Let's say the question that we have, why does the human brain project need such a, let's say, big computing and data analytics infrastructures? And so I formulated these questions and I tried to answer it today why we need these infrastructures and why we want to build it up in a sort of co-design process together with neuroscience. So my talk is structured in the following way. I would first give you a short, very short introduction. We already have heard yesterday by Richard Frakowiak something about the human brain project. Then I will concentrate on what is our subproject, the HPC and big data analytics subproject and the corresponding infrastructure to build. I will give some exemplary activities that we have done in the last year. So the project is running now for one and a half years to build the infrastructure and what actually do we do in terms of research on the one hand and the other hand in terms of activities to, let's say, give services to the community. Then I will introduce you to our concept to form the infrastructure. Building and establishing, let's say, computers, establishing data repositories, establishing the networks as well as the, let's say, parallel global file systems and so on. And on top of that, all the service is one thing. The other is it really adapted to the needs of the human brain projects and of the wider community on the long run and our concept to guarantees is to introduce this co-design projects together with neuroscientists. And then I will try to answer the question I pose at the beginning of my talk. The question is why do we need those concentration on supercomputing on HPC and on extreme scale data repositories? I have three examples. One is image registration. The other one is intercellular architecture. The other one is fiber architecture. And then I will also touch simulation requirements. And you will see that all of those are now going into regions of requirements as to the, let's say, compute capabilities and the data management capabilities and the communication capabilities that all are already at the high end of current technology. First, let me start with human brain project. In a nutshell, what is our, let's say, commitment? We want to work towards the unified understanding of the brain and its diseases.  One should remember that this project has been started or is started and is funded through the ICT Directorate, Directorate Connect of the European Commission. And here's a brain-inspired aspect of IT and the neuromorphic aspect. And of course, from my point of view, also the guiding, the future of supercomputing and of big data analytics through the neuroscience community's requirements play an important role. So from a European Commission point of view, it is something very new what they have set up. It's a so-called flagship project. There was a long process to choose from. I remember at the very beginning from about 15, I think it was 49 expressions of interest. They condensed it down to 20 and then to 9 and then to 6 and finally to two flagships. One is graphene. The other one is the human brain project. This is in the new and emerging technologies section of funding of the EU. It's a collaborative research effort. It is interdisciplinary. It's between neuroscience and a lot of other science and engineering fields. It is planned for 10 years, but of course, its impact should be much longer. So it's not just some project. It started out as a research project, but you will see that it has become much more than that. And of course, this discussion of its funding, there is a research project from the side of the EU in the range of 0.5 billion euro. It is expected that another 0.5 billion euro will come from the member states. This is in negotiation and different mechanisms to achieve this under consideration. Today, we have more than 400 scientists from over 100 institutions and many countries, of course. So you see all the logos here. Just to give you the perspective in which this project, and maybe one should not say project, it is more than a project, is developing. So we have three parts. So first of all, the neuroscience and the scientific aspect from the side of neuroscience is the idea to reverse engineer the human brain, of course, through the fact that one has a different focus of consideration, the mouse and the human brain, and of course, also others. But these are the major ones, data integration from all sorts of data, integrating also cognitive and theoretical models in research. Simulation plays a major role, and it is clear that the idea is that simulation is not just to have this fine running and performing human brain, virtual human brain. The idea is much more to use simulation as a research tool to bridge maybe unknown scales between the structure that comes from post-modern brains and functions that might have a much lower resolution through simulation. So one can learn through simulation during this process of understanding that's the idea that we have in the human brain project. So we want to have, secondly, because this is the first aspect, the second very important one is to create a research infrastructure that will serve the needs of neuroscience. And yesterday, Jean-Hill was heading the session on your ideas, also heading the sub-project neuroinformatics, where all the data federation is carried out. There is another sub-project on simulation, and all these sub-projects will transform into infrastructures.  The HPC and Data Analytics sub-project is headed by me and by Thomas Schuldes from Switzerland. So the idea is that we create all these infrastructures. And of course, we cannot just tell people these are those infrastructures that you need. We want to know from the neuroscience which infrastructures are needed. And it was always like this. I'm coming from theoretical particle physics. And I think that this field has very much that the influence, the development of computers in the past having, let's say, very high compute capabilities in order to perform those simulations of quantum chromodynamics and so on. We need, for the future, we need guidelines. And one of those guidelines, certainly, is this developing field of neuroscience. And as we have heard yesterday, many countries, and it is not by chance that all those countries also built and used computers and simulations extensively, like, for instance, the US, of course, but also China, Japan, and Europe, and more, participate in this research. And we can expect very much that this will influence our future direction of computing. Then, of course, the idea that we contribute to new computing technologies by brain-inspired, by neuromorphic computing. There is a neuromorphic computing sub-project that should develop into an infrastructure in the future. Of course, these infrastructures always are in a different stage of development, of maturity. Though the maturity of HPC might be most far progressed today, the other ones will follow. And finally, the idea is to have a neuromorphic facility working for the needs of, let's say, performing calculations for neuroscience problems but also for other type of problems where you can access those facilities by getting granted compute, or I don't know if you should call it compute, by getting granted neuromorphic processing time on those systems. Medical informatics, yesterday we heard this interesting talk by Professor Frakobiak by Richard. And he has given a quite comprehensive overview, also going into technical detail concerning these activities. There is certainly a connection, though if you might have this question, what is the difference between the medical informatics, big data analytics, and what I will talk about? Of course, from the point of view of federation, the medical informatics is a federated activity in the sense that at several hospitals more and more, computing will be done and data repositories will be created. Then, let's say, create anonymized data streams, aggregated data streams that can be used and integrated in larger analysis work more centrally. So this will be the connection. This is something that we will build up and that we will create in the human brain project. And finally, the idea to have these closed loop simulations in the field of virtual robotics, understand that the brain needs a body. The body needs a brain. Really intelligent roboters, of course, need to be steered by intelligent brains. This is a long-term vision, but we start with this already now, and this is the reason we have a virtual robotics subproject. Now, the third point of the human brain project, which was not foreseen when we started. It has become funding intermediary from EU to integrate new groups through open projects. I think that's a very important point. So it's not just that we started with a neuroscience project. Certainly, that's a research part. When it was found, a project of this size necessarily will develop into an infrastructure. There is no way around that. It's just the way such a project has to go and will go. And of course, in order to not to, let's say, being stuck with the people that are in the project to be open to the outside, it has to become, let's say, there has to be introduced a mechanism to create new research within the project. Through, we call it open calls, or there was recently this open expression of interest where four projects will be chosen to, let's say, being part of the project. So they come in into the human brain project as the others before. So this new project is led by Katrin Amunds. And I think this will be a very successful, let's say, next step to, and will show to the outside world how, let's say, flexible this project acts and tries to, let's say, re-humanize itself to become younger and to become forward-looking and focused to the real goal. So this is my view on the project as it is today. We will have these three fields where we concentrate on, but the structure that we have set up in the past certainly will still be reflected within the three parts. Maybe one word to why or how the interest of myself as leading or heading an institute for advanced simulations that's actually the title of the institute I'm in, in Yulisch, with a lot of different type of simulation activities. The reason is we have a sort of funding programs in our super-ordinated organization called Helmholtz Association, named after Helmholtz's famous physicist of the past. So we have here two funding programs that join. One is the program led by Katrin called Decoding the Human Brain. It's at the Foschungszentrum