headed toward dial let me see it you know him he's dead cousin dad this is an arrest warrant for Ed Fisher there's a reward Fisher I'll be done gone who cared that man I got locked up in jail for it Sandy King yeah that crowd's all about yeah there seems to be a popular man Marshall he's a good cook he's a good cook Chester go tell Sandy the news and let him out as soon as I break up this mob that's the man Sandy kill you made him break up yeah tell me something have this Sandy King kill Ed Fisher anyway you hit him on the head with an iron skillet that it was provoked I think the judge it'll let him off pretty easy you're going to a lot of trouble to bring him to trial weren't you wouldn't you have rhythm yeah yeah I would kind of thought so here is a hundred dollar award mister this was one that did her life I might have broke it's true but I'm afraid that camera money just wouldn't do me any good isn't thanks just the same whatever you say Marshall I'll be moving on I guess moving on yeah I've always had a hankering to see California Chester I guess this is as good a time as any over here everybody's expecting you back at Delmonico to cook up a whole batch of fancy vitals what do you think this whole hour about anyway tell them I'm sorry Chester I was just cooking to pay off a debt I don't want to make a living at it besides I hate cooking I'll be dog gone ain't you gonna stop him the men are gonna be awful man he's a free man Chester as that's how the men wanted it one of Kellogg's favorites Kellogg's all-brand going on 41 years now it's been America's most popular good food way to fight irregularity from lack of both because it's whole brand Kellogg's all-brand gentles away irregularity safely and reliably and because it's deep toasted for extra Christmas it never gets mushy and milk there's only one all-brand Kellogg's all-brand that's a double L hyphen BRAN Kellogg's all-brand directed in Hollywood by Norman McDonald SARS William Conrad as Matt Dillon U.S. Marshall the story was specially written for Gunsmoke by John Meston and adapted for radio by Frank Harris featured in the cast were Sam Edwards Vick Perrin John Daner Gene Bates Ben Wright Harry Bartel and Larry Dobkin Harley Bear is Chester Howard McNeer is Doc in Georgia Alice is Kitty this is George Walsh inviting you to join us again next week when CBS radio presents another story on Gunsmoke for you every weekday on the CBS radio network


few administrative tasks. For those of you who are joining us for the first time, we organize our webinars in a webinar format, so you are not able to show your camera or unmute yourself. However, we do encourage you to communicate with each other in the chat. If you have any questions, please use the Q&A function. Some of those will be answered during the webinar, and we'll hopefully have enough time to answer a few of those live after. If you have any technical difficulties, please reach out to myself or my colleague, Isabelle Verbeck, and we'd be happy to help you with that. Before digging into our spectroscopy material today, I'd like to provide just a little bit of background on the Global Soil Partnership and go so on. So the Global Soil Partnership, or the GSP, was founded in 2012 to work towards healthy soils and soil sustainable management for everyone. So this is done through a mechanism to bring all of the stakeholders together to collaborate on soil issues. It's done through a unique framework of exchanges of experience and discussion among seven regional soil partnerships, over 500 partner institutions, seven technical networks, and 161 national focal points from FAO members. So as some of you who've worked with us before know, we collaborate or work in conjunction with the ITPS, which is the Intergovernmental Technical Panel on Soils, a group of soil experts that provide scientific and technical advice to the GSP. And then every year we have our annual GSP Plenary Assembly, which is our decision-making body, where all of the representatives from our FAO members come together and our GSP partners work together to review and prioritize soil actions. So Glossalon stands for the Global Soil Laboratory Network, and it was established in 2017 in answer to a growing need for harmonized soil analytical data with the mission of building and strengthening laboratory capacity and soil analysis. So they began with wet chemistry, and then in 2020, in answer to demand from many of our partners for growing interest in soil spectroscopy or dry chemistry, the Glossalon Initiative on Soil Spectroscopy. In 2021 and 22, we've begun work on consolidating the capacity development and activities on soil spectroscopy, such as the series of webinars of which this is the 12th online video courses and additional training material, which is available on our website. And so today, we're very excited to have our speaker with us, Dr. Leonardo Ramirez Lopez. He's the head of data science department at Buki Laboratory in Switzerland, and over the last 15 years, he's conducted research on infrared spectroscopy all over the world, such as in Colombia, Brazil, Germany, Belgium, and Switzerland. In particular, he's devoted to share his developments through open source software, and some of you might actually be familiar with his work as he's developed two very powerful R packages that are widely used in soil spectroscopy prospector and re-symbol. So his main research motivation is to bring soil spectroscopy to a fully operational level, and we're thrilled to have him here today speaking on working towards operational large-scale soil libraries. And his colleague, Estefina Fernandez Perez, who is an NIR expert also at Buki will be joining us today as well. So without further ado, I would like to give the floor to Dr. Ramirez Lopez. Yeah, thank you very much. So I'm going to share my screen. I'm very excited to participate on this series of webinars. I have to share my screen first. Yeah, everything okay? Can you? Okay, excellent. We can see okay. Yes, good. So yeah, today I'm going to talk about, yeah, my point of view on how to make, how to benefit from soil spectror libraries, how to make, how to use them to make operational spectroscopy in the lab, okay? So yeah, this presentation is based on the work that we have done with many people, and especially with the co-authors of this presentation, okay? So this is the slide, the introductory slide. This is the original introductory slide. You see I put some flies there, but it's not my present because my presentation stinks. You will see that, I mean, why I put some flies there later on in the presentation. I really love insects. I was really passionate about entomology when I was doing my bachelors. And yeah, they are very interesting insects from the evolutionary point of view. And yeah, today I'm going to talk a little bit about evolutionary methods, and that's why I put them there. Okay, I will jump directly into this slide that basically introduces how spectroscopy, like spectroscopic models are usually developed and then deployed. I already made a presentation like two years ago or one year ago where I presented this, but I thought, yeah, I will present this again because it is how it's basically, it's the basis of what I'm going to talk later on how to make this operational, library operation. So the first thing that we usually do is to collect data. We spend a lot of time collecting data, okay, in most of the projects that involve the creation of a spectral library's data collection is a very long journey, but organizing the data is another very, very long journey. So I would say that in my experience, more than 50% of the time that you have to invest into developing a spectral library goes into organizing data, cleaning the data, harmonizing the data, and so on. So yeah, sometimes it's even 80% of the time that you have to invest into developing a spectral library. And then what we usually, then this step is followed by the calibration of spectral models, and we have, I think that the scientific community has become very good at it. So we have been able to gather a lot of information on how to calibrate and how to validate spectral models. So we are really, really good at it. However, and basically this is an evidence of this is that the amount of scientific publications that have been released over the past 20 years or 53 years. So, but then, yeah, the work shouldn't stop there, of course, we have to make those models operational, we have to deploy those models, we have to bring those models into what so-called production, right? And to do that, usually, ideally we should pack those models. For example, if we develop a model for total carbon, total nitrogen and cutting exchange capacity, we have to pack them into kind of an application or into a container that can be validated, I mean validated in terms of the accuracy of the application, and see what is the, maybe we define some acceptance criteria for the models that we, whether we accept the models for production or whether we accept the models for operational use or not. And then once we say, okay, let's accept the models, then we release the application so that it can be used for routine analysis. So, what I said before is that we have become really good at calculating models, but I think that steps for five and six, we are not yet into, let's say, into a knowledge level that allows us to quickly bring spectroscopy to an operational level. So, that's the reason what I'm gonna talk about, how to make them, how to make operational, how to make a spectroscopy operational at the laboratory. I think that I have to adjust my mic. If you give me a second, I'm gonna go to the settings, here, then I think here, sorry for that, see how can I, okay, maybe if I speak louder, then that would work. But if at some point you cannot hear me, then please let me know and then I will try to go back to the settings. And okay, one second. Okay, so again, so to recap. So, spectroscopy, we have become really good at building spectral models, but we have not been really, I mean, we are not yet there when it comes to deploying such models and to make, as well as spectroscopy operational. Another thing that I want to stress in this presentation is that, yeah, most of the papers that, in most of the papers about the spectroscopy, you read that it is a very cost efficient method. So, and yes, that is right, but it also depends. So, once the method is operational, yes, it is very cost efficient. But now when it comes to do the process, to build models, to build the spectral libraries, I mean, this process is very, very expensive and tedious. So, let's say the operational side of spectroscopy is very cost efficient, but the development side of soil spectroscopy is very expensive. So, I think that that is one of the reasons why labs find it hard to implement spectroscopy. So, they usually focus on routine analysis and they sometimes they don't have really time to invest into developing, say, like operational soil spectral models. So, because