Yes, thank you for that very kind introduction. It's a really great pleasure to have the opportunity to talk to you today. Especially given the very strong and long relationships that have existed between ISIS and Scandinavia over many years since the late 1980s. So what I'd like to do today is to really give you an overview of the facility. If you're familiar with neutrons and meons, there'll be some background that will not be challenging for you. But if hopefully for those of us not so familiar with the techniques, it'll just give a bit of background as to why we're interested in these things. I'll give you a bit of context about some of the research examples that we're driving forward. Tell you a little bit about how we've responded to COVID, how you can access these facilities and what we see as our major developments going forward. So let's dive straight in. And the spirit of the talk today will really be about providing unique information about the structure, atomic structure, molecular structure, nanoscale structure, macroscopic structure, and dynamics of materials that really you can't get by other techniques. I think there is a... Neutrons and meons are in such short supply that actually if you can do these measurements with other techniques, you should do so. And so we really want to play to the strengths of the techniques. But let's give you a little bit of background if you've not visited the Rutherford site before. This is what it looks like probably today. I'm just looking at the window. It's a grey sky, but reasonably clear. And so this is the Rutherford-Apton Laboratory, part of the Harwell campus. Actually, later on I should be talking about our activities in terms of clean growth and circular economy. And actually ISIS is a nice example of that because it arose from an existing proton synchrotron, the Nimrod 7GV proton synchrotron, which was housed in here. The actual spallation source was approved in 1977, quite a brave decision to go for what was a relatively new technology in terms of spallation, so accelerated driven sources, especially given the wealth of experience that the UK has in reactor sources for neutrons, as was demonstrated at the Harwell site just over off the side of the picture here. So a brave decision in 1977 resulted in the opening of target station one, which is a 160 kilowatts target station that opened in 1984, opened by the then Prime Minister Margaret Thatcher, who was a chemistry graduate, so she had a very particular interest in ISIS in 1984. And then in 2009 we opened the second target station, which is a low power target station, only 40 kilowatts as opposed to the 160 kilowatts of TS1, but actually taking advantage of a lot of the developments in things like optics, detectors, moderators, and neutronics to really holistically design a super efficient target station. And so even though the power is low in the second target station, the neutronic output and the scientific outputs are very high. This picture is already out to date. And in one way it's out of date is that in terms of the idea of clean growth and sustainability in our lives, but also in science. We're also generating our own electricity, which I'll show a little bit of in a slide just in a while. But also an important part of the campus is not only do we have the ISIS Neutrona muon source, but we also have the third generation diamond x-ray synchrotron. We have some of the UK's most intense lasers, and increasingly important, we have our scientific computing department. One of the big frontiers for our research is the ability to model and describe our data. There's really no purpose in us producing beautiful data if it can't be interpreted and then ultimately used to design new materials and so on and so forth. And so having the scientific expertise on site as part of our wider engagement with our community is a really important, holistic way to do science. And so lots of the examples that are described today really play on these synergies between all the facilities. So I just said I had a little bit of slide on all the why this is out of date. So this is if you were to look down on the top of TS to just building here now you'd see it is covered in PVs, which is really great. So we have something now like 800 kilowatts of install power just on our AT this is TS to alone, which is will take out hopefully 200 tons of carbon, but it's a small step. So operating protons is an expensive business. It takes a lot of energy. I see typically runs at about 12 megawatts in operational cycle. So obviously 800 kilowatts is a relatively small amount of to take off the top of that. But I think it's important that we all take a responsibility for the sustainability of the science that we do and we'll come on to that a little bit later when we talk about that clean growth and circular economy. So a few headlines to give you an idea for the scale of our activities. We are a national facility, but we're international in scope. So we have a number of instruments over 30 between our neutron and beyond instruments. And there are some numbers that I'm particularly pleased with. One is the very large student PhD training aspect that we do. You can see, this is in 2019 2020. And I think it's really important to pay attention to the impact of COVID. So almost 800 PhD visits, which, which is really important. And we take that training aspect of our program very seriously. And I think it helps that we have an amazing staff who really engage with the PhD students postdocs and so on to support the principal investigators. We have used visits in total over 3000 working with a range of companies and this number is continually increasing, particularly in the energy sector engineering chemicals and increasingly in the micro electronic sector. We might give an example of that later on. Typically, latest data is over 600 publications of this, the trend in publications needs to be going up. And it kind of scales with the number of instruments that you produce on the number of days you run. But that's a nice trend where we've seen 600 high impact publications. And a metric that I'm particularly keen on is that is the number of new users. I think it's easy to think that neutrons are a meteor technique that the user community is stable it's not changing but actually it's dynamically evolving so over a third of our users are new users have never used the facility before so that's a real challenge to us to look after our new colleagues. But of course it's really energizing and exciting place to be as a result of that. I said we were a national facility. But I just showed this plot here on the left hand side, which is the origins of our principal investigators. It's slightly out to date. So it has changed a little bit. But you can see throughout Europe. ISIS has a very strong reputation footprint from across Europe. Scandinavia particularly well represented Sweden in particular has had a partnership with us since 1988, which is recently a few months ago has been extended by another five years. And that's developed real step changes in our instrumentation Polaris I'm at, and really supported the engagements of the Swedish community. And I think we see this where, where countries engage with the facility you really see a growth in that community so Sweden is our largest international community about 5% of the user program. And as happened in this case with India, you can see that we have a lot of users coming from India, and that's result of the Indian government engaging with with ISIS and providing support and resources to allow Indian users to access our facilities. So if you have interest in Japan as you can imagine, North America, and even South America and also South Africa. So, so national facility, but with a huge international profile. I really want to dig and find a lot more. Our annual review from 2020 is on the web. The link is here you can maybe get to it in the recording. And that's a really nice description, a catalog of a lot of the activities that we do. I really want to understand about a sort of almost how do you build your own special neutron source. There's a really nice guide that we've just published on the web page in the UK. If you're old enough you can remember Ladybird books will kind of take a complex topic and kind of introduce it to you and this is our kind of Ladybird equivalent of how to build a neutron source. So I was asked to just talk a little bit about the next thing which is something that that we probably don't want to talk about but is actually in some ways is a big success story and is a huge credit to our staff and that is of course the impact of the COVID pandemic. We are by and large an experimental facility. And as such, we have we rely on people coming to our beam lines, doing experiments, making materials, writing code and so on and so forth. And that's just not being possible under the restrictions which hopefully we're starting to see the relaxation of. But actually at ISIS, I think we've responded incredibly well. We've run two and a half cycles now, under COVID restrictions so we have about a month left of our third cycle, before we go into our long shutdown which I'll talk about at the end. We largely had no users so most of the access has been remote here's just some examples of staff and users, installing various things on various beam lines. We've completed well over 500 experiments. Typically in a cycle we maybe have 2030 40 users, but all in very small groups, a group being sort of one researcher or maybe two under special circumstances, but I think the staff have responded dramatically. In a way that I think is is is great testament to them, but also slightly surprising in the sense that in a normal cycle we would maybe deliver an efficient program at the level of sort of over 90%. So over 90% of our beam days would get used doing good science. Over that number of shrunk. I thought it might have or be even smaller but it's 80%. So we're still able to deliver a strong program, but it comes at a huge cost to staff who are having to support a lot of external users who are able to access things remotely and so on. So yeah, I just like to publicly thank all of our staff for the work they've done under COVID. And one of the things that's come together to try and enable that is before we started the lecture today we discussed about new technologies. And one of our new technologies is IDAS, which is, you can tell we're not created acronyms. And so this is a basically an STFC cloud with a huge amount of compute resource that people can actually access remotely, create a desktop is one of our last colleagues, and multiple people can engage simultaneously on this desktop. And so it's a very nice collaboration tool so that people can look at their data in real time they can analyze their data. And actually, all the things they need are brought together in one place, namely, access to the data access to the compute resource access to data reduction and software access to the analysis, it all comes together. And so each of our groups sorry this is too small you probably can't see it. I've sort of created an adapted environment to bespoke environment in which our users can can analyze their data. So that's really been a something of a game changer. And I think obviously the sort of degree of serendipity with the arrival of COVID, but obviously this has been a thing that's been long in the planning, and it just arrived exactly at the right time. So okay, you're all very familiar with this electric run through it quite quickly. So neutrons have a lot of interesting properties that energies allow us to study the excitations vibrations in materials collective modes. The wavelengths are comparable to atomic spacing so it gets exquisite district description of crystal structures with kind of femtometer resolution, right up to a kind of the nano scale micron scale in even to microscopic. Particularly important that we interact with the nuclei. So we're sensitive to light atoms. We have a different scattering power of atoms with with neutrons varies quasi randomly across the periodic table, unlike with x rays whereas you know it goes in electrons. So we're very good at seeing light atoms. So hydrogen is important to you, for example, that we can study that, even when it's in the presence of much heavier atoms. We can also do isotopic substitution, which means we can swap hydrogen for deuterium, which has a different scattering power