Yulisch Research Center. She is a speaker. And the other one is super-commuting and big data. This is at two big centers, it's Foschungszentrum Yulisch, as well as the Karlsruhe Institute for Technology. I'm the speaker of this program. We came to the conclusion that brain is one joint vision or information processing is at least on this level is a joint vision of both of our programs that should finally lead, first of all, to a better understanding of the human brain organization. And of course, should bring more to computing and big data, especially when we address extreme scale. So we see it from both sides. Why do we need extreme scale computing and how do we do extreme scale computing? So these are the two sides of the metal. And so we decided to go for an allied program partnership that was started with a sort of pilot. This is called super-commuting and modeling for the human brain. So we were generously, but must really say this, funded by our research organization to be part in this activity and to be, let's say, also play a role in the human brain project. And this is our, so to say, our basis for this. And this we want to do, we want to help driving the FET flagship human brain project with this activity. So this is a motivation that comes from our center. So these are people and you see Katten Amunds and many of those you might know, from Augustis Mann, Achse, von Ja Grün, Abigail Morrison, she's responsible for NEST. There are also others from the chairman Cancer Research Center, Roland Eils, from the Munich Center, Wolfgang Wurst, Alan Evans, you all know, Felix Schirmann from the Blue Brain Project, Karl Zilles, Alex Zareel. So and finally to show you a picture of a computer, maybe this is one or two I have. These are big systems. I hope they become smaller in the future by integrating functions and better information processing technologies that we can learn from research that you do. I must say I was extremely impressed yesterday from both science talks.  And of course the large scale brains where I'm more in and learn more and more, this is where we want to go. One can really feel that this is fields that have a huge, let's say, impetus at this time and from all these activities that they have so to say triggered in the countries that I mentioned before, I expect that the impact also on the future of computing will be huge. Let me come now to my second topic. This is going into more detail about what is the high performance analytics and computing platform of the human brain project. What do we do and how do we learn to express what we want to do better and better? So this is not just that we started and that this is it. We are adapting to what is the needs and how all this evolves. I'm extremely optimistic that this project is on a very, very good track and is internally flexible to really adapt to the needs to the Euro science and what is required. What is our mission? And I should say this is a flexible mission. We certainly will not stick in the next 10 years exactly with those statements. These statements will of course be also, let's say, fed back from what research will bring. Of course, building and running the hardware and especially also software, of course, infrastructure in the human brain is one of our most important activities. This is concerning, of course, simulation, but simulation is not just running nest on your own. It is much, much more. It is starting from the Abinizio type of simulations via molecular dynamics by diffusion, reaction type of simulations, to going up to the cellular ones, to the nest, to the point neuron, nest and neuron type of simulations and going up to the theoretical models. Data analytics, data analytics of all aspects I will concentrate on what Yulish is doing in this respect, where I believe is the most important challenge nowadays in order to make progress. And of course, the understanding supported through visualization. Using big computers like a laptop might be the goal and this will be a goal that might also influence much more than just the human brain research or the brain research, since this could be useful for a lot of other type of research. Enable simulations up to the size of the human brain. Of course, many people discuss why is it necessary. There are certain investigations, for instance, from the nest people that tell us that certain type of averaging processes that you need in order to get proper signals require especially for those human brain type of simulations to go to larger and larger systems. There are multi-scales, certainly. It will not just cover one code. There will be several code. We have to close the border lines or to transcend the border lines between the different scales. And there will be, also in one code, there will be from a purely geometrical point of view, there will be different scales. There will be different timescales and so on. It will be massively parallel at an unprecedented level. It will be data intensive at an unprecedented level. We don't have the technology today to do that. So it's just impossible. And as I said before, interactive. We will enable analysis of massive neuroscience data sets. So big data analytic workflows now comes to this issue that many people talk about workflows. But if you go for automatization of analysis, then what's this? Something wrong here? First time in my life that it did this? Just broke down. I don't know. It's just stopping. Never saw that. I have to restart. I don't know what is going on. No. Should not happen. Sorry for that. I hope we can continue without interruption. Workflows that allow you to use, let's say different programs and to go back and forth between different steps of an analysis of, for instance, a registration problem or whatsoever have to be created and have to be made stable. You see stability is a big issue with computers. And we want to push. That's also one of our missions, the co-designs together with neuroscientists in co-design projects. And what is our roadmap? The best way to, let's say, express the roadmap is to the level of maturity we think that the infrastructure will develop. So we are at month 30, we have a first version of the high-performance computing platform. I will show you what this means. What is the ingredients of this platform? What can you do with that? We have the goal within the next 30 months to arrive at a sort of pre-exascale, exascale in both data and data communication and in compute performance and capabilities. From the point of view of simulation, this is a goal we probably have difficulties to achieve to have a sort of 20 petabytes system that would already be able to have lots of neurons integrated and with the compute performance of 50 petaflops all together for exceptional type of simulations, of course, only not, this is not a standard. And this should go on, so in the second phase of the project, end of the second phase, this is the ramp up phase, this is the first SGA, we call it a special grant agreement, special grant agreement. The second phase and the final phase we should be developed the extreme scale and let's call it exascale infrastructure, I'm not sure if it is really important to know the exact number of flops, but it should be an infrastructure that would really enable simulations that are not possible today. And how would we go forward to do that? Of course, we have a goal to serve, first of all in the project, several sub-projects and these sub-projects of course are reflected in the community, so activities in the community. At this time in the ramp up phase, we have one activity that has to do with the future of supercomputers, we call it PCP pre-commercial procurement, it's one of those ideas of the EU, how to let's say foster companies and so, on the other hand it gave us a good opportunity to include the, to convey the needs of the community to companies. So usually companies don't react, but here companies like Gray, like IBM, like Dell, like others reacted strongly on our needs on the molecular dynamic simulation benchmarks on the simulation benchmarks from the cellular simulators, from Neuron, from Nest, to let's say adapt their strategies. So I'm sure that already a little bit, through the small PCP projects, we have influenced the future direction of their research. The second one is important, I will show you results from mathematical methods, usually the mathematics field, it does not appear so much, but if you go further to HPC, every 10% of let's say improvements through new mathematical algorithm and methods is so to say real money that you spare, and programming models as well as optimization technologies adapted to the needs of the neuroscience people, not just free research, it has always the component that we are guided by those requirements. Interactive visualization, especially with examples and with real problems from the field. Management of very large data sets on a computer, integrating them is an important issue. Here we have let's say the direct contact to the medical informatics subprojects through Anastasia Ilamaki, who is a computer scientist working in the field of databases and of very efficient and large databases. Integration and operation of the platform itself is an issue and probably the most important, though that this all works together in several countries, four major sites and a lot of other smaller ones. User support and community building, certainly important. Here we have to contact to our education program and of course it's managed finally also. Interesting is that we are connected to a network that is created by the partnership for advanced computing in Europe by Braves. And as you can see, the major sites that have, let's say the carry the infrastructure, the HPC infrastructure, so here you see Ulish, here you see the EPFL, you have CSES in Lugano, we have the computer in Bologna, Bologna, where is it? I have to see it, this one I guess. Bologna Supercomputer, we have Barcelona and as you can see here the CSES, these are the major sites, but all those sites have satellites. Why is it interesting for us for the human brain project because of the fact that this partnership has already established a European network with global file system support, so data exchange on the level of hundreds of terabytes is possible here and it is very secure, it's all IPsec safe communication, it is pre-configured so there's no routing in the sense of that it is rerouted, it's fixed routes on this network, so also let's say data that might be very sensitive can be transported over