sometimes also for these users, for lab users, this involves a lot of many time consuming iterations to get these applications to an estate in which they can be used. And this creates, of course, frustration. So, they sometimes they don't want to invest towards that. I mean, because of the, maybe because of budget reasons. Another thing that this whole process involves is, involves a lot of, or requires a lot of knowledge about chemometrics. Our chemometrics is a complex subject which requires time and effort to be learned and taught as well. So, those things are really critical when it comes to implementation of soil spectroscopy and we have to take them into account. However, I think that with all the open source libraries that are already out there, that are available, that we can access and then we can use. I think that with all this information that we already have collected in the past, I think that we can build operational models for laboratories in the field. So, how we can benefit from spectral libraries? How we can make these soil spectroscopy, how can we make soil spectroscopy operational? That is the question that I mean, that we should try to answer. And we shouldn't really focus too much on the, say, calibration, model calibration side. Because we are, again, we are really good at it. We should focus on bringing spectroscopy to the laboratories for routine analysis. So, how we can benefit from it? So, imagine in a laboratory, the success of the implementation of a spectroscopy depends on many factors. It depends on the quality of the hardware that you are using. Usually, the quality of the hardware for all the instrumentation is more or less really good. Software data, of course, to a less extent in chemometrics, but it also depends a lot on automation. And also depends a lot on people. But today, I'm going to focus on the automation part. And I'm going to focus on the topic of library search methods. So, because I think that library search methods are critical to make spectroscopy, again, operation. So, and I'm going to talk about, more specifically, about evolutionary genetic search. So, this is a method that we have developed over the past couple of years. And it has proved to be quite useful when it comes to profiting from, from, sorry, the spectral libraries. So, just to give you an introduction of what I'm talking about, I mean, when it comes to genetic search, imagine that we have individuals, many individuals that we can call them as well, subsets, subsets of samples. But let's call them individuals. And then we have genes. The genes are basically, can be, can be the samples that we have in our spectral libraries. So, the goal in genetic search is to breed one or more individuals that are feed enough to be integrated or to serve a target population. So, the goal is that we have a lot of genes. Imagine, the genes are the samples, again, in our big spectral libraries. And the goal is to identify the best genes that are suitable to make, say, feed, the feedest individuals. So, okay, in the, we have, for breeding, we need to breed many offspring. So, the first one works more or less like this. So, to every individual, basically subset, we have to find a kind of a weakness score. So, we find how, we try to assess how every individual or every subset, how weak it is. So, and this score is assigned to every gene in the, of the individual population. So, then here I have a table that more or less tries to explain how, how this is done. So, imagine the first individual, again, is a subset. The first individual, for the first individual, you can assess how good the subset or this individual is. And then to all the genes that you have inside that individual, you assign, you label each gene with the, with the gene, sorry, with the, with the weakness score that you found for that individual. So, individual two, you do the same to your individual end. Okay. Note that every gene is not present in all the individuals. So, at the end, you can, if you, if you do this, at the end you can have a kind of a, a gene weakness score. Okay.  And then you can suppress all the genes that bring weakness to the individuals or to your subsets. So, yeah, that is the goal. Basically, suppress the weakest genes and breed then a new offspring with the fetus genes identified in the previous offspring. So, the idea is that you, you breed as many offspring, offspring as necessary until a gene pool is identified. And these gene pools should produce the fetus individuals. Okay. So, this is more or less how, how this type of search, genetic search is, is not. So, how can we use that in spectral libraries to understand, for example, target populations? I have to say that this, this work on genetic searches is inspired by this paper published in 2017. It is about artist local. So, what we did was we, we used the code of artist local, the code methods in artist local and then we adapt them to, to the evolutionary search theory. So, now to explain you in a more, let's say graphical or like more applied example, I'm gonna, I bring these, these, these two sets. Now, the first one, imagine that it is the reference spectra that you have in your soil spectral library on the left and then on the right, you have your say sample set of samples that come from a region or from a lab for which you need to produce a model. So, how, how, how this genetic search can be applied? So, I'm gonna go a little bit technical here, but then the goal is that you can use, for example, PLS, you can use partiality square regression to do this. So, you have, so in, in your library, you can build a PLS model on every single individual or subset that you randomly choose. Okay.  Then you can use this PLS model to project the spectra of the target population or the target set into, into the principal component, into a principal component or like into a PLS space. So, this PLS space is represented by this S. And then what you can do, I mean, this can be seen as a kind of a compression of the spectra in the, of the target population using a PLS model that it is built from the library.  And then you estimate the, like, the, the, the compression error, the spectral, the decompression error. So, and then with this spectral decompression error, you can, you can estimate the spectral reconstruction error for your target population using the, the, the, the, the model that you built from the library. So this is, this Q or spectral reconstruction error can be used as measure of weakness of every individual. Again, every individual is basically a subset of your library. You can also use the spectral dissimilarity to assess the, the weakness of your, of your individuals. So you can use basically the Q and the D. When you do that, you can then apply the, the genetic search method into your library. So what we, what, what I did here, it was that, okay, I use this, this, this, this method that we developed to find, sorry, one second. I use this, I use this, this genetic search method to find in the African soil spectral library that it is represented by basically, but every black dot that you see here in this map to find in this library, the best genes or samples that can be used to produce a model that predicts total carbon aspect like infrared model that to predict total carbon in the Chuappa region in Congo. And you can see that this region is very isolated geographically isolated from the rest of the black dots of the Apsis library. So that is, that was a challenging example. So here in the, on the right, you have the spectra of the Apsis library. And at the bottom, you have the spectra, the IR spectra of the Chuappa region in Congo. So what, what we did is that we identified 300 genes, which are basically samples out of a pool of and almost 2000 samples. So meaning that we extracted 300 samples and like basically that we extracted only, we use only 15% of the samples in the library to produce a model for the Chuappa region in Congo. So we breathe, we breathe 12, sorry, 18 offspring with a total of, we evaluated a total of 2700 individuals and we did that like 100 times. So at the end, we evaluated around 200, 270,000 individuals, meaning 270,000 subsets. Just to find the 300 fittest genes or the 300 best samples to produce a model in the spectral, in the spectral library to produce models for the Chuappa region. And this is basically what we obtained. Let's look first at the right validation plots. Okay. This GS local is basically the name of the, of the method that we developed stands for genetic search for local applications. And on the x-axis, we have the predicted total carbon and on the y-axis, we have the observed total carbon. And you can see that the predictions are quite, quite good. The uncertainty, what we can say about the uncertainty of these predictions is that the uncertainty is quite, let's say reasonable, quite low compared, for example, when it comes, when, when we evaluate the uncertainty of models, calibrated or total carbon predicted with memory-based learning methods. So although I would say that the results are very comparable. Okay. So here on the left side, I would say that this is the, the validation plot for memory-based learning. We wanted, we wanted to compare our results to something else that is, let's say, successfully used for predicting soil properties from soil spectral libraries. We also evaluated the confidence intervals. So in NBL, there are a lot of models that are produced to, to make up, to make the predictions of the, of your target set. But here in GS local, we only have one single model. And this is a PLS model, very basic model. So, and you can see that the confidence interval is quite, say, quite narrow, which means that the predictions are quite, the uncertainty of the predictions is quite small. So there are some, yeah, there are some maybe high uncertainties, the low levels of total carbon that might be associated with the detection limits in total carbon. So the advantage of using genetic search, for example, over methods of like, like NBL is that, again, here, you find these 300 samples and these 300 samples can be, can be used to produce one single simple model. In this case, these predictions, again, these predictions come from a PLS model that uses only 300 samples out of the 2000 samples of the library. So why I say that this is very, this is important because one single model is something that you can easily deploy, for example, in a lab. It's something that you can easily say is portable. It's easy to, it's easy to interpret as well. So you can gain knowledge about the ability of your target site or target population by looking or by exploring this PLS model. Okay. And so to come back to this slide about the success, I think that these type of methods, like genetic search methods are really important when it comes to automation. So why? Because they allow us to automatically detect the best, let's say the useful information that you have in your libraries for a given, let's say, purpose. So I believe that, I believe