All right, welcome back to operating systems So today will be a nice reprieve from your brain breaking from having multiple processes running Different address spaces file descriptors all that stuff So for today has anyone ever stood in a line before a Line getting food. Yeah. All right. So if you've ever stood in a line before This lecture will be fairly straightforward. So we're just going to be talking about lines today pretty much all day So basic scheduling First we can do some boring stuff. So there's two types of resources on your machine Preemptible resources and non preemptible resources. What does that mean a preemptible resource is something that can be taken away at Any given time and shared so a CPU is an example of a preemptible resource I can stop a process from executing let another process execute for a little bit go back and forth Maybe if you have siblings, you had a preemptible resource some toy or something you could share that toy Whatever so it's shared through Scheduling so you can schedule time with that resource and then each of you can take turns or do whatever a Non preemptible resource is something that can not be taken away without some type of acknowledgement So for example this space if I'm using a gig Gigabyte for a file. I can't just say hey you can go ahead and take a hundred megabytes of it and Just give it back to me when you're done because that wouldn't make any sense. They'd use it You'd lose your information. You wouldn't get it back. It would just not really make sense Another example of this something like memory actual physical memory So you allocate and you deallocate memory. So you use it and then when you're done with it you return it So for like super high-performance computing if you ever get into that sometimes you might find out that CPUs are a non preemptible resource. So you can actually allocate the CPU to your process and then you are guaranteed to be the only one with it and You will have some nice performance characteristics with it because you know nothing else can ever take it away from you But that's not in this course in general CPUs are preemptible. You have to share So there are two things. There is a something called a dispatcher and a scheduler that works together We've already kind of talked about the dispatcher. It's just the name for the low level thing that actually does the context switching So whatever is saving state and loading state that is doing the context switching to switch between Executing two different processes. That's what the dispatcher is doing So the scheduler is the high-level policy that we will talk about today And that is responsible for deciding what processes to run and sometimes when So we can start this super simple the scheduler will run whenever a process changes state So we can first consider non preemptible processes. So a process just runs until it is done So this is like uniprogramming systems It just runs until it's done the scheduler only makes decisions whenever a process Terminates exits and then the scheduler has to pick another process to run So preemptive just so if you ever run you name dash V before or looked at it That will tell you like the date your kernel was built and like options of it one of the options that will show you is preempt So now you know what preempt means it means it's a preemptible kernel. It can interrupt processes at any given time So when we talk about scheduling there are four main metrics So the first one is minimizing waiting time and response time So you don't want to have a process waiting around too long So like it wants to execute you want to give it the CPU hopefully as quick as possible and Let it finish and let it terminate as quick as possible as well you also want to Maximize CPU utilization. So you don't want to have a CPU idle So that is especially important if you have multiple cores on your machine You don't want a core idle because it's not really doing anything The other one is throughput so throughput is just running as many processes as possible and ending them And the last one is fairness Which is kind of at odds with all the other ones So you want to try to give each process the same percentage of the CPU and essentially treat them all fairly So first algorithm not really an algorithm. It's first come first serve. So That is actually a form of scheduling. So It's just means the first process I arrive gets the CPU Processes are stored in a FIFO queue first in first out if you don't remember what that means So it's just like a normal queue if you go and get food So It maintains this queue in arrival order. So whatever arrived first is at the front of the line So throughout this yep So for now, we'll just talk about a single CPU core. You just run Single core at the time you get the whole CPU So for this we'll consider these processes. So let's consider four processes I'm super creative with names. So I named them P1 P2 P3 and P4 And then this column is the time they arrive at I'm not going to use time units I'm just going to use like just Just atomic time units, so I won't use seconds or anything like that I'll just do it in time blocks that I won't have fractions of just to make our lives easier So they all arrive at time zero and then the burst time that might sound like a weird term burst time Just means how much time will this process actually execute on a CPU for until it's terminated So process one wants to execute for seven time units process two for four process three for one process four for four So since they all arrive at time zero, I have to explicitly tell you their arrival order So here I'll assume that they arrived P1 P2 P3 P4 So if we have first come first served, this is our schedule So we just run P1 because it was at the front of the line It runs for seven time units Then whenever it's done terminating Well, whenever it's terminated then we have to pick what process to run next in this case P2 is next we'd run