this network and as you see it supports global parallel file system, so if you do something on the machine in Bologna, it's very easy let's say to integrate activities on the machines in Ulish and so we will have finally a U-Wide multi-petabyte data repository at our hands for the human brain project. The final word to the main contributors of this work, as you see a lot of people are working in the project, these come of course also mainly from those sites and some of those you might be able to identify as I said before for instance Anastasia is in the project, so this is the site of the human brain project of those people that are doing infrastructure activities to build it up. What is the present status? We have had the first platform release and what is the content of this release? We have now available the supercomputers and this is not a triviality, usually you have no availability of supercomputers over national boundaries, so within the human brain project you can access those computers through proposals that are of course always peer-reviewed, that's a prerequisite, but this is now possible that some member of the project from another country can of course have a proposal in Ulish and the other way around. Using for instance the big system, the blue chain queue that I have shown in the first picture, one of the largest computers worldwide, I think still in the top 10. We have cloud storage available, then we have a system that allows you web-based access, but very fast access to those services and also access that would allow you to use this as a sort of platform that can integrate any type of analysis tool that you would like to integrate. And finally this is a basis also for our own collaboratory software platform that we just created in the human brain project. We have available high fidelity visualization that is not only on site, that can of course be developed and run on visualization workstations or platforms and is let's say compatible to larger systems, to caves or to power walls, if necessary, we do research on the software side with mathematical methods, performance evaluation, programming models, this research is not just that we choose it ourselves, there is an active process together with the neuroscientists. We have established HPV user IDs and single sign-on, those is all for the comfort of the users and we are now providing the basic middleware for our collaboratory to build where everybody that has access to human brain services can have, let's say a very convenient, let's say a portal, a very convenient portal to use all those tools that we are developing for the benefit of the community. And as I said, one other element is this pre-commercial procurement process that is evolved very far that now also influences the course of development of large systems from let's say, potent companies. Now we are also changing this structure of our sub-project somewhat, we adapt to the needs so the simulation technology methods will be instead of the pre-commercial procurement, we will be our work package here that are directly adapted to the needs of codes so several methods that have been developed, in particular the mathematical ones will be done here. We will concentrate on data-intensive supercomputing especially also including the visualization. The other ones are more or less the same but we will also include the simulator engine that's planned, like Ness and Neuron, et cetera. So there will come more in so we believe that through our open projects in the course of time we will have more simulator engines in the project and we will have methods and big data analytics as a new work package since this becomes the major bottleneck, the computational, let's say requirements here become the major bottleneck in research. Let me come now to my third topic, exemplary activities. One is for instance, who answers the question why we need mathematicians. There was a recent work on the inclusion of gap junctions in point Neuron simulation, et cetera in one of the talks there was also the gap junction problem mentioned. The problem in the simulation is it is difficult to include this because you have a time step wise evolution of your spiking activities and the let's say reaction on the gap junction always is a wrong one if you go along the time steps of the simulation because it's not properly resolved. So what have people done so far always has introduced factors of additional complexity and especially of wall clock time. So with the help of mathematicians and you thought of, I call it a wave form relaxation has been introduced. It's an interactive process that can be done easily between the time steps without increasing the wall clock time, let's say not more than maybe 10% or less than 10%. And it has been implemented in Nest. Now Nest contains those accurate handling of the gap junction. So that's one good argument. And we are seeing that in the future there will be many more of such let's say good ideas, new ideas brought in into the simulation software through the mathematicians. Next one, why we do need HPC experts and I mean here HPC in the sense of having optimal implementation of programs. So we have a long history in performance optimization all over Europe. So this is in Barcelona. We have very good experts in English. We have experts in all the other countries as well. And one or two codes that we are developing to understand performance bottlenecks are for instance Paraware. This is good for the local type of performance optimization and Scalaska. It is a scalable optimization tool. We have made found massive improvements for Neuron and Nest. And now if you grant supercomputing time with Neuron or with Nest, let's say in the range of a million Euro per year today maybe that's one or two million Euro per year if you improve by a factor of two either you spare or you have much better opportunity to use the codes. That's a very well invested activity because it also will help us in the future. That's the reason why we need those people in the project. Why do we need computer scientists? We need computer scientists because this project is huge. You cannot have particular solutions for everybody. You need unified solutions in order to give everybody the opportunity to let's say access any of the tools. This goes through a basic software we call Unicor. That's probably the only safe. Comes from the time of the grid. Access software of computers and supercomputers and all our big systems can be accessed through Unicor. You don't feel Unicor because it's more or less middleware in the context of what we do. Finally, users will go through a collaboratory. This does not mean that they are forced into, let's say you're going just one path. Finally, they can also go through the collaboratory and do an SSH connection to a computer. But it goes through a mechanism that is inherently safe and will not, let's say, be compromised. Nobody will be able to compromise access or anything on this system. And of course, then through the collaboratory, you will have access to all of those services that we will offer on the system. So that's the reason why we need computer scientists in the project to help us. And why do we need visualization specialists? We'll show you just with some examples. So we have simulation of neural networks interactive exploration, details, navigation of neural networks, abstract presentations, exploration of relational data, large volume data sets navigation in physiological data, pipelines and architectures. And of course, I said before, high fidelity platforms that start with simulations of neural networks. So one of those codes is the RT neuron. Some are integrated from other sources, some we develop ourselves in the project. So there is, I'm not an expert here, but those who are experts, we'll see what you can do with those types of visualization. This goes on for a while, I shortcut it here. If you want to have it, you can get it from me. This other one is, for instance, a tool that has been developed for nest simulations. So you see here the visual system, I don't know from what, but Katrin might know. Where you can look into, you can have details that you can also, let's say, show in table form at any stage of a simulation in an interactive manner. Then you have navigation in neural networks. So there's a neural scheme, you should say, that is an abstract representation. This can be used later in connection, for instance, with RT neuron to get several parts of a network, let's say highlighted. You have a form of pre-like representation of the network. This is neural scheme. One can explore a relational data. So you go from different compartments, you see which connections go to others. This is also in connection with nest simulation and neuron simulation. There is interpretation of very large volumetric data. We have also one example, there are several others, Levera, for instance. So you can go up to, I think, gigabytes of data that you can handle with Levera in a parallel manner. The same is Paraware. I know this is a PLI. In the context of the polarized light imaging activities at Ulish, together with the Aachen people, there is navigation in morphological and physiological data. I think this has been included from outside source into our tools, so these are all tools that you can get through the collaboratory and can use for your activities. Let's go on. There can be combinations, so you have on the one hand RT neuron and the neural scheme. So you can project to regions of interest through your navigator that you have on the right hand side and can project out certain activities on the networks. There is Paraware, where you can look into very large data sets as I run on the system. This means it's not going through a bottleneck. You don't remove the data from the machine. You only see the visualization. And you have high validity platforms. Of course, if you go to Exascale, it's not possible to get the data read out. The IO will not be possible then anymore. And caves, you know, all I don't need to show you caves. And you might know how to use them. Of course, for larger audiences, it's also important to have display walls. All this technology is compatible to the usage on your laptop as well as on more visualization, CD visualization workstations. Okay, this is more the technology. Of course, we also need good teachers. We do a lot of workshops, education workshops and summer schools. Yeah, I've had some of those, as you can see. And several locations have become others. We have planned a human brain project PhD curriculum with five syllabi, ICT for non-specialists, brain medicine for non-specialists, neuroscience for non-specialists, research ethics and societal impact, intellectual property rights, translation and exploitation of research. And we have a female coaching and mentoring program. You can find the training courses and the workshops on the web. So you see a lot of activities are going on in this human brain project. And as you can see, everything is existing in the HPC and analytics platform. But as I said, this is how we started and this is a work of one year. Now the question is, can we really develop the HPC activities and analytics activities for the benefit of neuroscience as well as all the other type of infrastructures to world-class infrastructure. And the idea is to go through co-design projects here. We can just sketch the four planned current co-design projects.  