that the best way to use a spectral libraries is to use them, let's say, extract the relevant information for every particular case and not to try to, for example, feed a model that serves or that can predict every sample that you throw in it. Some of the contributions that I think that are relevant to mention here is that we focus, I mean, most of the research that we do focuses on the usability of spectral libraries rather than in the modeling of spectral libraries. Again, I think that we have reached a good understanding on how to calibrate models. So we use also another contribution is that we use search methods to better understand the target populations. Again, we extract the best information from the libraries to understand some target population, some local variation. We move away again from this concept of global model to a more localized model. So this is a kind of a pattern line shift. We make a soil spectroscopy simple at operational and we want to make it fast to implement and to service. And again, there is some, there is some, there is this saying that it says think globally and fit locally. I like it very much and it's very inspirational and it is basically based on this paper written in 2003 by Saoudan Royce. So I definitely recommend that one. And yeah, just to finalize, I want to, I want to say that yeah, we are planning to bring more of these methods into the packages that we are developing into open source software and like, like, like resemble and prospect are. And yeah, please stay tuned because next year or soon there are more, there will be more tools available in these packages and even additional packages that we are working. So yeah, that's it for today. And I think that we can move to the questions now from the audience. And again, apologies for the, for the, for the problems that I had with the sound. Thank you so much for that presentation. And just as we mentioned before, please feel free to ask questions using the Q&A function where you can even put them in the chat. So I think the first question is, do we need standardization of settings for the library to be usable? Yes, of course. I mean, when you work with standardized spectral libraries is way easier to find relevant information. When you work without standardized spectral libraries, then it is extracting relevant information from it becomes more challenging. However, I mean, I think that there are methods can can handle those disturbances that are introduced by standardized data. So like, we believe that things like genetic search can partially handle those challenges. And then from Jean Robertson, how big and how variable do you think the spectral libraries need to be for this to work? That is very relative. I mean, I think that it depends, depends whether the important question is that we need to ask ourselves is that, do you think that this library that you plan to use represents somehow or covers the variability of the target side that you plan to evaluate or for which you plan to build a model? But that is the question that I think that we need to answer that we need to ask ourselves every time we are going to consult a library. Thanks. And then we have, could you talk briefly about the computation efficiency about GS local method? Essentially, follow up question how time consuming it is it when you're dealing with a large library? Yeah, well, the whole time consuming is it that it depends on the computer you have. But, but we have, I mean, the way in which the method was implemented, it was basically to we build, I mean, we implemented the method in such a way that it you efficiently uses the computing resources. So for example, for this example that I was presenting before, like this hundred hundred iterations to find the best 300 samples for the Congo data, I think that it took me like about 20 minutes in a computer with 15 cores. So it was really not, I mean, it was quite computationally efficient, I would say for the amount of computations that were done like, like in, yeah, to find these 300 samples. And from Melissa Luis Gutierrez, how can I estimate the percentage of samples that I should use to calibrate the models? I think this is going back to, oh, what's your answer? No, that is quite, that is a very interesting question because this is very, at the moment, we are trying to find a method to estimate the amount of samples that you should use. And this is basically something that we, that it is currently under research. But we usually, I mean, in our experience, we usually extract from libraries from 150 to 300 samples. That's more or less, this is a very arbitrary, let's say, range of samples. But again, this is based on our experience, 100 to 300 is enough to build a really reliable model for, again, for a very specific population or subpopulation of samples. So to Rick Mitron ask, what is the level of accuracy we could achieve for soil property prediction using spectral libraries? And I know you showed the uncertainty compared to machine-based learning. Yeah, I think that, yeah, again, it depends. Remember, there was this, there is this quote that I like very much that it is about think globally and fit locally. And from my point of view, it depends on what the target population is. Sometimes if you have a really good coverage of your target population in the spectral library, you will see that your predictions will be quite accurate. But sometimes, for example, if you have a very, very, very rare set of samples, then the prediction might be not as good as you might find in the other case that I mentioned before. So it's very relative. One thing that I also wanted to talk about is the errors that we always tend to look at the RMSE. But we also have to look at the distribution of the RMSE. The RMSE is just one single value, and it is not a deterministic value. It has a distribution, and it is, and we also have to take into account how wide the distribution of this RMSE is. We should try to beat this culture of RMSE. It should also look more deeply into the models. I think this goes to your quote that you mentioned earlier, the thinking globally and acting, but I got that slightly wrong, the global and local. So Melissa asks, again, what would your recommendations be that the soil spectral libraries will be comparable worldwide? I think that is, I mean, from my point of view, again, it's not about comparing soil spectral libraries. Of course, it's more about integrating soil spectral libraries. I think that we have produced tons of data, and what we need to do is maybe, perhaps, integrate and pull all this data together to make it useful. I think that we should maybe try to focus more, again, more on the operational side, rather than on the, say, operational side of spectroscopy, rather than on the calibrating models or maybe even standardizing spectral libraries. Of course, that is important, but we also have to look at what we already have available and how we can integrate that. Because everything that we have is already quite valuable, and if we don't benefit from it, from what we have right now, we are basically, I mean, the opportunity cost that we are incurring at the moment is quite high. I mean, we are losing opportunities because we are not making, I mean, we are not profiting at the moment from libraries. Rarely you see operational models for soils in the laboratory. Leo, if you'd like to take a look at the questions, you're welcome to as well. You're good. I'm just... I don't see the questions, actually. No worries. I have zero questions in my chat box. If you... Hi, there is Estefania here. If you want, I can reduce some that I have spotted. That would be great, Estefania. For example, can you please explain why the prediction standard errors are low in the genetic search algorithm as opposed to the NBL? Yeah, that was not a fair comparison, I would say. The uncertainty estimation that I made for NBL and the uncertainty estimation that I made for GS local use different methods. So that is really not a fair comparison. I just wanted to bring the attention to the importance of also evaluating the uncertainty. And also I wanted to stress that the uncertainty of Paris local is quite low. And not to say that NBL has a wider uncertainty range. One question here by Conrad. What is exactly the point of the multiple generations, the offspring, you described? Okay. Yeah, the point is that the first offspring, for example, it contains, imagine, thousands of genes. So, and then if you go, if you breed a new offspring, the gene pool, meaning the sample, the amount of samples that you have probably becomes smaller. So the idea is that then you suppress all the genes or suppress all the samples that do not bring any relevant information for your target population. So that's the reason why we breed different offspring. So in the example that I show, we bred around 18 offspring. So we went from 1,700 samples to 300. How could we quantify the spectroscopy similarity between training and testing samples? So the similarity, there are a bunch of methods that you can use to estimate the similarity or the similarity between samples. Usually you can use, for example, PLS components to do that. Basically you estimate for each sample in your target population, you estimate the Euclidean or Mahalanoi distance to the, to every sample in your, or to the center of the population of your subsets that you bring or that you draw from the big library. So yeah, again, it's basically Mahalanoi distance standard one. Anonymous attendee is asking if you think those algorithms are, well, still need to be tested by larger scale data before making them operational or if you think they are ready for application now? Yeah, this is a good question. And actually, every time I present this, I mean, I have presented this internally, for example, in the company and also to some, some colleagues here in Switzerland, and this question always comes. We have tested these intensively to build models and to make models operational in other, say, for other products, not soil, but for example, for other agricultural products. So we know, I mean, we have a good indication that it works in the field in reality, in real life. So, but yes, I guess that ideally we should do more total tests when it comes to soil spectroscopy. In soil spectroscopy, I have used the genetic search methods, not as often as I use it for, yeah, for our products, but I have found another one that I found interesting here. Sorry. If we need to select among several global soil spectral libraries, how can we select best library? Or how we know that? Or how we know which spectral library is best in our case? I think that sometimes if you don't know it, then you put everything together. But if you know, if you have a good indication that, for example, in this case, in the case that I, in the example that I presented, of course, I had, I could choose between a big spectral library and comprehensive spectral library developed for the US. But turns out that my target population