so we can light up different parts of the structure. And we'll probably see examples of that neutrons are neutral particles. So that means they're very highly penetrating we can get inside complex sample environment. Very low temperatures, very high temperatures, controlled humidities, in situ sample changes, whole range of things that you can go through a lot of liquid for example to get to an interface you need to study, or a lot of metal to get to a world that you need to study. I have a minute in moments to spin half particles we have a dipole moment so we can study minute structures fluctuations and so on. And we can polarize them and do lots of tricks to really separate out the important information in a measurement. So the other technique that we have which is is exotic, I mean muons are exotic particles is muon spectroscopy. So this is different to the neutron technique that we'll talk about in that muons are implanted in materials, and they process in a local field in the material. So the two sides which they have a large electronectronic activity, they process in the local field, and then 2.2 microseconds later they decay, they admit some neutrinos, and the positron we deduct the positron. And the interesting point is the positron is preferentially emitted in the direction of muon is pointing at the time it decayed. So you can see if you measure up enough statistics, you're able to back calculate the local environment that the muon must have been sitting in to give you that profile in time. Okay, so this is a really powerful technique which is very sensitive to incredibly small moments so for example if you have a complex superconductor that breaks time reversal symmetry, where you might expect a very small moments much smaller than you can measure at the level of what you can measure with techniques like XMCD, then the muons are a really powerful technique to study those sort of systems. And you can also do something interesting with things like battery materials as we'll show. So the key to ISIS is that we're a spallation neutron source. So without going into too much detail, essentially, we bring a high energy beam of protons. We're going into a heavy metal target in our case, tantalum. The nucleus absorbs that proton gets a lot of energy gets excited starts to boil and starts to kick off a whole load of other particles, which then actually can go on and and can generate more neutrons but typically you get about 15 16 neutrons for every proton that hits our heavy metal targets. Okay, so we have some like 10 to the 15 protons, which gives us over to 10 to the 16 neutrons. Those are all very high energies so our instant proton beam is typically of the order of hundreds of mega electron volts in our case 800 MeV. And that means we produce neutrons up to the energy so we have a lot of very high energy neutrons. For the most part we want to slow those down to the kind of energies that you find in systems. So that's angstrom wavelength or me electron volts in energy, and we do that with moderation. And then we do that 50 times a second 10 times a second. So unlike a reactor which is a continuous source, we have our neutrons produced impulses, and the difference between our source, compared to the source that's being built in the SS is our proton pulse is very short is about 100 microseconds, whereas at the ESS is much longer so we have a short bright force, and the SS uses that extra time to, to couple more power in the target to give you a large integrated neutron flux whereas we get all in a peak. And that then flows through and instrumentation that we have. So this time of flight is a natural choice for false sources because you'll see that by knowing distances and times, you can determine the energy wavelength of your neutron, and therefore you can do your scattering and lasting measurements and so on. Typically, just as a piece of information. A thermal neutron is a broader kilometer per second. So if you measure distances to better than a millimeter you need to be timing on the kind of microsecond times gone that's all very feasible with modern electronics. I'm very poor at PowerPoint animation so I'm going to skip that but that was a proton beam hitting a target neutrons being produced moderated interact with a sample and into a detector. So what does that mean for an experiment. Let's just take one example, and this case is is diffraction. So we're going to have our moderator or source of neutrons. We're going to have a sample, and we're going to just have a single detector at some distance, and at some angle. And the key feature here of the neutron technique is that within a single detector, we measure the entire defraction pattern, because basically the de spacing in your crystal is going to be proportional to the time of flight so if you know the distance between the all the sample detector, you know the scattering angle, then you can basically measure an entire spectrum. It's very simple expression you can see within a single detector, we measure this entire spectrum. But of course we don't just have a single detector. We actually have multiple detectors which we can then group and focus to improve the statistics, play around with resolution, and so on and so forth so a very powerful technique. We just have diffraction of course we have the full spectrum of techniques. We have a range of instrumentation to study the structure and morphology of materials, including chip irradiation that we might talk about. We have a range of spectrometers to allow us to study the dynamics of materials. And then we have a lot of ancillary support facilities that users can access to produce bespoke due to rating materials. We have a very well equipped characterization lab that allows you to do complimentary x-ray measurements, magnetometry, and so on and so forth. So we have kind of 30 instruments that we kind of group into various disciplines, and really taking advantage of the fact that we have two target stations, because that allows us to tune the instrumentation to actually match the science that we want to do so this particular target station here, number two, is a very good source of cold long-wave lab neutrons, which are very amenable to techniques like when you want to look at large scale objects, such as small angle sketch and reflectometry and look at the complex magnetic structures with large d-spacings for example. So those scientific applications, it's a real challenge within a half an hour or so to talk about some of the science. You could spend many days talking about the science, but let's just kind of give it a feel, and I won't have the time to go in any great detail, but hopefully they'll give you a flavor for the kind of things that we do. So I've chosen clean growth as one of the kind of buckets into which we can put our research, and this is important clearly. It's one of the UK's government's priorities for as part of a clean environment, sustainable growth, and so on. Interestingly, the government recently ended last year, published a 10 point plan for a green industrial revolution. The UK has ambitious targets for net zero in terms of fossil fuel by 2050. UKRI, my employer, has set a target for facilities to be carbon neutral by 2040. And so clearly this is something that we need to be contributing to. And of course the good news is that neutrons and muons have a huge potential for impact in this area. Whether it be in offshore wind power, carbon sequestration, photovoltaics, fuel cells, reliving reactors, nuclear reactors, and so on, the neutrons have a really important role to play. And so we don't just do a single thing in clean growth, we actually reach across a very wide range of areas. But let's have a little bit of a dive into some of these areas. I think a rather nice example recently is from colleagues looking at next generation materials for solid state batteries. So this is a lithium lanthanum tungsten oxygen system. The lithiums are the yellow color here. So there's a course of the ions that are going to carry the charge around for us. And this has a double perovskite structure. And of course, with neutron diffraction, you can go in and really resolve the structure with great resolution. And in fact, these figures down here, I apologize, I made them a little bit too small. So this is how the structure changes as you load it up with lithium. And you can see at the highest lithium concentrations, you can see what happens is that the lithium actually generates clusters, a lithium oxygen for clusters, which locate on a particular side, the A site within this double perovskite structure. So neutrons give you this absolute determination of where the atoms are and what the atoms are. And you've got this really powerful determination of the structure. Of course, you might also like to know about how those liliums are moving around on the system. And this is where the muon technique can play an important role. Recall that muons are sensitive to the local environment. So if a muon implants itself into a structure at a place where there's a larger electro negativity, then as the lithium ion, which has a nuclear moment, moves past the muon site, it perturbs the local magnetic environment, which the muon senses and here you can see this is, I don't go too much details but this is the muon signal as a function of that decay time. Remember the muon has a lifetime of 2.2 microseconds. And so you can actually infer the hopping rate of the ion from the muon data. If you look at it as a function of temperature, you can work out what the activation barriers are. The muon just worked for lithium, it worked well for sodium, can work with a whole load of other materials. So next generation matter technologies are probably going to be amenable to this site. And what you can see, this is a function of temperature. You can see here, mu, this is the rate at which the field that the muon sees is fluctuating. And you can see nothing's happening, nothing's happening and then at some point the lithium start to move. And you can measure your diffusion coefficients. Now you can measure it in other ways, the muon's, you can do it with electron and electron-pedium spectroscopy and you get this answer. And if you do with muon's, you get a reasonably similar answer, there's a little bit of disagreement there. You can also measure the activation barrier from this temperature dependence, and you find that muon's see a much lower activation barrier than is measured with a bulk microscopic technique. And of course the reason for that is that the muon is a local probe. So it doesn't see things like grain boundaries, which obviously in any kind of transport measurement, you're going to impact the ability of the lithiums to move around. There's a direct measure there of the impact of grain boundaries on this transport of lithium ions, which is so important in these materials. This is a nice example of from Shiha Young's group in Manchester, which is trying to develop materials to more efficiently reduce the energy and environmental cost of producing materials, in this case polymers. And this is to do with alkenes, propene, ethylene that need to be separated from our kinds before they can be converted into polymers. Now typically zeolites are kind of quite inefficient of this process, but the group of Manchester realized that by adding nickel, which is the green atom here, these are neutron structures determined by powder diffraction. And you can actually see that the, the alkenes are actually binding selectively at the nickel two plus sites as a chemo selective binding. And I apologize, you can't see the numbers here. We have this absolute determination of the, the loading of ethylene ethylene, et cetera, on these systems. So not only can we say where these molecules are binding, but we can also talk about dynamics. And this is what's shown here this is an elastic neutron scattering, which compares the side of settling with the settling that's bound to the structure. So you can start to see all of the, the changes to the trident transition, excuse me the translational vibrational vibrational modes in this structure. So not only do you have this determination of the structure, you also understand how these atoms moving around. Whoops. Sorry, I don't know what happened there my computer has just stopped as it's always going let's see if it comes back. Okay, so give me a second, take your time. Don't worry. I think the chance to say to the