So a simplified model simulation of the whole organ initially based on the nest code. So you see this will be exactly the point of contact with the performance analytics and computing platform. The source will be the multilevel mouse brain atlas. We will involve SP5 in neuroinformatics as well as the mouse data and finally closed loop neuro-robotic experiments have to be performed here. So also SP10 will be involved. So there's a huge interaction in the project through this co-design and it will influence the different sub-projects, especially also neuroinformatics as well as simulation. Second one is mouse-based cellular cortical and sub-cortical micro-circuit models. Here we will not use nest. Here we will use neuron as a tool to integrate morphological and physiological data. Again, simulation will be important. This is actually SP6. Mouse data will be important. SP5 will be important. Of course, also the neuroinformatics platform and of course the simulation of micro-circuits on supercomputers again will involve the performance computing platform and influence its direction. Third one is multi-level human brain atlas. Now we come to the structure, structure, productivity and functions. So this will be human brain data. This is our sub-project two led by Katrin. Prototype of the human brain atlas is to be created or is created in the neuroinformatics sub-project and the efficient HPC supported big data workflows will influence activities in the HPC and analytics platform. The fourth one is visual motor integration in the human brain. So the idea is to use multi-model top-down models of sensory motor integration with a focus on the somatosensory and auditory system. Again, HP human brain data will be important. The theory will be involved. Cognitive will be involved. This has to be determined and to be defined because it's now built up through our last open call. A match to the bottom-up simulations is planned. Here simulation will be involved HPC again and of course again the feedback will be given and will be involving SP-10s robotics. These are the pre-designed projects by which we try now to go a step further in adapting the platforms from the inside of the human brain project to the needs of the community. The next step on top of that will be that we want to have co-designed project with the outside world. So this comes. So this is now the first step. We will see what will be our experience with co-design and then we'll come to co-design project with community as a next step in order to further adapt the infrastructure to the needs of the full community and not just of the inner part of the project. Now let me come to answer the question finally that I posed at the beginning, extreme scale challenges. Some extreme scale challenges. Of course here I can more or less only recourse to those where I'm confronted with directly. So let me start with cellular architecture at different spatial choices or plots courtesy to the people of Catherine and of herself. You'll see if you have a resolution of half a millimeter what you can see and this is not very much if you compare it to one micron. It seems at least just from the visual inspection of this that one will finally be forced to go to one micron level of resolution and beyond in order to see all the details or all the fine scale structures. Of course one can discuss if all this has to be incorporated in simulation. This is not a point, but from the point of view of experimental research on yesterday I very much liked the citation that was made I don't know by whom from Freeman Dyson. Actually Freeman Dyson is a theoretical particle physics again guy and a very famous one. He invented quantum electrodynamics technology but instead of saying I'm the great guy and I invented a theoretical method he said what I invented in some sense he said what I invented was just to explain things that existed but the real things are the new ones that you find through experiment and the better you resolve certainly the more new land you will find. Just to see this more from the abstract point of view scales, instruments and structures. So if you have problems that you want to address though in vivo and postmortem of course then you need the yellow part you need different type of technologies in order to get results and these different type of technologies at some stage come to the one micrometer level and compare one micrometer with 20 micrometers. Since we live in a 3D world this means a factor nearly 8,000 it's nearly 10,000, 10 to the 4 on just on the poor number of data that you have to handle. That's difficult. This is if you go to large scale networks if you have smaller problems systems you don't have these problems today maybe if you do specific type of analysis you might have also very high requirements on data handling and computation. Yeah, going on. Let's discuss a little bit the image registration problem. So the cellular architecture but you can see here a movie from the big brain this is on the 20 micrometer level recreating a 3D picture from a 2D slices. This is interesting that it is possible at all in fact that you cannot distinguish between the different directions after reconstruction. It was also given an award the MIT technology review one of the 10 technological breakthrough in 2014 that was a very high recognition for the neuroscience research of this kind. The problem was it was a 10 years effort to create and of course cutting can tell you much better. I will come to the automatization here to create the first big brain. Now the group tries to do this in a more automatized manner and using parallel computing technologies and methods to analyze this. So things are distributed and you have workflows that you can go back and forth in the different levels if necessary you can also manually interact here. So the complete pipeline you should not need to see the details only the complexity of the complete pipeline of such type of analysis with all the let's say feedback loops that you need in order to do this properly. This is done on a machine and I will come just to complexity here just to tell you the story why you need HPC. This is done on a machine you could do it on a quite small machine in fact. I can show you why this is a machine that they use. It was installed in 2009 called Europa an eight core per node system with a lot of teraflops three under 10 teraflops like quite a large memory it was complimented to another system so this is actually one system but this does not play a role. All together I think 3,000 nodes that work on this system more than 3,000 nodes.  This is on the 20 micrometer level and I told you factor 10,000 going to the one micrometer level. This was a typical job size I already told you 150 of the system was used in order to perform 512 parallel evaluations of the slices. Now we go down to one micrometer we have this confocal laser scanner that big tiff images 30 gigabytes per slice one petabyte per brain. You can go to single cell analysis but what does it mean? First of all, we have a new machine now and let's now compare this in terms of the new machine the big system 45,000 cores it is a general purpose cluster computer it is not a high performance computer as let's say a blue gene it's a system that allows to do any type of research but with a very fast parallel communication system very fast access to parallel file system one node has in general 128 gigabytes of main memory it can also have 256 and 512 but only a few have that. So if you go on this system you would have access to 281 terabyte of memory and 2.245 petaflops of peak and now let's do the comparison. So working on the 20 millimeter slices actually the analysis could go only to 40 micrometers there was a reason for that on the previous system so big brain two at this stage was only done under 40 micrometer level will go down to 20 micrometers because it was hampered by local memory 512 cores were used in one analysis part run so eight cores per node altogether they used only 64 nodes this means 150 of the machine and the reason for that was the core memory of the Europa was exhausted and 512 slices exactly where the number of slices you need to go through and see how the different let's say distortions work so this was quite convenient for the guy who operated the system to work on. Now go down to one micrometer the actual resolution has been 20 micrometers going down to one micrometer means the factor of 400 in a 2D slice one section then require 600 gigabyte and the memory of the Choreca is 5.3 per core of course you have the other ones but only takes the first one 5.3 so this means one section alone requires 110 cores this means on this smaller memory five nodes so you have to go to several levels of parallelization in order to do it otherwise you cannot do it that's the problem one section we don't talk about one section we talk about more sections we would have all sections actually the all sections would be in 10,240 virtual sections on the one micrometer level but only 400 sections saturate the full memory of the machines of 281 terabyte okay this is not much if you would like more you would like to have more sections in real physical distance 512 times 20 micrometer would be the real physical distance you would like you cannot do that because in one run only 400 sections fill the full machine and the compute time finally will be you have to do this 25 times because then only you get the full brain you would need 25 times the full system so you have 2500 times more CPU hours that you need and of course the cost of computing will dominate the whole research that's what