was in Congo. So, yeah, it was obvious that I would choose the African one, because it's, I mean, from my ex, let's say like basic aspect knowledge, I would expect that the soils from Africa resemble the most the soils from Congo. So basically, yeah, I can limit, say, the extent of the library to samples, I mean, to samples that I know that are somehow they share some, that might share some similarities with the target population. Thank you very much. And anonymous attendee again, or perhaps it's a different one, but how would these methods work when extending models to spectra collected by another instrument with different spectral range and resolution compared to the host instrument? Yeah, of course, this is another, this is a different, let's say, this is a different topic that it would be basically how to integrate data from different instruments. So it's not much, it's not much about, let's say, search. But I think that, I mean, if you already have, for example, something like a subset of samples that you extracted from your big library, imagine you have 300 samples that you are currently using for, for routine analysis in your laboratory. If you want to extend this library with additional samples coming from different sensors, yes, of course, you can still do it and you can easily do it. I mean, the advantage of the method is that you have your samples available. You have your three samples, sorry, you have your 300 samples that you can put together with additional samples that are coming from other sources or that they are collected specifically to enhance the, for example, the accuracy of that initial model that you built from your library. So I don't see that there is any like big drawback on enhancing or augmenting the samples that you extract from libraries. Jean Robertson is asking, how much better are the GS selected local calibrations compared to other ways of selecting spectra for a local calibration? Yeah, okay, that's a good question as well. And I think that, I think that the advantage of GS, of genetic search is that our assumption is that it can handle very well disturbances in the spectra of the library that are caused by, for example, multiple data sources, for example, multiple devices that are used to collect a specific library, or it can also handle very well in some using an additional method that I didn't talk about today. It can handle also very well the disturbances in the in the in the response variables that are introduced by different laboratories. So when you have a library, maybe the response variables, the values of the response variables originate from multiple laboratories with multiple errors. I think that there are some options that you can use in genetic search to handle those disturbances. So that is the advantage basically over conventional search methods that you can filter out a lot of, let's say, circumvent the problems of disturbances, spectral and response disturbances in libraries. Okay, going up a bit on the list of questions. Do you use different soil types for the same prediction model? How local your feeds are? Too local? I didn't understand the question. Do you use different soil types like forest, meadow, arable soil for the same prediction model? Yeah, in this case, of course, again, in this case, I didn't differentiate between the soil, I mean the origin of the soil samples. So of course, you can again, you can use your expert knowledge to filter out a lot of information that you have initially in your respective library. You can limit, for example, if you are working with forest soils, then you might want to exclude some from your library soils that do not belong or that were not collected in forest or things like that. Yeah, I think that basically you can use, again, your expert knowledge to filter out first the samples that you are going to use to feed the GS local method. So with that, I think we're reaching the end of our time. So thank you guys so much for your presentation and for everyone who paid attention. If you have any other follow-up questions, feel free to reach out to me. I will be emailing all of you the link to the recording and the presentation. If you have any follow-up questions and thank you all for all your questions and your attention and for the ideas for training going forward, that's much appreciated. Bye, Dr. Stefania. Bye-bye, thank you guys so much. Thank you guys so much. Bye. Thanks, everyone.


Imagine you've been given an iPhone dump, but you don't have any specific tools to analyze it. The only thing you have right now is a copy of Linux. Can you still start your analysis? Sure. So here we have an extracted iPhone dump, and I have a couple file systems with the entire iPhone file system in it. So one of the first things I can do is use find period. That'll recursively list all of the file names in the dump. Just listing all of them isn't going to be that interesting. But what we can do instead is use find and then pipe that into grep-i and then search for a keyword. It'll look for a non-case sensitive keyword inside the file name. So for example, if I type grep-i keyboard, then I find any file name or directory that includes the term keyboard. We could also do something instead of looking for keyboard. Let's do jpg. And then I'll find any file name that has jpg anywhere in the file name. So you can see here I have .jpg.png. Well, let's say that I want only files that end with the extension .jpg. Because I'm using grep, I can do jpg dollar sign and that basically says jpg must be at the end of that file name. And then we only get files with jpg at the end of the file name. Now are these valid jpg images? I don't know yet. So I can take one that I think is interesting. I'll just take this one at the very end. I'm going to copy that. So I can use file-i and then paste in that file name and then hit enter. And that will tell me the type of file. So here we have application octet stream, char set binary. And then if I just do file without i, then we get for example, a apple binary property list or a BP list. So what I'm able to do is search by file name, search for keywords in the file name and try to find interesting things within the file names themselves. I can specify file types that I'm interested in and see if they have that extension and then pull out all those files and do something with them. What else I can do is use grep and dash r to be recursive and i and search for some sort of keyword. So maybe I'm interested again for keyboard, keyboard. And then if I do star, then I'm going to search all of the files in this particular dump recursively. So I'm going to go through each directory. And I'm going to look for any files that contain the word keyboard inside of the contents. So I'm not just looking for file name keywords. I'm looking for any contents that contain keyword. We had a couple hits up there. So we have a lot of files that are responsive to that. But let's look at some of these matches on my screen. It's kind of purple. And then we have in red, where we matched the file that it matched on. Okay, so we have some sort of localizable strings. And then we have the line that matched with everything else here looks like it's probably a binary match, which means that all of these files were responsive. But we expect that because keyboard is such a general term that a lot of different files are going to match it. I think the suspect's name was Marsha. So let's look for Marsha. We get a couple, again, binary matches, some with Siri, a couple matches that actually printed out. So that's a quick way that we can start to do keyword searches within files. Now, the problem with using find and grep this way is that every time you want to search for a particular keyword, you have to rerun that query over all of the data. If you have the time indexing all of this with some sort of indexer, maybe like autopsy will go through and index all of those files, then whenever you do your keyword searches, it'll use the index instead and it'll be much faster. But if you only need to check one or two keywords or do a really quick check of whether certain files exist, using find and grep is a very, very quick way to do this. Remember, we're dealing with an iPhone, so it's pretty much like doing command line analysis of a Windows system. What you'll find though with iPhone are P lists and BP lists and BP lists cause a little bit of problems. So let's look at how to look at those on the command line. So let's find our file that we're interested in. So I'm going to do grep user words does CTRL. Okay, so I'm looking for user words that CTRL and I got a hit. So its location is mobile library keyboard. And I want to analyze, for example, how many keyboards, software keyboards are installed on this phone right now. If I do cat and then paste in that directory, read all of the contents of this file. Now you notice that we have a lot of these question marks and it also starts with BP list.  Now one way that we could analyze this is by using XXD. And that'll give us the hexadecimal view of everything. But you can see that things are still kind of encoded and it doesn't look very easy to analyze. So we can do a little bit better by using a tool called BP Lister. So this is from three planet software BP Lister is excellent if you want to actually get access to binary P lists. If you're dealing with iOS, you're probably going to run into binary P lists at some point and BP Lister will really help in this situation. So it is in a GitHub repository. So go ahead and click code, copy the repository. So I'm going to put it inside this iPhone directory do get clone paste that GitHub repository in there. So we've cloned it down and then BP Lister is written in Ruby. So we have this P list parse dot RB. Okay, so the whole path of our file is here. I'm going to copy this. So now I'm going to do Ruby because I need to have Ruby installed to run the Ruby script BP Lister P list parse dot RB.  If I enter, then now we kind of get the pretty version of this. It looks something a lot closer to JSON, I can actually read it out instead of having to go through a hex editor. And we can see here a couple different things. But what I'm really interested in are these three entries and these are the software keyboards that are installed in the phone. And again, if we just tried to read that data out, it would just be a binary data that would be hard to understand. So even if you're using something like autopsy to analyze an iPhone, not all of these data structures are parsed out correctly, you might need to extract it. And if it starts with BP list in the header, run it through BP Lister, and you should be able to read it out. So there's a couple tips for you, you absolutely can do a full iPhone investigation through command line. Of course, it's not always going to be as convenient, you are going to have to do some of these conversions. But if you have something like BP Lister, it'll make the job way easier. But that's all I have for you today. Thank you very much.