audience that if you have questions, please don't be shy, and just write them on the chat and we will share them with our speaker. We try to fix. Technical issue. Yes, I'm sorry about that of course I run through the slides a couple of times this morning and nothing went wrong. I mean I guess there's a possibility that it's a high engine neutron has hit my computer. I don't think we can study but not something I wanted to study during the talk. Yeah, of course. This is going to come back. It's always like this things crash at the wrong moment. Yeah, give me a second. I think it's an experience that we don't have the worst cases. Yes, with our US speakers. Yeah, my PowerPoint just coming back it says. Yeah, I think it's worth it. Let's see what it says. I do apologize. Right. Let's close that. It's okay. The screen again. I think it should work like this. Yes. Great. So we can directly jump to this slide. If I can get back to where I was. Yeah, it was interesting maybe in some ways because the example I was going to give is about death. So I don't know why I've called this clean growth, but it sort of feels a bit like the kind of circular economy, maybe the circle of life. And this is just some really nice work from our colleagues in Italy. Wonderful. So I didn't like the word that no. Another collaboration, long-term collaboration in this case with Latio. Perfect. Yes. Very well. Excellent. So they have realized that actually you can, there's a very clean signature in elastic neutron scattering, which relates to the temperature at which bones were burnt. And so if you want to understand the funereal kind of process that took place in the first century AD, actually, elastic neutrons can can give you a key here. And what these guys realized is that when you burn a bone, the bone matrix undergoes some structural and dimensional changes, which make it very difficult to use kind of more conventional osteometric methods. What they did was to combine inelastic neutron scattering, bridge transform infrared, micro-roman, and so on, to actually look for the signatures, which allows you to differentiate bones that are burnt at 400, 500, 800 degrees C, and so on. Yeah, I don't know what this relevance is for modern forensic pathology and so on, but it's good to see that neutrons are really kind of given us new insight into what was happening in the first century. So let's roll forward. Let's look at nanoscale and quantum. And again, I think this plays to the government's agenda of transformative technologies and also healthcare, actually. So here are a few examples that I realized I've lost a little bit of time there. Hopefully we can recover some of that. And this first example is is one where colleagues at the University of Cambridge created little memory stores. Now we're all familiar with microelectronics, but we're very familiar with 2D planar structures. So if you think about your chip or your storage in a computer, it's on two dimensions. But of course, if you can go in three dimensions, then you have potential to massively increase the storage density and so on and so forth. And so this is an example whereby you can create these little nanoscale structures. So these layers in this case are an alloy of cobalt iron and boron, a few nanometers stick, separate them with a very thin spacer layer in this case ruthenium. And they're natural ground status for them all to be anti-pheromonezically coupled. Okay, we can then apply a field pulse and actually flip one of these layers. And you can see here now we've flipped this layer. We can, so we've basically got an information store. This is a little piece of information. We can apply another field and flip it up. And again, and we can move it through the stack. So we've got basically a shift register where we can move information and therefore transport data through this structure. So if you look at a conventional technique, like, like MOOC, for example, you get these complex patterns where you can sort of indirectly infer what might be going on. But with polarized neutrons in this case and reflectometry, here's some of the data you can absolutely definitively define what these structures are. So these structures that you see here are cartoons, but they're extracted from the neutron data. So we precisely know what's going on in our little structure. So that's an example of a metallic system on the nanoscale. We can also study soft matter as well. This is a recent example from Zoom, one of our latest instruments, a small angle instrument, which is work with colleagues in diamond in Japan, Switzerland, Italy, so huge collaborations in this case, creating these interlocking structures that you can kind of engineer their properties. And the really amazing thing here is that you can organize these structures in a self assembly that they start to interlock, which is really difficult to do. So you have to have these kind of pre-annual molecules that then pre-organize and that then give you these little structures. I do wonder if the reason this is in nature is because we have the Olympic rings here in what turns out to be an Olympic year. But of course this is just a microscopic, this is atomic force microscopy that shows this image. So it's very nice. But what neutrons allow you to do is with some modeling is not only see the structure, but also to see inside the structure. And so each of these has a little kind of structure, which is a so-called core shell model, and we can see inside that we can see the interpenetration of the solar molecules and so on, which you can't get from other techniques. And actually looking inside materials is clearly a big part of what we do. So here's a nice example from the healthcare, and this is work from Yumea Lund-Gottenberg for NMR. This is looking at a cancer-causing protein, PCL2. And I think this is a nice comparison. The NMR provides you with the structural and dynamic information on the atomic to the protein molecule level. Whereas what neutrons give you is this a location, the molecular compounds and volumetric contributions. In this case, you can see that this particular protein is tail anchored into the membrane. In this case, you can see it's embedded and so on. So just a huge amount of information. I'm going to jump forward, if I may, and just talk a little bit about just a couple of examples from engineering and imaging, which I think is an important part of what we do. The place to this agenda of transformative technologies. So with neutrons, as I said, we can see inside material in real space through radiography and tomography, or we can see it through diffraction. And this is a nice example of studying monopiles. These are the things that support offshore wind turbines. They're big. They're typically 10 meters in diameter, 70 meters in length. And with neutrons, you can actually go in and look at the welds within a monopile. And all you can do that, you actually get the full stress tensor, so you get all directions in your measurement. And so if you can understand the stresses and strains in a monopole, then you can increase the lifetime of offshore wind turbines, which reduces the cost, increases the capacity, and so on and so forth. So it's a really nice example between Cranfield OU and colleagues in Norway, which have been able to fully characterize the stress in the monopiles. Interestingly, that stress is far higher than when you measure with other techniques. So it's really important to know that. So that's using diffraction, but we can also do that using imaging. And here's some nice examples from my colleague Genevieve Berker at IMAT. So we have this beautiful image at the top of an ammonite. You can compare what you see with neutrons here compared to what you see with X-rays. There's an ammonite. And I think this is just really nice work where Genevieve Berker has extended these measurements over to plant science. And this is really looking at how water uptake and the impact of soil and so on on plant systems. And this is a really nice example of where you combine neutron imaging with X-ray imaging to give this complete determination because they see different things. So you start to highlight different components of the system. The components containing water, the heavier components and so on, very clearly seeing with neutrons. So this is, let's get this right. This is the combined image. This is the neutron image, I think. Have I got that right? Yeah, okay. We'll come back to that. So you can use similar technologies and apply them to batteries. This is a lithium-finite chloride battery, which has a nice capability of being extremely low self-discharge. And here's some imaging from Graves Asia UCL. This is a very nice PhD. This is, I encourage you all to take a look at that. This is white-bean imaging looking at something like 7000 projections as you rotate the battery and the beam about 30 seconds each, giving you a resolution of better than 80 microns. And you can start to see all of the different things that you can start to see the lithium anode. You can see as it's discharging, you can see this flower pattern, which is the porous carbon skeleton that they use to provide channels for transport in this material. You can see the SO2 gas reservoir as they start to billow out. And you can see this in as a function of time as the battery discharges. So really, really impressive. I told you that we can irradiate microelectronics. And this is our chip by our facility, which is a facility that allows us to produce very high energy neutrons and introduce them to microelectronics. High energy is being in neutrons that we produce up to that 800 MeV level, which is very similar to the spectrum you get in the upper atmosphere. So one hour in our beam is like 100 years in a plane flying over the Atlantic. So a very good way of accelerating the safety, the performance of microelectronics and their resilience to high energy neutrons that can cause damage, errors and physical damage in microelectronics and that's an increasingly important area. I'll skip to this very briefly. Ordinarily, we would run a lot of hands on training exercises, whether that's neutrons, muons, a lot of virtual resources available to users. And of course, this year, that's not been possible. We've run a few things in the virtual environments, but hopefully we're very much looking forward to bringing those back online in the coming year. I should say we have a very strong PhD program. We funded over 40 students up to the present day. And if you take a look at our webpage here, you'll find some of the active positions that are currently available. So if you're so interested, please take a look. So just to summarize them, what are we going to do next? So of course we have a roadmap that is for a complicated figure. We're somewhere here, which tells you we're just about to go into a long shutdown. And the reason for that is we're basically replacing the heart of target station one. So we're producing a new target, new moderators, new reflectors, and so on. We're doing an awful lot of work on our accelerator as well. So we'll come back with a new TS1 early in the new year, which is great, given the age of this system. Of course, ESS figures largely in our activities. We're doing a lot of work on things like data streaming. One of the things that I'm particularly involved with is two of the instruments that we're building at ESS. The first one being Loki, which will be one of the first instruments. It's a small angle scattering instrument and you can see kind of various components. Here's the heavy shutter. Here's the detector tank. Here's collimation that's all being built. And so we look forward to that being installed at ESS. The second instrument is a little bit later in the design process is Freya, which would be a reflectometer for liquid surfaces and so on. And these should offer us really game changing performance in terms of what we can do at ESS. At my own facility, we are developing our next generation of instrumentation. Freya is a series of projects that we are currently developing, awaiting funding, which we are hopeful will arrive next year. But if you're so interested, there's a range of workshops we're holding in July. Please have a look at the website. Our website, you'll find those where we will be discussing those instruments in a bit more detail. We have bigger ambitions and that's recognizing the fact that the UK will need a new neutron source around the kind of 