happens here let's go on image analysis yes go on image registration to the fiber object that's what we have learned the cost of computing will dominate the research can we do better we can do certainly better if we include maybe GPUs on this machine this will be done in the next step and we believe that this can help but actually it was dominated by memory and it was not dominated by compute so this one should remember polarized light imaging those are the idea to get the fibers disentangled current resolution 60 micrometers times 60 micrometers scan time 15 minutes per section the resolution one would like 1.6 micrometer times 1.6 micrometers scan time six hours per section this is also a limitation that is given today and actually the system to read out is much too slow so we will expect improvements on that in the future but this is the first bottleneck what is actually in the registration of course again here you need to do let's say manual or originally manual type of changes what you would like is an automatized process and this can be done by simultaneous registration and comparison to the reference pictures that you have from the block phase image so you have these are 1,700 images here and you see this now we do on GPUs we have 400 GPUs the machine is called CHATS Julia GPU system you have 20 hours of computation to do that so these are typical processes for instance segmentation that are done in an automatized way by seeded region growing also but this is not what I want to show you what I want to show you is the improvement in segmentation efficiency so if you look at the run time of such a code and if you take one CPU only so we are here the run time will be 290 days in order to do this but you can do better of course you can use more CPUs so run time goes down and this is all highly optimized this is not just a bad implementation you go to GPUs you gain a big factor of 20 and you maintain the factor and this communication does not play much a role here actually you start from 295 days of computation going to the GPUs alone reduces this down to 15 days this is just relatively let's say not interesting if you have such large run time it will not work you need let's say hours you can come down to hours so the idea is to go with CPUs to 5 days with GPUs to 5.6 hours starting from 295 days so that's the improvement that people could get and making this a viable tool and now from these 5.6 hours through better GPUs and usage of GPUs they can go down to 1.2 hours of this analysis that's why you need HPC in the field of this polarized light imaging and in fact this is not the only thing the other one is that of course with the polarizing microscope finally you need about 4 petabytes per brain again this is not feasible today in reality we have data repositories of 20 petabytes online yes but this is at the limits of current technology so maybe in 2 or 4 to 5 years this will be really possible and we are on the way to do this so we are at the limits of next generation HPC technology now don't know how we can do better another fact.10 seems impossible to me today maybe one has to become more effective but at this stage we know 1 micrometer is reachable feasible through HPC technology this is the last let's say the challenge I want to pose is on the simulation again you have many scales and this means you have different type of problems molecular dynamics problems reaction diffusion problems neuron based and mean field type of problems so for instance a subcellular proteomics neurotransmission receptor cellular connectome functional cognitive models and different type of codes these are exemplary codes chromax is well known, steps is well known neuron nest and of course there are special solutions upstairs if we count let's say the memory required to let's say go through a full neuron for instance on this level it's very large we see 1 megabyte per neuron for the code neuron 0.1 megabyte per neuron for nest this determines let's say the large system this determines more or less what we can do on current supercomputers concerning molecular dynamic simulations one of those examples is for instance these multiscale activities done by Paolo Caloni here is looking at kinase signal transduction you know this is very important type of code say it was a Nobel Prize two years ago for the let's say technology that has been invented here in physics what they do they do a multi-level type of approach and he is working here on the Abinicio type of simulation we have to provide the machines for that we do this and of course it becomes increasingly easier to simulate so this is not done on supercomputers these are small scale machines but here you need the largest systems available this is done on the u-queen blue chain machine that we have in eulish another type of simulation is done through nest so you see again the different problem elements involved if they want to do a one centimeter point neuron region in the cortex it covers already 10 to the 11 neurons we stand to the fourth synopsis panneuron and you see of course they include also long range connections at least they simulate or emulate those long range connections with nest and finally they visualize it online the requirements for that as I said 0.1 megabyte panneuron and going back here means 10 to the 11.1 megabyte is a lot they made scaling experiments some time ago already can scale very far on several types of systems for instance on the k-computer as well on the blue chain computer in eulish on u-queen meanwhile we have better data points we can go up even further here so what you see is nest is memory bound it's not compute bound and today the point neurons one can use can get to 1% of the actual number of neurons of a of a human brain whatever it means so this is just in order to understand what will be possible at all there is also from the computer science point of view they have monitored the development of the software this is something that's very interesting to see how much work and different versions codes and code elements over time go into that if you are interested it would run for 5 minutes and you see also branching of codes and branching to new directions the different elements they have and so on it's just from the point of view what does it mean to conduct such a huge effort in software development it's quite fascinating I stop it because it does not give you new information now let's come to the final slide the memory challenge this is just a rough estimate if we take our present computers and if we want to go further we have rodent brains and we want to touch the human brain we know there is this region to cover and we think current systems that we have planned HBP systems have to go this roadmap finally and though we will end up certainly in the range of 20 petabyte for the next step and we should touch 100 petabyte in 2022-2023 it's unclear if it's possible this is one of the other research aspects of the HPC and analytics platform to get hierarchical memory technologies realized on the system so you see we believe that we can touch that and can provide machines but this will require innovation and it will lead to innovation in HPC and computing I hope that what this guy said is not true that it will take several hundred years to let's say disentangle what the human brain is with the current efforts I believe one might be much faster and I wish you all the best to achieve that and I hope that we can help a little bit thank you very much


Here we go. Good evening. Good afternoon. Good morning, everybody. This is August 18th, GSOC office hour, global one. Hello everybody joining. And let's start. So the first point I wanted to discuss or to raise was that I think now is a good moment that the projects are advancing very well and we're slowly reaching the end of this campaign is that it would be a very good idea that every project would write a blog post on the Jenkins IO community blog to describe in short format what the project is about to give some explanation about the project. This would be a compliment to the Jenkins online meetup we had some time ago. I have some suggestion for the topics. So 500 words depends how you count them. This is about a page, a page and a half maximum. And the besides describing what the project does, it would be interesting to explain in a couple of words why the project is important for the Jenkins community or what it brings to the community. And another topic idea is why is it or was it important to you as a contributor. So a couple of words what you learned or what it is what you're learning right now as we're still busy with that with the projects. The purpose of that is it's a way to communicate and explain a couple of things to future GSOC candidates because we start to have people interested in the next GSOC campaign.  Are there questions, clarifications regarding this recommended blog post? Yeah, I do. Should the contributors include the coked products as well? I didn't catch that again. Okay, should the contributors include links to the coked products as well in the blog post? Why not? Why not? Everything that could be useful for the community and the passerby to learn more. So to go deeper. We don't want to scare the people by having three or four pages to wade through to understand what it's talked to, but it's always a good idea to have. This is what I want to say and here are links to further discussions or documentation or to look at the code or things like that. Good point. Okay, for how to proceed, what is the magic required for proposing a blog post on Jenkins.io? Your mentors can help you with that. There's also the contributing page on the Jenkins.io page or repository. Other questions or comments? Okay, good. We have two projects being present here, three, which is third one. Oh, who's she cash? Okay, good evening. So we have three projects and we'll start with Vian. Vian, tell us, where are you standing? What you achieved and where are you heading to? So the usual roundup? Yeah. Hi, everyone. So this week, I was able to create a draft build request for the changes that I proposed last week and the changes are getting built nicely. So no errors with that. And for the draft build request, you're also planning to add the instructions for the review to be so that I've done today. And along with that, we also throw in some tests that will test the new processing layer. And that would basically sum up the entire build request and then it would be merged. And after that, we plan to add the last feature that