Ranger Bill, warrior of the woodland, struggling against extreme odds, traveling dangerous trails, fighting the many enemies of nature. This is the job of the guardian of the forest, Ranger Bill. Pouring rain, freezing cold, blistering heat, snow, floods, bears, rattlesnakes, mountain lions. Yes, all this in exchange for the satisfaction and pride of a job well done. Hot weather, little rain, dry forest. All this put together adds up to terrible danger. Forest fire, scavenger of forests, a roaring hungry monster consuming everything in its path. Bill has sounded the alert. The forest around Notty Pine is tinder dry. All firetowers are carefully watching for the slightest wisp of smoke to curl up from the millions of acres of treetops. All eyes are watching, watching by day and by night. They're watching for fire. Listen to the story of the fire bug. Stumpy, have you seen our copy of the Ranger's magazine around here? Nope, there ain't no comics in it, no house, honey. They'll give you comics. Gray Wolf, have you seen it? I not see it, Bill. There are plenty of good articles in there on new diseases of trees. Yeah, that's why I want to find it. Where'd one? I'll answer it, fellas. Bill Jefferson speaking. What's that? Yes, yes, we're on our way. Goodbye. Fire? Right. Let's go, boys. Where's Fire Bill? South Forks. It ain't long. The Red Valley free tracks again, is it? Hell, that's where it is. Stumpy, your closest to the driver's seat. Hop in. Yep. I'll let her roll. There you go. Step on it, Stumpy. Maybe we can nip this one in the bud. You're just in the nick of time. Another five minutes and whoof. Ah, getting tired putting fire out along tracks. I am too, Gray Wolf. This is the fifth fire in three weeks. Randolph Thompson's got to do something about those spark-throwing locomotives of his. Yeah, we've got to get proof that his engines are throwing the sparks. How do you plan to get proof, Bill? I'll tell you how, Gray Wolf. You and I are going to camp out here and watch the iron firebugs throw sparks with our own eyes. That's a good way as any, Bill. All right, let's pick up our gear and go back to town. Right. Then Gray Wolf and I will get the horses and come back. Randolph, we've got to do something about the stacks on our engines. They're throwing sparks like a torch. Listen to me, Scotty. I'm not spending one red cent on those steam locomotives. In a couple of months, we'll have our diesel engines and then we can junk the iron horses. Yeah, but the rangers are going to get pretty sore when they find out how the fires are starting. Oh, that's all I've heard since the first fire. Rangers, rangers, look out for the rangers.  They're federal officers, boss. You stick your neck out far enough and the rangers will wrap a cord order around it. Is that so? Let them try it. I'll run my freight trains when I want to and how I want to. Your job is to see that my trains stay on schedule. Okay, but don't say I didn't warn you. This is the easiest job I have for a long time, Bill. I'll agree with that, Gray Wolf. Then we'll stay camped here until one of Thompson's spark-throwing locomotives comes spouting along. Are you planning to stop these fires? I'll ask Randolph Thompson to put spark arresters on his steam locomotives. I understand him plenty hard man to talk with. Yeah, I've heard that too, Gray Wolf. I know Thompson by sight only. I know he took over the Red Valley freight line from bankruptcy and made a going business of it. So he's plenty good. Sometimes these self-made men are hard to handle because they've had to work so hard to get where they are. Here comes the afternoon freight now. Maybe we hide back off right away. No, Gray Wolf, we'll stay right here. Miss right away is least on government property. Look, Bill, engine-throw plenty spark. Well, I'll say. That's what I was hoping to see, Gray Wolf. That's our firebox. Again, Scotty. What is it now? I've got hot news for you, boss. I suppose you saw some rangers in your sleep last night. I put the engineer on the three o'clock southbound saw them. What? Where? Along the right-away. You sure? Sure as I'm a foot high. Not only that, but the conductor watched them from the caboose. So? Then they followed them on horseback. They know what's causing the fires now, Randolph. All right, so they know. Let them make the first move. What? You heard what I said. Let them make the first move. Well, what you do about this is your business. But I know one thing for sure. What's that? If Bill Jefferson comes to see you, you'll know you've tangled with somebody. Yeah? Yeah, and take a tip from me. Don't rub his fur the wrong way. Speller Thompson's got a lot of equipment in his freight yard. No wonder. He does quite a freight business, Tempe. His line moves a terrific volume of freight, both north and south. On his north runs, he has to use double headers to get over the mountains. Bill, I checked up on his schedule. He runs from six to eight trains in 24 hours, both ways. Oh, that'd make plenty tight schedule for one track. Yeah, his engineers had to push the throttle pretty hard. That's why the old locomotives throw so many sparks. This looks like the general office building right here. That's what the sign says if he can read, sonny. Well, if it was alive, I guess it'd bite me, huh? Yeah, what's the plan of attack, Bill? Well, Tempe, you and Ralph find out how many old steam locomotives he's got. Those are the ones that throw so many sparks. The newer steam jobs don't do that. Gray Wolf and I will go see Tums. We'll meet you back here at the car. Okay, Bill. Come on, Tempe. Let's go count iron horses. It's Tempe. He sees something funny in everything. Maybe we should have taken him into the lion's den with us. Now, let's go pay an official visit on Randolph Thompson. Good afternoon, gentlemen. It is never a railroad man who has a visit from the forest rangers. We're not here on a social call, Mr. Thompson. We're asking your cooperation in preventing a disastrous forest fire. I'll be glad to help as much as I can. Thank you. Now I'll get straight to the point. We've had five fires in the last three weeks along your right away. It runs through the giant pines. Yeah, and did you put them out? Only quick action saved whole forest from burning down. That would have been most unfortunate to lose the giant pines. How does this affect me? This affects you directly, Mr. Thompson. Since you own the firebugs... What do you mean I own the firebugs? You'd better be careful who you accuse, Bill Jefferson. I am being careful. Very careful. That's why Gray Wolf and I camped along the right-of-way after the fire to get absolute proof. You camped on my right-of-way without permission. That's private property in case you don't know it. It's private property leased from Uncle Sam in case you've forgotten. You already forget? You and Colonel Anders signed papers that gave you permission to build railroad through giant pines. I built my road according to specifications. I don't know how you can accuse me of owning firebugs. Maybe you'd pair to explain. The firebugs we're talking about are the ancient steam locomotives you're using on the southbound line.  Those ancient locus have always thrown sparks. How come they're so suddenly dangerous? Simple. The forests have become tinder dry because of the heat and drought. Isn't that my fault? No, but it everybody's job to be careful when so forest very dry. I suppose you want me to stop my freight trains until we get some rain. Thompson, all we're asking is your cooperation until we get rain. These evasive tactics only convince me that you know exactly what we're talking about. Isn't that so? We know engineer on trains see us standing alongside track. He must tell you by now. What do you want me to do? I want you to put spark arresters on those old clunkers. Spark arresters? You want me to spend a lot of time and money putting spark arresters on some old locomotives that will be junked as soon as we get our diesels? Thompson, surely you're not comparing the cost of installing spark arrester equipment with the value of the giant pine national park. If you are, you're not the businessman I thought you were. Now look here, Jefferson. I don't intend to put money on those old locomotives. Why can't you put your rangers on guard along the track until the dry spell ends? That's impossible. One fire needs all the men I have available. The rest of the men are busy watching the other forests. Well, gentlemen, I'll think it over. That's the most I can promise you. I want something more than a promise to think it over, Thompson. I want action. Your firebugs kindle another forest fire. I won't be so pleasant to deal with. That's a warning. I said I'd think it over. And that's the most I'll promise. Good day, gentlemen. So your visit with Randolph Thompson didn't bring much in the way of results, huh, Bill? No, Ralph. He was polite in that cold sort of way, but definitely uncooperative. He in for a big fall maybe sooner than he thinks he'll come down from high horse. It seems to me we ought to teach that ordinary critter a lesson. Yeah, that's what I'm going to do, Stimpy. What do you mean, Bill? I'm going to ask for a court order from Judge Deems. If the judge comes through, as I think he will, Thompson won't be able to move a single train south until it's equipped with a spark arrester. Here's your court order, Bill. All signed and sealed. Thanks, Judge Deems. I appreciate your cooperation. It's a matter of conscience with me. Randolph Thompson ought to be ashamed of himself for disregarding the safety of our national forest. Uh, what recourse do I have if he ignores this court order, Your Honor? Well, we've got a big jail here, naughty pine, Bill. What I mean, Judge, is this court order binding on his employees as well as the owner? That's right, Bill. The court order reads that during the dry spell, not one train is to move south unless it has a spark arrester on its stack. Any person or persons who break the order may be arrested on the spot. Thanks, Judge. I won't serve this unless I have to.  