2030 timescale. So we're currently in the process of really quite detailed feasibility studies, physics design studies, looking at new technologies, looking very much at the energy sustainability argument we have to make facilities that have a low environmental footprint, even though we're going to do great research in terms of sustainable and clean growth. We're going to analyze the environmental impacts of what it is we do at the large scale facilities. So there's a lot of activity on that at the moment, which is very, very encouraging. All these facilities you can access them through a range of mechanisms. Our next proposal deadline is currently proposed. It's postponed because of COVID, but it's likely to be the end of this year for TS2. And that would give time on the instruments around the March period in 22 and early 2022 for the TS1 instruments that come on later. As part of the shutdown, when we come out of that, we expect TS2 to start up earlier than TS1. So there's various access mechanisms. You can do the direct one, which is the one I've just kind of described twice a year. And at any point in time, if you need rapid access, we have express access for the quick measurements of samples, testing ideas, cleaning up people's PhD thesis and so on. And then of course we have dedicated industrial time. So just to summarize, hopefully I've shown you both neutrons and muons possess this amazing range of characteristics that are very well matched to curiosity and technologically driven research. So we're friends of complexity, driving emergent behavior. We're looking at a multi-scale approach. So we want to study materials on many lens scales and on many energy scales. We're bringing together the complementarity of the facilities on our campus be that computing, neutrons, muons, photons, and so on. We have multi-mode measurements where we're not just measuring in elastic spectra, we're measuring in situ, mode, in situ, Raman, whatever it may be. And increasing that frontier that I talked about right at the start of theory modeling and software. And we're also very lucky that we have an expert and engaged staff. So if you have an idea for an experiment, you'll certainly have somebody in the lab who will be interested and be able to guide you through that process and try and support you to deliver a really good experiment. So with that I recognize run over a little bit because of the technical hitches, but I just like to thank you for your attention and also to thank all of my colleagues at the lab who provided basically all the information for the things I was able to just talk about. Thank you very much. On behalf of all the audience, we really thank you for such a great talk. So, now is the time for questions. And we have couples one. So we can start. So, there's one question is about so the staff at large scale facilities have a chance to do some research and we know that other. So, how do you read the innovation of the research led at ISIS compared to that led through the traditional university route. I think first of all, I'll challenge the premise of the question. All of the research that we run the facility has gone through a process of peer review so unlike many facilities at ISIS, we don't allocate in house research time. So what many facilities do and we can have a discussion around that we don't do that and so staff compete in the peer review process process as other people. What they do have is the opportunity to take out small parts of the program for commissioning. And I think, and maybe this is at the heart of the question that does allow people to develop new techniques and this is where our PhD programmers, I think it's been so successful because it allows you to develop new capabilities, whether that be in situ NMR, for example, in situ growth capabilities and using that small amount of commissioning time and it is a tiny amount, which is again is reviewed and has to be justified. So I would say really good outcomes. So I would say very much that for me the best science comes from the strong collaborations. I'm old enough to be a fan of the film spinal tab. And if there's anybody on the call knows that film, you know, the staff at the facility know how to turn the experiment up to 11. And so if you really want to push the boundaries of what's possible, then, you know, you work closely with those guys and they get you up to 11 and beyond. And so I think I'm not sure I'd make a valuable on the quality, but I think when we work together, our industrial, our academic and our staff, when they work together, we get, we get the best outcomes. So that's partly why I think COVID has been such a difficult period because a lot of those natural interactions have had to reform in a different fashion. Yeah, we fully agree like collaboration is really the key of success. And we're social people as well. I mean, we like, we like having a meal together and those things happen on an experiment. So we shouldn't, shouldn't forget we're humans. No, exactly. But we hope that with this format, we can all interact today and ask a question to our special speaker, Sean. So, yeah, in fact, the question was what followed was so what does this say about the need for a UK National Laboratory along the lines, for example, of CNRS, and lots of the Department of Energy Labs, for example, so this is also the following question from one of our Yeah, I mean that that's a really interesting question. So the UK has things like the National Physical Laboratory, which is a very different thing to what we have. We are actually called National Laboratories. Because I think our scope, we clearly demonstrate that we do things that you can't do anywhere else in the country. And in many cases, you can't do anywhere else in the world. I think that, you know, in my career, I've done a lot of work in, in the DOE labs in the US, for example, and that model works very well too. I think that the challenge is going to be for our future big projects like ISIS to is whether that's something that's still on the scale of a nation, or if it's a more international endeavor. At ISIS, because we have such strong international partnerships, we mentioned Sweden, Italy, the Netherlands, India needs to apologize for forget any. But we just have this whole range of interactions and they just bring such a rich richness and diversity to the program that I think, you know, it's about trying to find that sweet spot. We've been in the National Lab and being well adapted to the UK's needs. But of course, science is global. And so we need to be part of that wider, wider community. So I guess there's opportunities and risks in going down a sort of DOE time model. Yeah. Thank you for such a detailed answer. Another question. So what would be the niche for ISIS when he says it's fully up and running. Okay, that's a good, a good question. So the, the answer to that I think is, is, is quite nuanced and complex for let me have a go nonetheless. The first argument is that it takes a long time to get facilities operational. If you look at a lot of major investments, the time between them sort of really switching on, so producing photons or x-rays. There's almost a decade before those facilities are really developed. So ESS is getting closer to its first operations, but there's still a period in which we need to build up that capacity and knowledge. ESS as a long pull source is totally new. So whilst we've done a lot of modeling and we think we understand we will be learning things throughout that process. So it's going to take time and there's going to be great achievements and there's going to be mistakes and, and that's how these facilities come online. But there is an intrinsic difference. ESS is a long post augustation, which means you've got a lot of power, but you've got relatively poorer resolution. And so you play that off against the power by having longer instruments by having complex chopper systems and so on. ISIS is a short post augustation has a very intrinsic high resolution. So I think it's going to be a complementarity. It's going to be about the experiments that you can do at ISIS where you absolutely need the high resolution. You know, we have one of the, if not one of two of the highest resolution instruments in the world, and they just provide new insight. So there are going to be certain science examples where you need that insight. There are going to be certain science examples where you need a very large energy range, wavelength range where you need cold neutrons, you need epithermal neutrons that we do very well. And then there's going to be experiments where you need lots of flux. We don't have that many neutrons, even at the most intense sources, we don't have that many neutrons. So I think there's going to be a really nice complementarity between the long pole source of ESS short pole sizes ISIS to very exciting developments at Oak Ridge in America with their second target station where they're taking an approach like us a low rep rate. So, yeah, I think there's a really, I think neutrons caters in Europe, potentially have a really bright future. Yeah, with these facilities, but I think I think you do need this landscape you do need this balance. Yeah. Next question. So, do you have joint proposals with diamond to make it best use of both x-rays and neutrons at both facilities. Yeah, so that's a good question. In a few cases we do where the science requested.  So it's not a global thing. Not for any instrument you got any other instruments but in specific areas, we've implemented that and and it works very well, just because there is this natural complementarity that is, allows you to get this, this, this deeper insight into the material systems that you're, you're studying so you know that that proximity a bit like ESS and Max for, you know, you'll have that that synergy, which I think is is really vital. And then you need to back it up with a computational resource as well. Thank you. I think we have time for a couple of questions. So there is another more general question, which is about like. So, can user have, yeah, testing time before the actual being time I think this is what someone in the audience wanted to know, because some experiments might be a little bit exotic people have any hard time to, you know, to have the feeling of how the experiment will be actually once. Yeah, everything will be set up at the deadline so that is there a proposal a channel that people can apply for to get access to test the new experiments. Yeah, so, so there's various mechanisms that won't one can do this. And the express mechanism is something that we, we use at this a lot for where people have created a material. You know, you can calculate the scattering pattern where you're not entirely sure until you measure it that's why we do experiments of course. And so that is a mechanism where people can just post in samples, and then at specific points in the cycle we will run a huge number of samples, typically with with relatively straightforward conditions you know we're not going to be doing ultra high or whatever but we can do temperature and so on so forth. So that's one way of getting materials developed in preparation for an experiment. Interestingly, our peer review panels, quite often they will request exactly this so they'll, they'll see a proposal come in and they think that's really exciting. You can do it. And so what we'd like you to do is go away and make that little test measurement that says, I got the system with a tiny moment can I can I see the moment in a magnetic case for example. And so then we would respond to that. And then equally, it's about, you know, I think now the connection. The problem with the connection that jumped the, in the middle of your answer. So, sorry, about to say if it's possible or not. All right. So yeah, you can do numerous mechanisms. Sorry, you can do it through express. If it needs more kind of a technique development and discuss it with the local contact and they can maybe make a case that there's some commissioning time. That would would be useful. And yeah, and again, I think that's where where our PhD program comes in so useful because often a lot of those developments are being generated by those guys. And so we can we can try out new things and quite often you don't need neutrons to do the development you just need to make sure your little widget fits in the right gap and so on. So so yeah, it's all about the discussion.