would be to get the plugin names from which each parameter of a step is coming. So for example, if we have an SCM, we have something like it SCM. So once the user clicks on that, so now it will be a clickable link, which leads to a new page. So once we click on that, the user should see that, okay, yeah, this parameter, this class is coming from the good plugin. So we are working around the ways to do that. But other than that, I think as far as our goals are concerned, we are pretty much on our track and we're almost done. Okay, good. Sounds great, Fian. Happy to hear that. Next project is yimming with his Jenkins file runner action for GitHub. Tell us what's happening on your project. So recently, I worked mostly about my documentations and use for examples. So firstly, I created the documentation demos and detailed the plane and how I will develop with my documentations, which includes user guide and developer guide or something similar. So because we decided to host all the documentations in the GitHub pages, so I need to write a detailed plane and a demo and about how I want to implement it. So secondly, I created several useful examples for the users, so they can get started in our actions quickly. So it is those examples are stored in another demo project. That's my work recently. Okay, and no impediments. I think everything is under control for your project. That's very good. Okay, that sounds good. Thank you for the update, Yimming. The last one we have, unless somebody else sneaks into the meeting, I'm teasing you, Koshikash. So it's your turn. Tell us what did you achieve, what you're working on, what is brewing on your site? This week, I worked on persisting the maintenance records into an XML file. So after each maintenance task is run on a cache, I store all that data into an XML file on the Jenkins controller. After persisting the data into an XML file, I also created a table. I also created a table to display the maintenance records. So this table contains pagination, sorting, and also a search functionality. So that's what I've been, I worked the previous week. I'll have to improve the performance of how things are being done because behind this was only a way to connect things and integrate and finish the functionality. But behind the scenes, the performance isn't that great. So I'll have to create, think of a way to improve performance and improve the overall functionality of this feature. Jumping on that, Koshikash, a good advice, and I hope you will remember that, is when you're working on something like that, first, implement the feature. Note that the performance is not as expected and then only work on improving fine tuning and eventually refactoring the code to improve performance. It's a pitfall to start while implementing the feature to start immediately working on tricks or thoughts on how to improve performance and speed. Believe my long experience. And you're doing this on that way. Yeah, this is something I have to consider because I spend the whole two days just reading about how things, how do I improve performance rather than completing the functionality. So yeah, this is something I have to think of. It's a lesson and you experience that and think about that tip. But try to do it sequentially. It's a general advice that's very often given. People get lost in trying to improve performance while they're still building and they lose oversight. Just a small tip based on my experience. Do you want to add something else Hoshi Cash? Nothing. No, it sounds good too. So I'm very happy to hear these updates. Well done. Congratulations, everybody. Vihan shared the link to the contributing document of Jenkins.io and how to add the blog post. And I added it to the meeting notes here. So the link is available there. Is there another subject, question, comment that somebody would like to discuss or raise? Nothing from my side. Thanks. No, okay. I've done, so let's do the countdown. Three, two, one, no subject. So enjoy your week, wishing you a lot of progress and a lot of fun. Bye-bye everybody then. Thanks everyone. Bye-bye.


All right. Thank you, everyone. Please take a seat. Good morning, everyone. Thank you for being here with us this time at the COSHA Opening Press Conference. My name is Phon Matarot, head of the media at the World Economic Forum. I'm very honored to be the person to introduce you to our seven outstanding COSHAs here. We will start in this order, in terms of the speaking order as well. So, we have the Prime Minister of Norway, Erna Soberg, Shetna Sinha, Founder and Chair, Mandership Foundation from India, Jeannie Rometti, Chairman, President and CEO of IBM Corporation, Christian Lagarde, Managing Director, IMF, Isabel Korsche, CEO and Chief Group, France, Fabiola Genotti, Director General CERN, or European Organization for Nuclear Research, I think. Sharon Burrow, General Secretary, International Trade Union, Confederation, ITUC, in Belgium. Thank you, everyone. We would start already with the Prime Minister. Shall we ask your hope and aspiration for the meeting? First, I'd like to thank for the opportunity to co-chair the whole conference. It's, of course, an honor. Also, it's an honor to co-chair with so great ladies as this, not just because they are ladies, but also because they are representing a cross-sector of our societies in the world, and all of the driving forces that we need to have behind our reach for the global goals. If I can highlight two areas where I would like to have a focus during these days of discussions, and I think it's vital for reaching the global goals, the STGs in the future, it's number one, corruption, illegal financial flows. I think that's one of the biggest challenges we have. If we are going to have a sustainable development in the world, we need to talk about, we need to focus on, and we need to work against all type of illegal flows of finance and of corruption. This is what fuels conflict, this fuels terrorism, this fuels security threats, but it also undermines the possibilities for national states around the world to, in fact, tax make it possible to achieve social goals for their societies. That's why I think a focus on corruption and illegal financial flows are one of the most important. At a week where business meets politicians and NGOs and decision makers of the world, I think we have to say that this is also what will undermine inclusive growth in a lot of countries if we can't combat it. There is a lot of money in this world that could go to development if we had been able to fund them into the organized channels. And in these days where we discuss MeToo on all levels, I think we would need a sort of a MeToo campaign on corruption and illegal flows because we think we need to see and show who is taking money, who is bribing others, and how can we make sure that this is unacceptable in all of our societies. Promoting peaceful, joint and inclusive societies is SDG 16. It's a key to achieve all of the other goals that we have. And I think it's important in context where society meets decision makers, where business meets decision makers to talk about this. If we want to do something about climate change, it's also important to do it. My second issue, of course, is gender equality, which I think is represented by this panel, but it's also, which is not very, well, it's very unequal, but it's, on the other hand, an important manifestation of women into a lot of very important parts of our societies. Still getting women into paid work simply means that we are using the entire pool of talent in our societies. The barriers to women's participation is still large in many societies. And denying women equal rights has no valid ground for the future. And it doesn't make sense economically or politically. So education is the first step. I work hard on making sure that we, through the focus on the SDGs, get education to all, both boys and girls, but especially make sure that girls are fulfilling 12 years of secondary education so that they have a basis to start their life on. And I think if you want to discuss inclusive societies, I think that Nova was pointed out as number one on the list of the Wafia last night, but I think if you want to start education, it's a starting point, because that's when you groom the talent of all young people. So that's my two-focus area. Thank you. Thank you very much, Prime Minister Shadna. You have something, what is your objective and goal for this meeting? Thanks. I'm honored to be a co-chair at World Economic Forum, and I'm proud to be a part of this accomplished group. I represent the voices of a fractured world, voices of the rural women in India, some of them really poor. And 20 years back, when many of our women wanted to do, wanted to plan their lives, wanted to open a saving account, banks were not ready to open their accounts. Why? Because they are not affordable client. And so our women said that we want a safe place to saving, safe and secure place. And that's the reason we started a Mandeshi Bank, a first rural women bank in India. And this bank provides saving, lending. We were the first bank to launch the pension fund for rural women. And when women got the banking, they said we want access to knowledge, we want access to finance. So we started the first business school for rural women. Imagine Indian women, rural women going to business school. And so our bank was also providing the doorstep banking. While working with women for nearly a half million women for last three decades, I want to share the lessons I've learned from them. The first lesson I learned is never provide poor solutions to poor people. Our women are running bank, our women are getting doorstep banking. They are running radio. So I again emphasize that I learnt was never provide poor solutions to poor people. Microfinance industry is big in India. It's a huge, it's a 20 billion dollar industry. Why this microfinance was successful? Because women took the loan, repaid in time and they proved that we are bankable. They proved that there is a business in that. But now our women are looking beyond power. They are just not looking only getting beyond out of a poverty, but they want to go towards prosperity. So from microfinance to microenterprise. And our women who have created established bank with no outside support, no external support are all set to set up, start an alternative investment fund. You have something to announce here.  