Situation. Do we get this thing straightened out? You'll have to patrol a railroad track constantly. That's the only way a disaster can be avoided. We'll keep our eyes open, Bill. I realize you're not Superman, of course. So I've alerted towers three, four, and five to keep a special watch, too. If a fire starts, send for help at once. We'll take care of things, honey.  That's good, old timer. Now, you fellas, it better get going. It's almost time for the early morning southbound to take off. I'm going to have a talk with Thompson again. Okay, Bill. Get him up, King. Hey!  Scotty, I'm depending on you to take care of things while I'm gone. I'll be back this evening on this freight. I'll keep an eye on the whole operation, Randolph. Maybe you're convinced that Bill Jefferson means what he says, huh? Why do you think I'm riding this caboose on the south run? Because you're afraid of Bill. Don't be ridiculous, Scotty. But I think you may have gone after a court order, so I'm leaving. You can't serve it on me if I'm not here. That won't stop him. He'll serve it on you. Don't worry about that. Maybe so, but this shipment of perishables, the machinery has to go through, and I'm going with it to make sure it does. Boss, you'd better get going pronto because here comes a ranger car down the road now. Thanks for telling me. Here's the engineer's signal, and I'm on my way. Here's the ranger by regards. Will you, Scotty? Bye, boss! You just missed him, Bill. He's in the caboose of the morning southbound. He is, huh? Well, that's the last train. He's going to move south. What do you mean? This is a court order, Scotty. Judge Deem says it's binding on all employees of the Red Valley line as well as the owner. But you have to serve it in person, don't you? No, I don't. Not the way it's worded. I'll leave the order with you, Scotty. I'll let Randolph get away with the train he's running through now, but no more, understand? Bill, believe me, there won't be another drive we'll turn until this whole thing is settled. Good, Scotty. I wish your boss had the same cooperative spirit. I'll only be back. Well, probably around midnight. Why? I want to see him. Thompson hasn't let another train leave the yard since this morning. That's good news, Ralph. I didn't like to take such drastic action, but the man wouldn't listen to reason.  Yeah, that's why he came out here. Let's call it a day and head for town. That's plenty of good idea. It's plenty hot and muggy out here. All right, engineer, let's get rolling back to Naughty Pine. Wouldn't advise it, Mr. Thompson. I don't like the looks of the crack and that left mean piston rod. Oh, want it all? Well, possibly. Hard to tell. I'd say no. We'll chance it anyway. Just reduce your speed going back. But sure, it's... I said get the train underway. Do you hear me? That's an order. Okay, you're the boss. Give me the high sign when they're getting the cab with you. You bet. I ran into you. I was on my way over to your office. Why? Why are you so excited? Well, couldn't get a hold of Randolph at the other end of the South Run. What do we do now? Oh, just wait until he returns. That's all you can do. Do you think you'll bring the freight back here or return the other way? You said it was bringing the freight back. Okay. You'll just have to wait and be ready in case of trouble. Ranger headquarters, Bill speaking. Along the Red Valley tracks, Thompson ran another train back from the South. Did you notice it? Okay, Tom. We're on our way. Again? Yeah. We've got a fire to fight. Let's roll, boys. We'll take care of Thompson later. This time and fire get good start. It on both sides of track. Ralph, call for bulldozers and man. I'm not taking a chance on this getting away from us. Run away, Bill. It's a treetop fire. Our best attack is to knock down the burning trees under the railroad track. Let's get dynamite in the safety cases and stuff it under those trees. Right away, sonny! I give you hand, Stuffy. Hang up the dynamite, old ship, Bill. Fine, Stuffy. Ralph, set up flares on the tracks. Gray Wolf, let's get after those big trees and blow them down. I'm ready to go. We get going. It's a good thing there's not much wind. Come on. Behind this big tree, fellas. Good job, fellas. It's a good thing we can use the railroad right away as a fire lane. Yes. Them trees fell right across the railroad tracks. And now Ralph's got the flares, you know. Hey, here comes Ralph like he's got a hot foot. Bill, Bill, look what's coming up the track. Great Scott. It's one of Thompson's big locomotives. Pulling flat cars loaded with man. Yeah, here comes the fella. And it isn't Thompson. No, that's Scottie. I got here as quickly as I could. About all the men I could get my hands on, including the yard crews. Oh, that's fine, Scottie. We'll need them. Gray Wolf, Stumpy, Ralph. Divide the men and get after that fire. More help is on the way. I'll be right here in a minute. Bill, I'm terribly sorry about this. A new Randolph would get into trouble by not doing what he should. What do you mean, Scottie? His train standing on the side way at the 10-mile bridge. He and the boys were looking at the drive wheels on the engine. Something's wrong with the old clunker, but I didn't take time to find out. Well, now we've got real proof on Randolph. That's why I kept going and trying to offset some of the damage he's done. How'd you know there was a fire? I could see it from the dispatcher's tower. Well, I'd better general this fire. Come on, Scottie. We'll get to work. We've got to save the giant pines. Replace your tired crew. Pass the word to Gray Wolf. Here I am, my train. The caboose on his train will smash into the black cars. Get the land back from the tracks. Here comes the place. And she's got a space. Let's get him out. How's Thompson feeling now, Bill? Pretty good, Ralph. Me and the crew jumped from the caboose just before the trains crashed. Good. Got a nasty crack on the head, but the doc says he'll be okay. Well, I'm glad for that. There's been enough damage done. Look, he's waking up. What happened, Randolph? You able to tell us? Yeah. The piston rod broke, climbing the 10-mile grade. The rod damaged the hair breaks, but we managed to back down the hill under the siding and hold the train with handbrakes, but the handbrakes gave out. And then I decided to roll back down the valley and let the train stop by itself. But didn't you think about the fire? I'm afraid I didn't do much thinking. I didn't expect the tracks to be blocked. What a mess. Did you get the fire out? Yes, the fire is out, Randolph. Scotty, have you got the piece of paper I gave you? Sure. Right here in my jumper pocket. What piece of paper is that, Scotty? The court order, Bill served. Oh. Well, Bill, Bill, go ahead and serve it. I deserve it and more. Randolph, I'm going to tell Judge Deems I didn't need it. I think the Lord sort of allowed circumstances to serve their own court order on yours. Yes, that bump on the head knocks some cooperative spirit into me. And I appreciate your spirit, Bill. Thanks. Believe me, I'll do all I can to make restitution. Yippee! The giant pines is safe from them. They're firebugs. Well, Bill and the boys, with the Lord's help, finally got Randolph into a cooperative mood, even though it took a bump on the head. Some folks do have to learn the hard way, don't they? We'll see you next week for more adventure with... This is Stumpy Jenkins, a Ranger Bill's old sidekick. As I guess you all know, just adding a little extra word of thanks for getting yourself in on the program today. Always glad to have you along. And I hope you invite your friends, too, for we sure got lots of adventures to tell you about. And we don't want you to miss any of them. So you make sure to be there by your radio every week. Don't lose out on our next story.


I'm Emma Van Blert, I'm connected to the Wemble Wemba, Gwindi Jamara, Nattajun and Tanarong people. When I paint with bright, vibrant colours, I'd like to hope that when people look at the artwork that they're feeling what I felt when I do put symbols in there and the story. You know, there are symbols in there that I hope people would look at and will connect to, such as, you know, I've got a lot of symbols that represent respect. I've got a lot of line work in there that represents building knowledge, moving forward, working together. The purple area with the footprints represents journey. The footprints are CFA members walking and working together to create a safe environment for community. The purple section in the middle of the painting represents the CFA. The white dots connecting the figures show ongoing unity and sharing knowledge when looking after land and communities. The red coloured circles with the lines in the middle shows planned burning in a safe and controlled way supported by CFA and outers when caring for land. The top middle sections represent important concepts for Aboriginal people when caring for the land. The footprints are Aboriginal people living on the land, their knowledge and connection to land. I've got a lot of figures in the paintings so I hope people can put themselves in the painting and know that they are doing a good job on the ground, you know, from your volunteers to your CFA workers to outers who then pass on the knowledge. There's a hope that you would look at the painting and connect and feel the same way that I felt when I painted the painting.