Welcome back to another video. So in this video, we're going to be taking a look at percentages. I have looked at this in previous videos, but it continues to be one of the most or frequently asked questions that I receive. So how to increase the number by a percent and basically anything else revolving around percentages. So in the workbook, I've got a couple of sheets and we're just going to go over a variety of different percentage calculations that you may need or wish to calculate. And what I'll do is I'll make this workbook available via a link in the description so you can download it after we've watched a video or if you want to jump ahead and get it now, you borrow means you can. So we'll just step through each one of these really simply and just go over how the calculation works and just give you the information that you need. So the first one we're going to look at is comparing against a baseline. So I'm looking at this first table here. So you can see we've got monthly sales figures and what we've got the first of December 2019 all the way through to November of this year. And all we want to simply do is for each of these months, so in 2020, we just want to compare this current sales figure against the figure in December. So we can see that the company, for example, obviously sales have been reducing. And we just want to compare each of these to this baseline number we're calling in December, just so we can see what it looks like. So in order to do it, it's very simple, we're going to start on the top part here and all we're going to do is go equals and take the current sales number divided by again, in this first example, that same sales number. But this time we're going to lock C nine, so by pushing f four ones. So it's always going to refer back to C nine. If I hit enter, you'll see you've got the value of one. And then as I pull this down, I'm just going to get to the bottom here and go fill without formatting. And the last thing we need to do is convert these to a percentage. You can see that we now have that picture. So what it's telling us is obviously this first one here in blue is kind of irrelevant. It's just going to be our 100% number. So we know that 350,000 is 100% of 350,000. But as we move on, we can see how sales have changed as it's gone on. So if we were to jump ahead to May, for example, we can see that the sales in the month of May are actually 28% of what they were in December last year. So obviously, it's not good, but it would actually give you the numbers to actually visualize this in a graph. So if I wanted to put this into a graph, all I simply need to do is highlight all these, go on to insert, pick graph, probably a line graph for this. And you can now see we have this picture here. And this is quite a commonly used graph or metric, shall I say, I'll use this in previous packs. And it just demonstrates obviously of previous supply or demand being 100%. How does the sort of picture look thereafter? And you can see there's a big drop off here initially, and it started to build up again. But I've digressed and done in the charts, but this is how you can also do a percentage when you're trying to compare it to an initial baseline. So the next one we're going to look at is percentage change. So I've got two options here for percentage change, and you'll, you'll see what I'm doing as we step through. The first one is the most simplest of the two. But to be fair, they're both quite simple. If we want to compare 2020 sales against 2019, so obviously 332 against 360, all we need to do is go equals the 2020 number divided by the 2019 number, hit enter, we'll pull this number down, and let's just convert that to a percentage as well. And what we can see is that when we compare these two numbers for January, we can see that 332,000. So the sales for 2020 are 92% of what they were in 2019. So it's a very similar calculation or is the same calculation as we looked at in this first baseline one. But obviously it's going to show us obviously how things have changed. It's going to just use to say 2020 2019 as the baseline to compare 2022. So yet we can see sales in 2020 were 92% of what they were in 2019. And as we go through, we can see that jumped down to some bigger ones actually sales are now 121% of what they were in 2019 for September. So that gives you another way of comparing, for instance, or in this example, two years worth of data. Carrying that on and often what you'll find is you might not want, even though you're wanting to see what the percentage change was as we have here in February, we don't want to show it as 103%. We actually just want to show what the change was. So it was actually 3%. So we'll move on to option two to do just that. This calculation is slightly different, but it's the same principle. We're going to do equals and open brackets. And the first part we're going to do is we're going to take the 2020 number and deduct from it the 2019 number. And if I was just to do that calculation as it stands, you can see we've got minus 27,500. So it's a reduction. So to turn that into a percentage, we want to take the actual change amount. So this 27,500 and divide that by the 2019 number. So we're basically saying what is the reduction? It's that 27,000. Okay, what is 27,000 as a percentage against 360,000? Oh, and I haven't actually even put it in there. So yeah, 27,000 divided by 360. So we can see what, you know, what is a percentage of 360,000. And it's not brought anything up first. But if you copy that down, convert these to a percentage, you can there see we've actually now got just the actual change percent. And the reason it's beneficial is again, looking at this first row for January, we can see in the first table was showing actually it was 92% of what it was the previous year. But actually the benefit of having this change percent in option two is actually clearly shows what's happened. It's gone minus 8%. And anyone who's used to working percentages or Excel to you that probably doesn't carry much weight. You probably think yeah, it's just tell me exactly the same thing. But if you're working with an audience or maybe not too sure what they're looking at, or they just need a really clear and concise number without having to try and work anything out, obviously, that is the favorable way of using option two, because it clearly shows them regardless what the numbers were, it's gone down by 8% minus 8%. And then so on and so forth for all those other numbers as well. So the next one we're going to look at here is a percentage change of bite baseline. So what we've done in that third one, we basically copied this table from the first one over here. So we've gone through calculations and we now know that each one of these is a percentage of that previous baseline. But just like an option two here, we want to actually know what the actual change was. So rather than saying, okay, we know it's 95% of what it was, we just want to show obviously it's a reduction of x%. In order to do that, again, really simple one to do all we need to do here is go equals, and we're going to enter the number one, because one refers to 100%. And as I'll go over this in the next sheet, but as we know, percentages in numbers and especially in Excel, they are treated as a decimal number. So if you wanted 50%, obviously it's 0.5, with number one referencing 100%. So all we need to do is go 100% minus the percentage we have here, hit enter, copy this down. Obviously I'm going to just do about formatting. That one's not done a percentage. And you can now see we've kind of got the inverse really of what it was. So we now know that the percentage of baseline here, so our sales here were 95% of what they were in December, and that reflects a reduction of 5%. So we know there's been a change of 5%. And if you wanted to get clever, obviously just like the changes, then obviously you could add the minus sign in here as well if you wish. But the purpose here was literally just to show you how you can obviously get the like say the inverse here. So rather than the 95, okay, what was the actual change, which is 5%. So moving on, and I hope I'm not going for this too quickly, but I'm trying to keep it nice and concise without spreading the video out more than it needs to be. And this is the table I was referring to in that last example. So if you weren't aware, obviously percentage as you see it formatted in Excel will display like this. So 100%, 75%, 50% and 25%. And because we reflect them as a decimal number, this is how you can see it looks here. So the real simple calculation is, as if you want to work out what 25% of 100 is, all you need to do is go 100 times 0.25. And then you can see it's given us 25. So that's the real quick way of obviously doing your manual maths with percentages in Excel, well, even just on the calculator as well. But what we're going to do is going to cover off a couple more calculations in this sheet here. So the first thing we want to do is increase by percentage. So we've got similar sales numbers that we looked on the last sheet. So we've got the sales total. And this time we've been told a percentage that we want to increase this sales amount by. In order to do that, all we need to do is go equals. And let's sort of step back a bit here. So if we were to remove percentages from this for one second, so let's change that back to the general, we can see obviously the decimal number that obviously refers to the percentage that we're trying to do, just obviously unformatted here. So if we wanted to obviously increase 332,000 by 5%, what we actually need to do is we want to times that by 105%. So in order to do that, all we simply need to do now is convert as such our increased number to reflect 105% rather than just 5%. So in order to do that, all we're going to do is go equals and we'll do one which refers to 100 plus the 0.05. And if we to put this back to a decimal as well, you can see that we now have a value of 1.05. And this is the actual number we want to times our sales by. So just to tidy our calculation up, all we need to do here is put this into brackets. The part was actually calculating our actual number we want to increase or times by. And all we need to do is go select sales times by that calculation, which gives us 105%, hit enter. And you can see that's now been updated for us. And if I was to put an easier number in here, so let's say 100,000, we can see it's done obviously 105,000 because we know 5% of 100,000 is 5,000. So obviously at 5,000 added onto 100,000 gives us our correct result of 105,000. And if I copy that down for all these other calculations, let's get rid of decimal places, change that back to a