it for its four time units then after that P3 is next for one time unit Then P4 finishes it off for four time units, and then we're done Any questions about that schedule? Fairly straightforward. Yep So in this case, this is like the true burst time This is how long a process will take which on a real operating system. You won't know that, right? So we're just setting up a framework for dealing with this generally you You analyze Scheduling stuff after they've already completed so you know how long they run for and then you kind of do like a Postmortem and figure out, you know the average response time and all that stuff So in this case if we wanted to figure out the average waiting time Well, process one waited around for zero seconds Process two waited around for seven seconds because it also arrived at time zero Process three waited for eleven process four waited for twelve. So to calculate average waiting time No tricks twelve plus eleven plus seven plus zero divided by four So in this case, it's like seven point five. I think So there could be a slightly different order So if we assume that our order is p3 p2 p4 then p1 Now this is our schedule. So we'd pick p3 runs for one then pick p2 Which runs for four then p4 which runs for four and then p1 which runs for seven So if we did the same thing and calculated our average waiting time again It would be zero plus one plus five plus nine So if we calculated it out, it would be three point seven five or half of what it was before So you might think well first come first serve is pretty bad Because the average waiting time varies wildly between just whatever order the processes happen to come in which generally isn't Generally, we want our systems to be fairly predictable. So first come first serve It's an algorithm. It's not the greatest So If we wanted to minimize average waiting time, we could do something like shortest job first So it's a slight tweak where we always schedule the job that has the shortest first time first Again, we still don't have preemptions So the only tweak I'm going to do here is change the arrival time of the processes so that they don't all arrive at time zero so P2 arrives at time 2 p3 arrives at time 4 and P5 or p4 arrives at time 5 Then we'll just use the same example throughout the rest of the lecture just to compare them So for all that I like always like drawing the arrival time on the top So here I just draw it on the Gantt chart directly just to make things a bit easier when you have to schedule things so Our algorithm at time zero There's only p1. So it is the shortest job because it is the only job So we would schedule p1 for seven time units and then while it is executing p2 p3 and p4 would arrive so at time seven we get to decide which one to run between those three processes and The one with the shortest job is p3. It's only one time unit So we'd execute it for one then at time eight Well, there's a tie between p2 and p4 so to break the tie you can just pick whoever arrived first. Yep Yeah, so the question is hey at time p2 at time 2 it's shorter So can I just preempt it and switch to it that would be better But right now we're doing non preemptible. So we're not allowed to preempt it So so that will be our next week though. So Hold on so If we do this and then we calculate the average waiting time it just changes a little bit because some processes do not actually arrive at time zero so P1 its average waiting time is zero it arrived at zero and Waited for no time units p2. It's average. It's waiting time Well, it's came arrived at time two and it waited for one two three four five six time units until it started executing P3 while it waited for one two three time units before it started executing and then p4 waited for Seven time units because it arrived at five didn't start executing to 12 12 minus five is seven. So it's average waiting time now is four so The next tweak well first off. This isn't super practical This isn't practical it provably optimizes the average wait time if there's no preemption, but On a real system, you're not going to know the burst time of every single process Even if you did it might change from process to process you This is something you could think of you just throw a I at maybe it would be good at it but the longer the schedule take takes the worst because it's also counted as overhead so you want it to be as fast as possible and Also, there is this issue of starvation. What does that mean? Well, that means that some processes might never execute So you can't imagine if some system Constantly gets a bunch of really short processes and there's this long process Well, then all of the short processes will just run Hog the CPU and that long process would never execute So for some reason a process that never executes the term for that is it gets starved or your algorithm suffers from starvation So we can do the tweak where we add preemption so to add preemptions the shortest job first The algorithm just changes to shortest remaining time first so We also assume that our minimum execution time is one time unit, so I can only preempt on time unit boundaries So I don't have to deal with fractions and then similar to first shortest job first. This optimizes the average waiting time so Here is our schedule if we have preemptions, which was like your idea here so Time zero p1 comes in and we have no other choice, but to schedule p1 So we would schedule it for two time units and then p2 comes in at time two so at time two p1 has five time units remaining and The newly arriving p2 has four time units remaining because it hasn't executed at all So four is shorter than five. So we would schedule p2 So p2 would run for two time units then at time four p3 comes in So p2 has two time units remaining p3 only executes for a total of one so it has one remaining that shorter we'd schedule p3 So schedule p3 for one and then it's done Then p4 would come in so we have to choose