Entrepreneurs, it's 100 million dollar, 100 million rupees fund. But why am I launching in Davos? There are two reasons. One, Davos is the place where head of the states, business leaders, venture capitalists, impact investors and social entrepreneurs come. The second reason, Davos is all about passion. Davos is all about innovation. And our women, this is the most innovative fund in India. It's first, which is dedicated to our women entrepreneurs. And I would just like to urge you to see the business opportunity in our women. You know what they say? They say that we talk about finance and capital. Our women say that my courage is my capital. I would invite you all to join me in this fund, engage with our women, partner with our women, and invest in our women. They are amazing. Thank you very much. There you go. Thank you. Mrs. Rometti. Please. Thank you. And like the others, I'm quite honored to do this. And I must say quite humbled to sit next to a woman like Chetna with what she's accomplished there. So I think that's wonderful what you just announced. Wonderful. So what you asked us is our goal. And my goal out of this would be to have all companies adopt what I would call responsible stewardship for new technologies. And so let me frame that. I think the world's at an inflection point, be it for a company, an industry, for society around many of the new technologies. And on one hand, they are the opportunity to solve what I think are some of the most unsolvable problems in the world. We were just talking about in India itself, one oncologist for 1,400 patients, you've got to find a way to address all these issues. On the other hand, what the Prime Minister just mentioned, I think this could also be the issue of our time. And the issue meaning many people talk about, will there be jobs displaced? I don't think the number of jobs is the issue. The point is that 100% of all jobs will be impacted in some way. It will be man and machine working. And you can't create a world of have and have nots. And so when I talk about adopting a responsible stewardship, I would say and advocate three things. The first is that all of us usher in these new technologies with purpose and transparency. So just take artificial intelligence. Purpose meaning it is man and machine. It is make man better. It's augment man. And when you say transparency, we all have a right to know, well what train that? What data? What people? Before you're going to give me advice back. The second thing would be that we all really do live by a set of data principles. And those data principles would be around ownership, the free flow of data. They would be around privacy, around security. They would also be around government access in what can and cannot happen there. And then the third thing I'd advocate is that we really prepare the world's workers. As I said, 100% of all jobs will change. Our lives will change. So for our youth, that means not everyone will have a science degree or an advanced degree. And there are many ways to bring, we coined something called new collar to allow the youth with just a little education extra to participate in that workforce. It means retraining and for governments it will mean lifelong learning and a changing of how we look at education. And then of course all of this underpinned by inclusion. Because in the end, I believe we'll all be judged by society and they'll determine who they trust if we live by these kinds of all of these principles. Thank you very much. Mrs. Lacarte. Well thank you very much. And I'm also delighted to be in that group of prominent talented women. And my hope is that we can collectively demonstrate, as has already begun actually, that even without testosterone, we can actually produce positive, constructive energy to deliver solutions. That's my goal. And I think the two key objectives that we have, now that from an economic point of view we are in a sweet spot. That's where we are now. We can actually focus on shared responsibilities and shared opportunities. And what I mean by shared opportunities is first of all empower women and turn this anger that we have seen into action and that we have to do together, both men and women. And that ranges from what we have seen and that has agitated the media to very trivial issues that are critically important. Sanitation in some countries. Physical violence against women. Legal obstacles on their way to work and to finance. The second one which is key as well is to empower the young people. We will be launching tomorrow's study that actually compares the status of young people today relative to other people in their respective countries. And you will be surprised to see that even in those countries where there are no massive excessively and growing inequalities, within generation we have an issue that we really need to focus on and that goes through education and inclusion. Turning to my second one, which is the issue of shared responsibilities. Erna, I join the Me Too that you launch because the anti-corruption movement is something that is in our view critically important. It's a difficult issue, not one that we should tackle either lightly or quickly. We need to really focus on that but it's critically important. Those shared responsibilities applies to the areas of climate change, corruption, of excessive inequalities developing in many countries and on that front certainly the IMF with its 189 membership is going to try to also lead the charge and participate in those projects. Thank you. Thank you. Mrs. Koshia please. Good morning. I'm very honoured Me Too to participate in this inspiring panel. My goal is to contribute to something which is in my view absolutely necessary and urgent which is to accelerate the pace of change towards a more harmonious progress. As a business leader I witness every day the fact that there is a strong impatience. Really a stronger awareness that the current models of our society is unsustainable, is not inclusive, is even not inspiring. And I'm very impressed by the contradiction between the level of a viable money which is unprecedented and the fact that our growth is so slow. So not inspiring. And I feel a strong desire, a very shared desire to invent something different that would reconcile, reconcile economic growth, economic development and common good that the famous are the same time, the favourite words of our French president. Because people they refuse now to choose between the two. The second thing I'd like to witness is that over the last decade we experienced the emergence of a lot of new technologies that effectively are unanswered. As an energy player I can tell you that in the energy field it is spectacular. Renewable technology they are now on par with for science beating them in now a number of geographies and not only they are clean but they are agile, flexible, rapid to implement. And it means that they are also a way to accelerate access to energy which is a way to reduce gap of poverty between the regions of the world. And what I'm just saying is that the energy field but not the only one is typically a case where we can now focus our technologies, business models on solutions that not only limit the damages but repair, heal the fractures. So I lead a group that has decided to deliberately go to that direction. It's not an easy road at all. It requires a strong orchestration. It requires strong choices also. We dispose 20% of our activities to refocus, reinvest, redirect our resources to new business models that are effectively able to reconcile. And we started experiencing the fact that we increased our pace of growth and our value creation level. Now the bad news is that the pace of change is alarmingly low and then it's necessary to accelerate. And in my view one of the main issue is to massively direct flow of money towards harmonious progress. So my expectation is mainly this one for this week to identify the road blockers and to systematically deal with them. Thank you very much. Mr. Cianotti, please. Good morning. I'm also very pleased and honored to be part of such a distinguished panel of co-chairs and I'm also grateful to Klaus Schwab and his team for choosing a scientist among them. So surprise, surprise, my goal for this Davos forum is that people become increasingly aware of the role of science to address societal challenges. So I would like to anticipate and share with you a couple of thoughts that we will develop more in the discussion in the coming days and even today in our plenary panel. Science can play a key role in connecting people and creating a shared future in a fractured world because it is universal and unifying. Universal because it's based on objective facts and not on opinions. An example, simple example. An apple falls in the same way whether it falls in Isaac Newton's garden in the England of the 17th century or anywhere else on earth in any time of history. Unifying because the quest for knowledge and the passion for learning for understanding are shared values and aspiration of all humanity. Scientific knowledge has no passport, no gender, no race, no political party. And I would like to bring my own experience. I work at CERN, the European Laboratory for Particle Physics, where we run the most powerful accelerators on earth to understand the fundamental constituents of the universe. And CERN brings together 17,000 scientists from all over the world representing more than 100 nationalities. Some of them come from countries that are in conflict. And yet they all work together peacefully, animated by the same passion for knowledge in pursuit of common goals. Certainly places like CERN and other research institutions cannot directly solve geopolitical conflicts. But they can help break down barriers. They can help grow the young generation in a respectful and tolerant environment that values diversity. I think places like CERN and other research institutions can plant seeds of peace. Second, scientific knowledge is the fuel of progress because it pushes back the limit of what we know. And of course progress is fundamental for the future of society. Without innovative ideas and breakthroughs from scientific research, progress sooner or later stagnates. Another example, the light in this room is not just the results of the smooth evolution of the candle. It's not by building more colorful and bigger candles that you get this light. It requires a genuine jump and a qualitative jump in the knowledge. And history often shows that often major breakthroughs come from fundamental research. Think of quantum mechanics and relativity, two big discoveries of last centuries. When they were