percentage, you can see how our result has changed there. Next one from that would be how to decrease by a certain percentage. So it's going to work exactly the same apart from rather than add our increase to one, we're simply going to deduct it from one to give us the opposite. So what we're going to do here is go equals 1 minus 5%, which gives us 95%, or if we're going to here makes it easier. So obviously 1 minus that gives us 95 or 0.95 referring to 95%. And that is exactly the same. So all we need to now do is go sales times point or sales times 95%, obviously we reflect a 5% decrease. So all we're going to do here is at the front, we're going to go sales times by that. And that gives us our result, we'll copy it down, get rid of the decimal places. And yet just to put an easier number in here, so 100,000, you can see obviously a decrease 5% of 100,000 we know again is 5,000. Therefore to decrease by 5,000 would give us a result of 95,000. The next one we're going to look at here and we've only got two more. So please just bear with us if you made it this far. So find 100%. So we've been given these numbers, so we've got five numbers here. And we know that for this first example, our number of 50 reflects 5% of its total value. So we want to find out what 100% of 50 is when we know that 50 is equal to 5% of that number. So to easily do this, all we need to do is go equals, take our number divided by our 5%, which is not point 05. And that will give us our result. And for us, it's going to be a thousand and it's not 1000 pounds would change it to number. So if we go to general on this one, there we go. So you can see that yet we know 50 is 5% of 1000. So to obtain this result, all we need to do is take our initial number, divide it by the percentage that it reflects, or it is of that other number. And that's how we can then get our 1000 result. And as you copy that down, you'll see how all the rest of these ones work. And obviously some of these are easier than others. So obviously 200 is going to be 10% of 2000. So that's an easier one to work out. And the very last one, and I sort of did touch on it down here, is if you wanted to do any calculations manually, the easiest way is to just type them in. So let's say we wanted to work out what 5% of 100 was, all we need to do is go equals, take our value of 100 times by 0.05. And you get the value of five, as you can see there, just put this back to general. Alternatively, if you didn't have the number of 100, and you just wanted to do the type the maths in, so you're just trying to work something out, if you just typed in 100 times 0.05, again, that would give you your 5%. If however you wanted to do decrease by 5%, again, all you need to do is go equals the same calculation, 200, 200, being our number. And then we're going to times that by minus 0.05. So I've literally just done a minus sign there rather than the plus over here. And you can see that we've got that same result as well. So that gives us minus 10. And everything in between. So if you want to do obviously increase 100 by 100 by 5%, all you need to do times 1.05. And that will do the result for you. So I hope all that made sense. As I said, I'm going to make this document available to you via a link in the description. So if you click that link, you're about to download the document and get all the calculations you need. And then hopefully this, what the video will serve as a purpose of showing you just different calculations of percentage that you can either use, or obviously the answers if you did have any questions. And also if you've got the word book, you can obviously refer to that as and when you need, if you want to do any of these calculations. So I hope you enjoyed that video. If you did, please do give the video a like. I'd be greatly appreciated by myself. And if you're new to the channel or you have watched one of our videos before, please don't forget to subscribe to the channel and hit that bell notification button. So you're notified of all of our future videos. So lastly, thank you very much for watching and we'll see you in the next video.


is the setup of the problem I want to discuss today and hopefully I can convince you with that's not too unrealistic and maybe has some interest. So we're going to look at the very the easiest possible perceptron which is the linear perceptron which is represented by the circle which is trying to learn random associations between correlated inputs which are representing here as signals that involve in time and correlated output. So just to set up the correlated inputs are represented by this matrix x i mu and mu is you can think of it as an index of time or patterns and inputs have a certain mean which can depend on the pre so all that we can think of this as freezing up the neurons and sigma i's are the standard deviation and then we will set the correlation structure of these psi i mu which are the mean removed input and the same for correlated output okay so the output will have its own mean and its own start and deviation and then we will decide whatever correlation structure we want. So all of this boils down to solving the regression problem and I wrote the loss function here which in general depends on the input x on the output y and on possible regularization gamma which is an L2 regularization. So what you have to do is basically use some overall patterns and you can think of this as summing over time and then take this color product between the synapses w and x you add some sort of bias you can think of this as a constant current can be an excitatory current can be an inhibitory current and then you want your processing input to be very very close to the prescribed output okay so what we want is that we want these inputs to come from excitatory fibers that means that these weights should be constrained to be positive and the blue ones are constrained to come from negative fibers so they get multiplied by negative synapses and so that we think of wi to be greater than zero for when i is less than fe and so fe represents the the ratio of excitatory to inhibitory fibers, excitatory to inhibitory increasing of the neurons and the other ones are negative okay so now it's my job to sort of convince you this is somewhat interesting problem and the starting point will be uh training a recurrent neural network okay so we start from what i consider like the hydrogen atom of theoretical neuroscience which is the so-called scs model the Sombolinsky present the Somers model you can think of this as like a continuous time version of spin glass you just you take a random network you just draw a Gaussian recurrent weights this jij and you scale the variance accordingly and the dynamics of this network will basically like calorically wander around this very huge state space and this has been studied to death we know many of these using dynamical mystery and then what we can do from this sort of reservoir which we will use for computation is that we can try to read out something from from this random neural network okay so we we set up some some readout weights like I say which are indexed like a these are weights w I'm just representing one and if we let this thing evolve over time we will see that the some sort of spontaneous activity which which is when projected onto this random W network will look something like this okay let's say we want to uh transform this random network to what is called a functional network we want this network to do something maybe to respond to some to some stimuli in in some way uh the the very popular force learning algorithm what it does is that it just learns the readout weights so these weights w and then has uh some feedback from the readout to the the actual uh neurons you can think of this as a rank k when k is the number of readouts perturbation to the uh initial uh completely random uh connectivity metrics and if you use a very fast uh least square method like a recursively square you can train these weights and after training so after learning you you you will have the uh post learning the network is able to uh for instance self-generated self-generates and very complicated readout so what about the recurrent weights of course there are many ways there are all the stuff that we know from gradient-based learning and gradient-based learning in this case would be a way to solve the credit assignment problem so we given an output uh z of k which has to be equal to f of k which we decided as prescribed output uh we have to to assign a certain uh time course to the each uh neuron over time right so one way to circumvent completely this uh this problem which is net resolved by uh gradient descent is to restart completely and uh invent a second network which we will call the t-shirt network this is a non-train completely uh random network which receives as an input the output that we want so we want to generate an output here to read it out and so we feed these output to uh completely unstructured say a non-learn effort and then we take the target currents which is with the which uh this teacher network generates and then we match these currents which we read out from the teacher with those of the student so what the student has to has to do is that each neuron in the student has to reproduce the time course of the current so these current are representing here at h i s is the current generated by or impinging on a neuron i in the student network and it's basically like that the row of the geometric time fee and this fee can be whatever so in a rate network can be uh like what we call the rate activation in a spiky network can be uh the post synaptic potential where we integrate spikes of prismatic units and it has to be equal to the the target currents the h i t which are generated by this teacher network which uh gives which has an input with this random weights u k i the actual output f k that we want so by introducing by circumventing in a way this uh uh this problem of uh created assignment we factorize the problem at least along neurons right each neuron has its own targets and in parallel it has to learn some time course and so of course we can rephrase this as a regression problem so we have a loss function this loss function in principle is just a sum overall of loss function over every neuron and we have an output which is the current uh of uh in the teacher and h i is the current in the in the student that we want to learn we want to learn the j j's and possibly bias in order to match this to okay with some formal regularization uh i don't want to give you the idea that this only applies where we when you take the teacher you use it to generate the targets and then you throw