between p4 p2 and p1 So p4 has four time units remaining p2 has two p1 has five So we would schedule p2 It runs for four time units, then we'd schedule p4 then p1 So if we calculate the average waiting time for this so we can use a little bit of a shortcut So if you want to do a shortcut and not count all the blocks in between here Well for process one you can just figure out when it ended so it ended at time 16 and it started at time zero So that was the total time it was available to the CPU so that total time was 16 time units So if you want to figure out the waiting time just subtract its burst time from that total time so 16 minus 7 well, that's 9 so it must have been waiting for 9 time units So process 2 waited for one time unit right here Process 3 waited for zero and then process 4 waited for two time units right here So our average waiting time in this case is 3 which is you know an improvement over what we had before Without preemptions, which was 4 So any questions about that yep? Yeah Yeah, so for this I'm assuming context switching is instant for so in general one of the things you want to report with Your scheduling is how many times does it context switch? So we'll see that with our real algorithm. So this is as far as it will get today round robin so So far we haven't handled fairness and it's a trade-off with the other metrics anyways So we do something like round robin. So that's like if you have a toy sibling You each get 30 minutes with it You have it for 30 minutes you pass it around They get 30 minutes and then if there's a third sibling they get it and then it goes back to the first person Just goes around in a circle. So that's what round robin is but we have fancier terms for it So the operating system divides execution into time slices for some reason their computer scientists So they want to use fancy turns so they'll call them quanta and the time slice will be called a quantum Why I don't know they want to be fancy and sound smart really. It's just a time slice a set amount of time So with this we maintain a FIFO queue of processes similar to first come first serve and We just give each process a time slice and if it's not done yet We preempt it at the end of its time slice and add it to the back of the queue And then we just keep on going over and over again So what are some practical considerations for determining the time slice or quantum length? So I heard one already a bit if it's too low, you'll have a lot of context switches And if the time it takes for context switch is like even comparable to the quantum length You're just gonna spend all your time context switching and it's gonna be real bad So what is the opposite problem if my time slice is absolutely gigantic? Yeah, so What if the time slice was Even longer than the process ran for Yeah, so the the time slice you don't have to use all the time slice so if you end early and Terminate well the next process the scheduler would just know that and then schedule something else to run So it wouldn't be idle for the rest of the time slice Yep, so yeah the question what how would sleeping play into this so sleeping it would just give up its time slice probably Yeah, not maximizing my throughput close So at the beginning we saw a first come first serve right and said it was really kind of crappy So if my time slice is longer than What any one process executes for what does round Robin become? Yeah, it becomes first come first serve right if my quantum length is like eight years or something like that Then well guess what it's first come first serve because the process this is going to run until it's terminated and The only thing to differentiate them is the arrival time so There's a trade-off here. You don't want the time slice to be too low. You also don't want it to be too big so This is as hard as it will get today too. Well, we'll get a little harder after this, but We're going to do the same scheduling But we're just going to say for instance that the quantum length is three time units So this might look like a bit of writing so we will go over it So here we go with the same schedule. I will Have all the arrival times written on the top and then a nice trick to do for round Robin Is to write the queue on the bottom so you keep track of your queue at all the At all the time and then you know what to schedule next so At time zero p1 arrives. It would be the only thing in the queue So it would get scheduled and here our time slice is equal to three so We p1 is the only thing to run so we would run it for three time units Unless it terminates early, but in this case its first time is seven. So it's going to still want to run after it's three time units So we would schedule it for three time units and At time two our queue would be empty and p2 would arrive So it would be the first thing in the queue and Then here whenever p1 is done. It's time slice. It still wants to execute So we would throw it to the back of the queue so at time three our queue would be p2 then p1 So if we want to figure out what to run next, it's just whatever is at the front of the queue So the next process to run is p2 So p2 would run for two time or for its three time units and During that well at time four I would have p1 at the front of the queue and then p3 arrives So it would go at the end. So my queue would look like p1 p3 and then nothing and then at time five p4 arrives So it would go to the end of the line So we would have p1 p3 then p4 so At time six here. I have to make its Process two is done with its time slice. So I have to schedule another process. What one should I run? P1 it is at the front of the queue. So I would run p1 Also p2 is still executing. So I would put it Or sorry p2 is done. So I would put it at the back of the queue So I execute p1 for three time units It still wants to execute a bit more, but it has done its time slice So it goes to the back. So my queue would be p3 p4 P2 and then p1 now p3 