discovered, they were developed, they were considered useless knowledge. So abstract and so far away from any practical use. And yet without quantum mechanics, modern electronics, based on transistors, will not exist. And without relativity, our GPS will not work. So I cannot emphasize more the importance of fundamental research and applied research, scientific research in general, for the progress of humanity and the need for strong financial support by the governments. Thank you very much. Sharon? Well thank you and I applaud Klaus Schwab for his courage to send a strong message with an all women panel and I'm delighted to bring the voice of labor to that. I have a simple message. The world needs to negotiate a new social contract. We need to change the rules. Eighty-five percent of the world's people in our global polling tell us they want the rules of the economy, the global economy rewritten. Why? Because they're the people dealing with the historic inequality, the global wages slump, the massive unemployment, the deep anxiety about jobs for themselves and for their children. And indeed of course it's women and women workers who face the consequences of misogyny, of sexual harassment, of incredible levels of violence against women in our societies and in our workplaces. And climate devastation, again it's working people who are on the front lines of both the loss of lives and livelihoods as seasons change or you have massive events. We know that if we're going to heal today's world of work and that's what the economy is based on, sadly as much as the CEOs are important and the investors are important, without working people you simply wouldn't have an economy. And it's today's working people who are actually a part and parcel of a global workforce in trouble. We need to heal the global workforce of today if we're going to deal then on a strong foundation with Industry 4.0 and the promise and the threats of technology and of course of climate imperative for industry transition. We need just transitions in those fields so you have to get the fundamentals right and it does require a new social model, a new economic model where we take the strength of today that works for everybody and we rid ourselves of the greed that's dominated the worst of it. I would say on technology we want to see four things. We want to see human deployment of technology, absolute remaining control of human deployment of technology, human control of data commons, human mediation and we want to see human rights and labour rights. This is the 70th year, the anniversary of the UN Declaration of Human Rights. If people and humanity mean anything we need these deployed in to the social contract that will see us build a secure future. I would simply say that I think Klaus and the WEF have it right. If we are not prepared to stand up and negotiate collective solutions, cooperative solutions together to build the future then we will reap more of the same. Human beings bought us this fractured world. We built this fractured world. We can now say we need to learn some lessons and rebuild on the basis of a common humanity, a common solidarity where the economy does really serve people and our planet. Of course to creating a child future. So we will take a few questions. We don't have too much time so I can see. Can we go with the ladies first? Good morning ma'am. My question is with Madam Lagarde. Can you also say your name and your organisation? Vasundra from All India Radio, Country's Public Service Radio Broadcaster. My question is that IMF have termed India's Indian economy as the fastest growing one. So according to you, what is the reason behind the changing perceptions about the Indian economy? And question number two, Prime Minister Narendra Modi is going to deliver a speech in a short while from now. So what is your expectation from this speech? Thank you. So forecast growth for India is 7.4% coming. It is indeed of the largest, all largest economies, the fastest growing in the world at the moment. We certainly hope that the development, continued implementation of reforms in India, the focus on the financial sector, the implementation of massive changes that are taking place in India will continue to be supported and become sustainable. I personally hope very much that there will be a focus on the inclusion of Indian women in the economy. We have published research that shows that if Indian women were participating at the same level as Indian men in the economy, which of course is a bit of a myth, but it's the way you calculate those things, that Indian economy would increase by 27%. So that is clearly something that needs to happen. The level of discrimination, the roadblocks that Indian women in particular face in the rural areas are phenomenal. And I applaud Shetna for actually being very active on that front. Ladies and gentlemen, I would just tell you that Mrs. Lagar has mentioned that the panel of coaches is testosterone less. What will be your message to Mr. testosterone who is coming to divorce Mr. Trump? Who would like to take that? Well, my most important message to the American president is that we need a United States that's in the international arena. That works on our mutual work towards the global goals, about economic development, about climate change, about gender equality, about education. The 193 leaders of 193 countries have underlined the 17 global goals, and we need to see a United States that aligns with this and works hard because we only have 12 years left and we have some really far-reaching targets to reach. Anyone would like to add to that? I just would say that let's hope that Donald Trump's not simply a distraction from the challenge of negotiated global solutions because if you actually retreat and you are creating more risks than actually solutions with your leadership, then that's a problem for all of us. Erika Bierström, Swedish television. Two quick questions. First, Madame Lagarde. You said that there's a sweet spot that we're living in right now, but it also seems this is a time of paradox. The economy is strong, yes, but the democracies are receding, even stagnating, and I'm wondering in what way does this worry you. And also, Madame Kochere, being one of the most influential businesswomen of the world, you know that free trade is under attack. What is your best argument how free trade actually can help development and the issues that you've been addressing? Thank you. So you're right that the economy is currently in a sweet spot. With 3.9% forecast growth for 2018 and 2019 and a very well-scattered around-the-world growth, it's something that we have not seen in the last 10 years or so. What we are saying is it's when the snow stops that you clear the roads and now is the time to actually take advantage of that sweet spot from an economic point of view to focus on the issues that are possibly undermining democracy, that are fueling populism. And we've talked about some of those, whether it's excessive inequalities, whether it's lack of training, whether it's the feeling of exclusion, those matters should be addressed, can be addressed, and the financial means are available and as Isabel was saying, need to be harnessed in the right direction in order to address those issues. We take the last two questions here and over there. Oh, sorry. Very rapidly, I'd just like to say again that everything that boosts innovation and that is to say ability to answer to worries for all of us, a big challenge which is to direct, even redirect business and action of all the stakeholders of society towards something which is inclusive, inspiring and sustainable is good and free trade is part of that. Joel Hills from ITV News in London, forgive me for bringing the focus back to President Trump, but he does lead the largest economy. One, here's a thing for you. I wonder, do you think that some of the comments that President Trump makes about women damages attempts to empower women? Chidna. In fact, I would say that when President Trump immediately after the elections started making some comments, that actually brought women together. That actually made them so clear that we are not going to listen to that. We are going to give some really positive indicators and that is the reason I think the first indicator is this, that at World Economic Forum, we have a seven women co-chair and when the questions are being asked, I feel other way around, I feel that people, those who don't believe in this, they will get nervous. Why should we get nervous about it? Thank you. We have one there and we will take the last one there. Yes, go ahead. I think it's a really rare picture we see here, so there's one question or two questions to Ms. Rometti. You are often here at the web and the participation of women at the web is raising, do you feel any change in behavior here of the leaders? That's question one and question two. I mean, there was the me too debate. Even here at the web or in the business world, concerning this me too. So similar observation here. So why don't I start with your ladder question. Look, I think, I've come from decades and decades of a company that has honored and set the gold standard for inclusion and to me what web represents and what inclusion represents under all of this is about having everyone bring their best or whatever it is they do. And that will give you the best company, the best country, the best society. And I think that particularly this year this is underpinned. In fact, the headline isn't inclusion itself. I think inclusion underpins everything we talk about and that's the better way to look at it. And inclusion is not just diversity of gender.  It does not matter and in fact, it doesn't matter in that definition because inclusion is what will allow people to participate and to give their best ideas. And so it is the underpinning, not just the goal in and of itself. Look, I think, I mean too, there is obviously not tolerable in any situation in any country under any company in any way you look at that. And I think, I know, I live in a company in a country and certainly in my company as I run it, it never has been tolerated, never will be tolerated. But did you make some observation here kind of? I think she has answered that. Yeah, I think I just did. So because we're running out of time, our course chairs will be with us throughout the next three, four days. There will be a penalty session at 6 p.m. today. You can come join us again and thank you everyone for joining us today and enjoy. Thank you. Thank you.