Good morning, everyone. This is a talk about experiences and lessons learned in a local project that uses Debian at schools in Germany, in Schleswig-Holstein, which is the most northern country of Germany. So before we start, I'd like just to go around how many teachers are in the room. Yay. How many developers, techies, system administrators? Well, developers first. Developers first. Okay, technicians, assistant administrators. Okay. Okay, so this story I'm telling you is maybe about all of you. How many, oh, that's a good point. How many parents are in here? Grandparents? Ah, good. Okay. Actually, I forgot to raise my hand with the developers and the techies. So, hmm, I raised my hand then. Yes, yes, yes, yes. Okay, so, okay. Okay, so that's the official title of the talk. And so I mentioned the sharing. I'm really interested in what you have to say, not say, maybe, but write. So please, if possible, use the gobby. So I'll be talking about a project in Germany. And the first question is actually, who am I talking about? Who are we? So that is our design. We are a little group of people, actually, including me, and a local computer company in Kiel, including my wife, actually. And including all the schools that we are working with. And the project setup is more like we're not selling a product to the schools, but we are actually offering our cooperation and our support. And so, and what we try to achieve is actually, not this normal customer care, first level, second level support, but we actually try to achieve, on the one hand, introduce free software to the schools. On the other hand, be a partner and also see them as a partner so that there is a mutual learning process going on. So there's the first story actually coming in. Like in 2011, I was doing support for one club of Rome School in Kiel and I didn't know anything about what's going on in the Linux world, really. I was a Linux system administrator by the time, but I did not really connect to communities at that time. So they asked me for help. I said, yes, I'm a freelancer. I come in. I did it on my own. I invented a really complex setup. And then Andreas, who is the guy from the computer company, he said, well, let's do something for other schools because we have all these school customers coming in and they are asking actually for how can we do IT better than we used to do it or than we are actually doing it now. And he said, and you've been doing this at the club of Rome School in Kiel, so what's your choice? What's your idea? And then I sat down and I didn't know what to do otherwise. I wrote a big concept, actually, a big document about how to do IT at schools without having a clue about what I was writing about, actually. So then it came to a point where all IT teachers, so the teachers at a school that sort of feel responsible for the IT at the school, were gathered together by one of the deputy headmasters of the school we were actually were in communication already with. So I was sitting, I and Andreas and Angela too. So Andreas is a techie guy and Angela is more into communication. We were sitting in front of this group of IT teachers and proposing our idea with a presentation with like 50 slides and everything. So it was really overwhelming. And then there was this one teacher saying, why do you reinvent the wheel? Why do you reinvent the wheel? So there are loads of solutions already available and he just enumerated them.  We tried to rescue the situation, but we left the room and then Angela really took us aside. You did not look at what's going on on the planet. This is amazing. Why didn't you do that? So then we started looking at stuff. So we looked at stuff done by a company in Bremen that's called Univention. Is my voice still okay? Hello, better. Okay, thank you. So we looked at what the Univention people do. They have a UCS at school thingy. We looked at a different county in Germany which is Baden-Württemberg. They have a, they had a solution for schools at that time. And we looked at Debbie and Edu, Schole-Linux. And that felt different because the others were doing the county thing was just for the county. They didn't really plan to actually promote it somewhere else. The Univention product, UCS at school is really a product and I tried to install it three times. At that time that was 2011 at work. So it just, the installation failed on a local computer. So I said okay, this could not be it maybe. And then I tried Schole-Linux. And what I found actually was a CD installer that provided me with a school server that was sort of working out of the box. So actually only something that I was used from Windows small business servers at the time. So, and then Angela really came in and she said, okay, this looks, this feels like something you should do. We go there. So we went to a place in Germany called Gutersloh which is my birth town by the way. And there is one Schole-Linux group. And this Schole-Linux group handed me over to another group which was in Zwei-Brucken in Rheinland-Pfalz which is a different county. And then they had a project going on for the whole county actually. Working on Schole-Linux as a model solution for the whole county for all schools with a lot of money involved everything. And there actually I met Holger, Holger Liefsner was in the room, and Jonas as well and a couple of other DDs and people attached to Debian somehow. And it's really started to, well it started making sense. So and at that time in that project in Rheinland-Pfalz there was a big issue actually why the project at the end people would say it actually failed. And the point was that a lot of stuff got produced there but it didn't get upstream to Debian. So they ended up to three years with a different package server with different software on that that was an overlay for Debian. But the back streaming, back upstreaming did not happen. So I came back from this meeting I said to Andreas and Angela, if we do it, we do it right. So we need to do it sustainably. One of us has to become a DD, everyone was pointing at me at that time. And we work for the schools and what we do has to end up in Debian so that we finally can actually stop working on school projects, orphan stuff. And then it's still in Debian and hopefully someone else takes over. So have a large context for our little project in Kiel. So far to the theory of doing free software at schools, but then we started deploying it and then we started actually getting involved with people, with teachers, students, parents, not so much sometimes. Teachers, teachers, a lot of teachers actually. And without turning this into a rounding session, we were actually confronted with the demand, this is working at home, why is it not working at school? So we really needed to actually change our way of being when getting in touch with the people and the most important part was not IT. It was communication and it was listening. So and considering ourselves as being partners, not as much as imposers, missionaries, free software advocates. And more like going in touch with them and actually really trying to get, trying to understand what they need, what are the needs and what can we provide and also be honest with that, what can we not provide? So yeah, I just go back because otherwise this happens. So the intersection becomes really, really small if there is any at all. Okay, so I would also like to mention that all this, what we did, all the experiences we made would not have been possible with such a great community in the background as Debian and also with the work of a few people in the Debian Edu project, namely Holger, Wolfgang Schwer, also Petter Rinalson and more the legacy stuff because he's not that active anymore at the moment but responsive on questions, really good. So we wouldn't be there. And what we also tried to be, how we tried to be actually was we did not want to sell a product. So there's actually a competitive product in Lesbich Holstein and in the counties, in the south of us, it's called ISERF and they really, they shoot it in every school, and it's like mushrooms popping up from the soil. And it's a very rigid way of providing the system. So if you have a question, if you're at the school and you have a demand that's not complying with the default setup, then there's virtually no chance as far as I heard to actually adapt the setup. And that was something as partners, we understand ourselves as being able to actually modify what we provide and that is actually very, very easily, can be done very easily with Debian Edu, Scholar Linux. So just as a reminder, what Scholar Linux is, it's a main server, it's clients attached running Debian, it's diskless clients attached running Debian, it's terminal servers that you can attach and then you can run thin client stations. We actually extend the setup by wireless in the school building, e-learning software, et cetera, et cetera, and every step that we, every piece that we sort of develop, I'll try to actually have Scholar Linux to Debian. So let's come to the lessons learned. It's not about IT, really, not at all. If the social setup is working well for you, then anything is possible, it might just take a bit of time, it might just take a bit of money, but the main thing is communication. So, and the main thing also is if communication goes wrong, if something goes wrong, the space becomes unclear, clouded between people, between you and your customer slash partner, then it's really important to clear up the space immediately as soon as possible. So get in touch and stay in the flow of communication. To be able to communicate actually, as a floss enthusiast, it's probably not that easy. Well, for me sometimes it's not that easy actually, but the best thing you can do is listen. So really train your ears, train your heart, and get the people in. Try to really understand what they run from you, and once you have that, you can come with the ideas, slowly maybe, and maybe not in the technical detail because that's always overwhelming. I have never seen a situation where it is not. And then do the technical solution and then see if actually what they wanted is what they got. And check that early, maybe during the deployment. What is also in the context of that is actually don't impose, don't demand, but know your boundaries. So if you go to school and then you start the whole thing with a free software project and you want free software because free software is what we need in education. It's not Chromebooks dumped into schools like for a couple of pens. It's, we want students to actually be able to choose the devices, the operating systems, the software that they need, the way of communication they want. So we want to maybe provide the means for declouding people, which is actually a nice word, to decloud people. So know your boundaries, but don't impose.  Don't be a missionary either, it doesn't make sense. It's, I've done that a lot. You have to use free software and then they try it and it's broken. And then it's actually the whole thing gets worse because they tried it and then it's broken. So that's why we don't actually do acquisition with all the schools around us and tell them about our project. They find us, we don't have many customers, but the customers we have are awesome and the cooperation is awesome and it really is fun. So another thing is that we have one guy on our team. His name is Marcel and he's an administrator. No way of saying it differently.  