it away to use the student so uh this can be used in general and has been used by christopher kin for instance has been used very recently by the group of carna carajan when you actually have some recordings uh from and you from neurons in circuit so you have some recording at the level of single neurons or you can have some recording at the level of population and so you have those targets which are not just generated by a random matter but the actual recordings and you want to construct a student say which reproduces those activity and you can do different things you can maybe you have some measurement on the current themselves maybe you just measure the the activity and then you want sort of to infer the current that that's what's kind of garage on for instance is doing to uh sort of infer the differential effect of different areas uh in this image her network of network approach okay so what happens if we uh remember something about the brain which is uh the brain respect base law which is the fact that uh that the the the courses uh differentiation and the course the courses division in the in the brain is that uh between neurons is that we have excited the universe and individually we have to consider a student which is divided into two populations so an excited population and inhibitory population of course we can use we can play the same game we have a teacher which is itself divided into an excitatory and inhibitory population and it receives as an input the same readout that we want to actually read out from the student okay this opens all the breakthroughs in this case because you have to solve many other problems there's not a it's not easy to like uh generalize regardless of the square to this constrained uh to when you actually have constrainable synapses uh calc suppression in the networks uh when they are in the balance of all balance regimes complicated and actually we studied this a lot especially uh Reiner and Gelfan as Van Gellicke are again sort of a complete uh dynamical mean field theory description of of of this problem and so okay let's jump right to the to the like the spoiler it's actually possible to solve this problem you can construct these models which have this uh this layer of biological possibility in the sense they uh they are composed of two different populations so for instance in here I'm I'm showing some results uh like a network of I guess 400 units 200 excitatory 200 inhibitor endurance that just uh oscillates over time produces this uh self-sustained non-linear isolation in this case you know sort of a functional network which receives two different signals with a with a delay period of delta tau and then responds with the pump which is exactly at the same uh with the same delay period okay so this is the actual response of the network this is the input okay so we can do that but then at some point we uh sort of ask herself the regime the dynamical regime which a rate network or a spike network uh would operate strongly depends on the kind of solution that we have for this regression problem so we we're asking each neuron to independently solve uh a linear perceptron problem we think so we have a resulting uh connectivity metrics and the structure of the connectivity metrics uh acts feedbacks on the actual dynamics of of the network so the the internal dynamics of the network we uh will depend strongly on the structure of the learned connectivity okay and so and now I hope that I motivated the fact that uh we can consider a linear perceptron task with excitatory and inhibitory constraint as a sort of a toy model of target-based learning okay we can consider like these targets coming from a random network we can consider these targets to be actual recording for instance okay so let's put some more ingredients just to formalize mathematically the problem we said that we have some correlated input correlated output we take the number like the length the time length if you wanted the number of patterns to scale uh with the proportionality factor alpha with uh with n which is the number of synapses the output is correlated with this metric cy and for the input we consider two different correlation one uh the first one which I call the ensemble covariance I don't know why I gave this name but ideally it means that you have the uncorrelated uh we have no correlation among neurons okay so that you have xy and u xy j nu there's a delta ij but you have only correlation which have been called semantic correlation or correlation in time if you want and the second one is the sample covariance uh we assume that we measure something we we measure the targets for instance and we do pca so in order to do pca we just uh take out the mean we normalize and we have the xi can be written with an sbd uh as us v transpose and and let's assume that we know s so we know the the spectrum uh if you want s square we know the variances of pca and we don't know anything about actually the direction of pca so we will average over u we average over p this is the model okay just two words for the method the the method that I use was just the the usual replica method maybe we can talk about this afterwards but basically what you have to do is you have uh you write a free energy and then the free energy is sort of a a generic function for all the all the important quantities that you want to that you want to uh measure say once you average over the distribution of the input and the output and technically speaking what happens is that if you consider um inputs which are uncorrelated across neurons but uncorrelated but are correlated in time what you can do is that if you have a stationary process the covariance matrix would be topless or if you want you can consider periodic boundary condition and it would be actually circled so in the end what you have to consider is just the power spectrum in in Fourier space and the actual error the regression error if you want is epsilon which is the density of the regression error will just be an integral over a lambda and you can think of lambda as like the square of singular values in this case or the eigen value of the covariance matrix or in other words it's just the Fourier spectrum so if you have a certain Fourier spectrum in frequency it will it will act on the structure of the weights and we act on the structure on the actual regression error that we will get in the second case let's say we we measure and we do pca and we have a certain a certain spectrum for the singular values okay and we don't care about the direction uh you and me so we integrate over the school r measure because we know that the the metrics of the left and right uh singular uh vectors of the basic octagonal matrices so we can integrate over these octagonal group we have this complicated integral and in the end what we get is uh the that the um the the result which describes this delta q which basically describes the the variance of the distribution of the weights and epsilon which describes uh the the the actual regression error will depend on uh in general on the stilties uh transform of the uh of the distribution of the eigen value so we we uh in the end everything will just depend on the distribution of the eigen value distribution of the covariance matrix which we sort of fix uh or we we measure it and then we can use this mean field regression if we have a measurement of uh when n is not actually going to infinity that is very big say even if you have n equal to 100 this kind of question work very well and um I think that methodologically speaking it's very interesting to to see that if you have iad output uh you can actually write this in terms of a quantity which comes again again in in the in random matrix theory and and it's been uh sort of studied from different perspective in average stack equations in the in the concepts of spin glasses in in the context of random matrix theory which basically is the integrated R transform which is dualistically connected to what people information theory uh called the the channel transform so you basically have these quantities which only depend on the spectrum of the covariance matrix in the end on what uh on pca which people in neuroscience do every day okay so uh so much for the method uh what are the results that we have so uh the important thing is that the uh the optimal solution for this regression problem are uh balanced weights so the the the notion of balance uh uh network comes to mind so a balanced network is one in which each uh neuron receives a very strong excitatory current very strong inhibitory currents but the two sort of balance and it's different in a case of sparse and and that's natural but the the basic idea is that they they balance at the level of the mean so that you have a current which is over the one but with fluctuation which are still over the one so you have Poissonian spiking for instance in the case of spiky and on spiking network and what we see is that at the level of a single neuron you get basically the the same scaling so if you have an external optimal bias which is the word of square root of n then uh so the average weight this we for instance and the average inhibitory weight there will be over the one with square root of n such that when they get multiplied by the average activity say the average input the average excitatory input in the average inhibitory input they cancel each other so the the mean contribution contribution from the mean gets uh canceled up to order uh one of a square root of n and it gives you the uh what you want in the output so the average output and and uh this only works if you optimize over the bias too so if you uh if you don't learn a bias for instance you get into the trouble uh of having uh yeah like a total current which is a function of n is always days of order one but uh this contribution which I'm calling h tilde which uh goes up and seems to increase as orders per root of n but uh is sort of balanced by by some finances balanced by uh by by correlations between the rates uh and the and the variation of the weights we respect to their averages so you don't get basically a balanced network which which have some very interesting dynamical properties so we can sort of in this very simplistic model you can already see that the optimization of the external current either gives you a solution which is balanced or not uh in terms of the of optimizing over this quantity i I think the second in the second interesting result is that there is a sort of a gauge symmetry once you take out this contribution from h tilde so once you learn the optimal current there is a gauge symmetry which implies that basically no matter uh what is the ratio of excitedly limited endurance and for whatever correlation in the input and output the optimal capacity for this perception is always 0.5 and here for instance it says gamma equal to zero gamma was the regularization parameter so at no regularization parameter you basically can learn and half pattern and then you start uh increasing like the the the regression error status increasing linearly and these curves are for increasing uh value of the regularization you have sort of a zoomed transition and this gauge symmetry also happens to uh impact the distribution of the weights so if you look at the optimal distribution of the weights once you optimize over the external bias you see this very uh strong delta function in zero and again whatever is the the ratio between excitedly inhibitor endurance you always have that uh in half of the synaptic weights are zero so you have in half of the excitedly and half of the inhibitory are zero and the the shape of the distribution tends to be a Gaussian with variances that you can control so I'm running out of time I'll skip this and maybe we can talk afterwards just to conclude I show you that target-based approaches are are effective for training recurrent networks and we can try and understand the structure of the resulting connections inside these recurrent neural networks just by using like very simple models very simple methods that come from the analysis of feed-forward networks and that's it thank you very much