So that is really, really nice actually. When actually we have had masters at the school that say, oh, can you bring Marcel? Can you bring Hezando, that's his name? Because he is such a convenient, nice way of being and he's a really quiet guy and when he says something he really brings it to the point and people understand him. So if you have, well, you shouldn't do that on your own anyway. So you need a team and if you have a team, a person like that is really, it's so valid, so good. So, and mostly those persons, those staff people don't go in depth when it comes to fixing bugs or problems or packaging software. That's not what they do, but they create the link between you and your partner slash customer. So also think long-term, not short-term. So sometimes we hear a demand and maybe people get angry at us, but it vanishes after two weeks. And it doesn't come back. So on the other hand, think about we have release cycles in Debian, so we need to actually enroll our customers in having and going with these release cycles. We don't want to deploy an image and then they are stuck with that image. So we want to actually create a flow regarding software upgrades, regarding corporations, regarding staff people, so really think long-term and also act short-term. Something we haven't achieved yet really, but we need to do is involve your team in Debian so that at the moment I am the main person that actually interacts with Debian so that is not ideal. So we need to improve on that. And one important thing that we have experienced now, I experienced a second time, watch out for staff changes in the schools. So if there's a headmaster change or deputy headmaster change or the IT affine people in the school, if they change, probably the whole situation changes and maybe also it changes that you are not a customer anymore. So be aware of that and sense what's going on and be prepared that you actually lose a customer when such a change happens because those people in charge actually, they already have been at other schools, they have been doing IT, they had their solution there maybe in Germany, it's quite common that you change the school when you become promoted. So yeah, so and that's fine. They have their experience and they want that, of course, for their school and with the teachers I have met, only with some of them, they actually really react and say, no, no, no, we want to keep the setup or we want free software. It's more like, well, yeah, let's try something else if it makes sense and I'll bring my Android to my lessons anyway. So yeah, really be careful with that. So we have one more minute. So any comments, any remarks, any questions? Mike, please. Hi, I'd like to ask, after the initial setup, how many times per year, for example, a school needs you and calls you to handle some stuff that they cannot handle themselves? So that difference actually, we have one school that let us come in every week for two hours. We have another school that we only play the fire engine, fire, what is it, fire people for? So when something fails, really, and that is like, depending on the reason, sometimes it's the DSL provider, sometimes it's actually that we have a disk failure, so we try to monitor that, of course, but also you normally notice that immediately on the network, so they call us in. We have one school that is so in, well, it's actually creating progress in our project that schedules bigger project with us four times, five times a year. Like we work on a school manager plugin for Goza, which is the elder management front, and so they actually did the incentive for that. Then I package Elias for Debian, which is an e-learning software. So they drive the project forward, so it's really, really different. And my recommendation is actually, if the school can afford it and keel, there's a model change. So, you know, like five years ago, it was they had something going wrong with the sink or the toilet, and then they called a plumber, yes, sure, but with the IT, no way. And there's a paradigm shift at the moment that they say, okay, the toilet and the sink and the sockets and the wall, electricity, and the computer, it's all equipment. So we actually need to get some money for that. And if possible, send someone to the school on a regular basis and stay in touch. And also as the project lead and the school head, you have a chat at least once a year, maybe better twice a year. All right, welcome. Hey. It's Anna. Welcome to Montreal. Thanks so much for being here with us. I'm very excited about learning more about Skoll Linux. A quick question for you. Do you handle requests with a support tracker? Yes, we have OTRS. If so, do your customers handle that directly or they just call you, email? How do you handle this? Yeah, that's a good question actually, because we wanted our customers to have as transparent access to our processes as possible. So what we have is we have OTRS tracking system and two, one or two persons from each school have an account as an agent, which is a supporter on the tracking system so that they actually can see everything that we see. So that, I mean, that's sometimes a bit revealing and you cannot, you know, you cannot rant about your customers and that, which is not belonging there anyway. So, and we also need to train our staff members to actually not be afraid to put a comment in a ticket and say, that says, I didn't know how to do that because I pick it up and then I try to sort of help our staff people because then you have to reveal your failures, your incompetence and actually that is one of our big principles we have in this project, to not hide our stuff. And it's derived from Debian. In Debian, we don't hide our mistakes either. So, and we have the tracking system and the customer lets, we let them know, let them know anything. Okay. Other question? You stop me. Okay. Yeah. One more question and then. Well, just a quick experience sharing. As a parent of four here in Quebec province in Montreal, I have two in elementary school and two in secondary school, high school. And basically in Quebec province, there's the schools are managed by a system of school boards that manage territories. And it's a system that looks very much like the municipal system with elected officials and so on. And I participated in school elections for school board official two years ago. I also was a volunteer for half a day or week during our whole year in elementary school. And it made me witness to all the problems that we have. At least here in Quebec, we have a lot of people paid directly so that free software never happens or never gets close to the schools. And we have, it's a worse situation than only having non-free software. It's that now we have three companies, Microsoft, Google and Apple competing for space in the classrooms with iPads, Chromebooks and Microsoft 365. How do you address that? Is that even a problem for you or do you see it's a different situation in Europe than what we have here? And in German schools, the teachers have the right to choose their media equipment for learning. So the teachers and the parents in the school conference, if they say we wanna use free software, then that's the decision. So it cannot be superimposed by government or county government or- So the decision is at the school level? It's at the school level exactly. We, in September and this year, we have a meeting with an institute, part of the county government, which is basically taking responsibility for quality assurance regarding media and IT usage at school. And they got in touch with us by different story. And they, in this talk, they said, well, we have IT providers working for us. And then it's like, I don't wanna name names, but it's like they can sell you education-priced hardware and then they see there's a demand to actually set up the hardware in the school. So they say, yeah, we can do that too. So they send someone and then they hand over the setup back to this institute. And then they realize that they have to actually fix loads of things. So they are actually looking for someone that is really involved in schools and IT and then they get in touch with us and they were a bit like, feels like you have made up your mind about IT and schools. That's actually quite amazing. We need to talk. So, and I'm quite curious what will come out of that. One thing is that the paradigm goes, moves on from school server, one per school, user management per school, and it goes over to ID management on a municipality level or county level. Okay, so I'm here at Debcon, address me if you have questions or more feedback. Thanks for attending the talk.


Hi! Today, I would like to tell you how many Danes identify as both cultural Christians and as non-religious at the same time and explain to you why this is in fact not a paradox. I would guess that quite a many of you are members of the Church of Denmark, right? At the same time, however, I would also guess that quite a many of you don't go to church very often and do not really believe that much in God. The term of the Scandinavian paradox denotes this seemingly contradiction. How do we make sense of it? And how do we accurately describe the religiosity of contemporary majority Danes? Today, I will tell you how I believe we can make sense of this paradox. In my research, I have analyzed qualitative data as well as server data to try to make sense of this paradox and today I will explain to you one of the most important findings. What I found in my data was that many people have actually not just one but two identities. Let me explain. So first, a lot of people identify as individually non-religious. They identify as individually non-religious because they do not reflect very much upon religion, they do not have religious practices, and when they construct meaning in their lives or handle life crisis, they do not do this through religion. However, many of the same people also identify as cultural Christians. Feeling like a cultural Christian is connected to feeling an infinity with Christian values, traditions and holidays, with going to the church when they celebrate big life-changing events such as baptisms or weddings, and with going to the church when we celebrate the holidays such as Christmas and Easter. So how do we make sense of this that people have two simultaneous identities? Well, what I argue is that we should take it seriously. Rather than always forcing people into simply just one box with one identity, we should make room for people to have several identities in our research. Lots of people will feel cultural Christian when they go to church at Christmas, but the next day they will feel non-religious instead. Taking that seriously into our research is how we saw the Scandinavian paradox. So just like many of us have multiple identities in our everyday lives, as for instance a researcher, someone's friend, someone's daughter, an amateur baker, so do many people have multiple religious identities at the same time. And that is how I believe we can solve the Scandinavian paradox. Thank you.