Like a dumbass, again, I was talking to myself and I went through some of the steps. Okay, we're doing bottom of the doors. We're saving on the bottom of the doors, continuing the saving front forerunner or saving on the forerunner mission. Okay, again, you have the elbow pad goes right over here. OK, super easy. Watch this, get yourself a panel popper like that. You got the two clips. Make sure they are on it. They don't stay in there. And you have a Phillips chrome like looking screw here. Then with the hook tool like this, small guy like that, get in behind here, top rear corner and try to pry this away towards you, just go around. Don't break it.  Now we're going to try and pop this thing off. I hope that's only the two screws that are holding this door skin or door cover, whatever you want to call it. We're going to try and... This may be difficult, especially if this was never taken off. I see no screws. Once you get one, come on. I don't think there's any more screws. I could be wrong. There's sometimes screws behind the window buttons here. I never done the rear. I did the front other side. That was it.  Yeah, okay. Let me bring you in. It's probably better if the window is open. All right, so pop the window open. Just be patient. You got to be patient doing stuff like this. Because you don't want to break stuff. Okay, so first, first we're going to disconnect the light. Here I can actually see the plug through here. That's one. Then you have the top cable lines here. Come on. For the handles, the green goes on the bottom. The white is on top. One is the lock button. Get off now. And the other one is the handle. This is pretty self-explanatory, guys. Got to pop the ball out. And then we have the window button harness. So the door skin. So yeah, make sure all of these clips stay on the panel that none of them stay on the door. So double check, like it looks like, okay, the blue one here. So we got to remove that and put it back on the door. And this actually happened to me on the front door. You got to be super careful not to break this. These white ones seem to pop out, stay on the panel. But these blue ones tend to stay on the actual door. I'm going to remove the harness for the speaker. Now we're going to close the window. So we have access to the bottom of the door. The bottom portion of it broke. The top part is fine. So I'm going to pop that back on the door. It'll be fine. I'm going to remove the speaker. Kind of glued. Three kennel bolts only. Look at the size of this thing. And this is always nothing. And now we have a nice visual on the bottom of the door. Now, without breaking the foil apart, the older your vehicle, the easier this will be. If not, then what you want to do is you want to pull on the foil a little bit and start cutting the black stuff, the black gooey stuff. And now we're going to close the window. Controls from the driver's door will not work. We need to plug in the button. OK, it's actually not too bad in here. I'm going to get you in here in a moment. OK, so maybe you can see. I don't think the black stuff is dirt. Maybe too much light on the very bottom.  It's just dirt got stuck to it over time. It's what happens. But it was much worse in the front door. I'm going to get to that. I'm going to do the front door as well. But we're going to clean that out. Not all of it. Just scrape off the dirt and spray the wax thing. OK, if you're going to use something metal, like a flat screwdriver or whatever metal, make sure you don't spray too hard. Don't press on it too hard. You don't want to take some of the paint off because it's going to rust. So just carefully, lightly scraping, letting loose all the dirt, dust, and stuff that accumulated during the 15 years. You can actually see the bottom hole there, dirt falling down all the way from corner to corner. Because usually this corner right here that will rust. OK, I do got to say the rear door is much cleaner than the front. We'll see if it's going to be the same on the driver side. I did the passenger side. All right, so now spray away. All the way. Spray away, spray away. OK, don't be shy. Nice. Right here. This is what you want. Check that out. I did it all the way. What about to here? So that's it. I'm going to wait for it to dry for a little bit. Wipe the bottom off and put it all back together. And we're going to replace the seal. That's going to be a different episode. One tip, guys. Whenever you're ready, you've got everything plugged in. Check your work, if everything works before you pop it in. Check the window. Not much to check here. But make sure the top part is all the way down. OK, don't force anything. That's the last thing you want to do. Once this kind of feels like it's going to go in, make sure the clips line up. OK, on all sides. And then just, there should be no gap all around. Well, you are going to have a little gap from where you pulled before. Just no way around it. Just the reality. Check my work one last time. Window all the way up. Window all the way down. Make sure nothing is catching it and whatnot. I'm going to just check the door and opens from the inside. We are Gucci. Cut in the two screws. This little cover. Now we're going to wipe up the bottom. We're going to move to the front. All right, we continue with the front driver side. Same exact story. Pop this open. Seems like the bottom rear corner of this plastic always wants to come out. And then just go forward, pulling on it. And then, where is it? The two Phillips screws. You know, I bet you it was the broken-up window seals on this car that are causing all the dirt to come in from the outside to the bottom of the door, right? There is a push pin sort of deal. And these you want to, it's like this. You want to push this in and then pull it out. And then when you are ready to put it back in, pull this out, push it in, and then push the little guy in. All right, now it's time to pull on this again. This is never easy. There goes the clip. It's a good idea to pop this off too, the little triangle. Easy, one little clip. OK, again, green on the bottom, white on top, two plugs on the window and lock controls, and a little light here. The deal with the window controls is going on. What's in there? It's a dime. All right. I actually cannot see how this, another one? That's a penny. From 2018.  Oh, it's on the side. It's just great. In between the panel and the plug is the push thing. There we go.  See the blue one? Blue one stayed and one white one popped off. I'm going to take the blue one out and break it again. Ha. Well, where did you go? My lovely little missing clip. That's just great. It came out perfectly, and now it's missing. Man, are you seeing this? It's right here in a very convenient spot for me to find. All right, those are back on. I'm going to remove the speaker. Again, this is just, I mean, yeah. OK, so now see this one is stuck or doing what it's supposed to. So we're going to pull on it a little bit. I want to stretch it in the foil. And then we're going to cut the glue, not the foil. Cut the glue. Sometimes the glue comes with the foil. Now a sharp knife would be also useful. This one is so dull, it's not even cutting the gooey stuff. Fold it over, stick it right there. Bring back the zador panel. Check this out. This is actually better than cleaner than the passenger side. For some reason, the passenger side was, it had actual kind of sort of mud on the bottom. It was still wet. And I was so glad to clean that out because, you know, and it's not rusted. That's amazing. It was kind of a wet-ish moist mud or maybe muddy dust. All right, so we're going to clean that out. It's not too bad, but we're going to clean that out and we'll do the same. And then when it rains, some of the water gets through the window seals and goes inside the door and drains out. That's what these are for, these drain holes. And on the other side, these drain holes were nearly plugged up. OK, guys, we have sprayed the stuff on the inside. Let me quickly show you. Make sure it's leaking out. Then you know it's all covered. One more drain hole here. I also replaced the window seal. There's going to be another video on that. OK, now we're going to just, you know, just basically the same process, just a different door. OK, so we've got a situation. Lacks work. But the auto option doesn't. All right, guys, thanks to Canadian Gearhead. Thanks, man. This is no biggie because I disconnected all the window.  Then go all the way down, holding the button, and stay there for two seconds. That's it, let's see. That's it. We are good. All right, guys, that's it for the doors. Obviously, I've got two more, well, one more to go.  Look, I replaced the hatch in a guest struts. Look at it go on its own. Before it would stop, we're right about here. Annoying. Nice. And these are from Rock Auto by FCS Auto 86107, blah, blah, blah, both sides. Got some tires. And let me show you what I have found inside the hatch. Look, are you seeing this? You probably are not, but I mean, there is just stuff coming out. Yeah, so sometime in the future, the hatch, I still got to take the front bumper off and paint the bumper beam, you know, with the front frame and stuff like that. All right, update soon.