would It's p3's turn. It only executes for one time unit then it terminates. So it the scheduler has to reschedule something and The new process wouldn't just get the remaining time slice. It would get restarted. So p4 would go and get four time units here and Then our queue would look like p2 p1 then p4 so the reason why p4 gets a brand-new time slice and not Just continue and it kind of just uses the remaining two is Well, if you were malicious about it and you could figure out How big the time slice was well, say the time slice was 10 units and Another process just got whatever the remaining time was Well, I could time my process so that after nine time units it does something that causes a sleep and then the scheduler makes a new decision and then gives that new process a single time unit and Then it reschedules me and I get my full 10 again then I go wait for nine go to sleep It gets one I get nine nine one nine one So you could kind of game the system if the time slice just didn't restart every time so in this case P2 would run for it's one remaining time unit Then p1 then p4 and now we're done. Yep. Yeah, so you're the Yeah, so so the scheduler is done by the kernel so p3 would terminate the kernel would know about it and just start a new time slice All right So now we have our fun math questions. So first would be average waiting time So our average waiting time. Well, we can do that trick again. So This is 16 So p4 ends here P1 ends at 15 P2 ends at 14 and p3 is It's somewhere there I can count so Process one was around for 15 time units. It executed for seven of those. So it must have waited for eight Process two was around for 14 here Minus two when it arrived. So it was around for 12 And it executed for four of those based off its burst time. So it must have been waiting for eight That is a really horrible eight Then process three Well, how long did it wait for? So it came in at time four and it waited for one two three four five five time units Then process four it ended at 16 Arrived at five. So it was around for 11 time units Executed for four of those. So if I can do math 11 minus 4 hopefully it was waiting around for seven. So my average waiting time would be all this Which would be seven So the next thing to calculate is the average response time So average response is again The response time is the time that the process arrived to the time it first started executing. So For process one it arrived at time zero and started executing immediately. So its response time was zero For process two it arrived at here. Where's the highlighter? It arrived at time two and it started executing at time three. So its response time was one Process three again waited around for the same five time units that are there highlighted in green and then process four arrived at time five and Didn't start executing until Let's just count One two three four five. So it also Its response time was also five So if we divide that by four we get two point Seven five something like that So the last thing that you calculate is the number of context switches So to calculate the number of context switches You just count the number of times in the schedule that we had to change between processes So we don't count time zero and we don't count time 16 so the quickest way to do this or the most obvious way is to just Do a red line whenever we change between processes. So we change between process One and two there then we change between two and one Then one and three then three and four and four and two Then two and one then one four Then to count the number of context switches. We just count the number of red lines So how many context switches do we have there? seven so That would be What you would have to do on the exam. Yeah The numbers look horrible Why they look horrible So the other methods we introduced before aside from first come first serve you couldn't implement them Because you don't know how long a process is going to run for so Yeah, so so far. This is the first one we can run so in order to make this The hardest thing we can do today We'll do the same thing round Robin For the time slice is one So this is as hard as it gets for today and as hard as these scheduling questions get so Starts off the same thing drawing the queue at the bottom is going to save us. So Process one It arrives at time zero. It's the only thing in the queue So we would execute process one whoops Then it's time slice is one it would go to the back of the queue guess what it's still the only process So we schedule process one now we have an instance of a tie so You might be Think we could do process to process one or process one than process two So if there's a case where you're re queuing an old process and a new process is arriving at the same time You favor the newly arriving process Why well the other thing already ran and if I favor the new process I'm going to get an overall lower average response time. So Generally for these tie-breaking rules, there's generally a reason for them So the why in this case is well, I'll favor the new one because I want a lower average response time So P2 arrives So we would favor that newly arriving process and then put P1 behind it So that would be our queue at time two. So we would pick P2 to run immediately So P2 would run It would get thrown to the back of the queue then P1 would run Then same thing is going to happen. So P3 arrives. So P3 doesn't get to butt ahead of P2 because it was already at the front of the queue whenever it arrived So it doesn't get to butt ahead or anything like that so after that We would get P3 would go because it's the newly arriving process and then our re queue of process one so Then we would have scheduled process two to run Same thing is going to happen P4 arrives. So our queue would have been P3 P1 Because they were already in the queue when P4 arrived Now P2 is getting re-queued So it would be favored over the newly arriving P4. So our queue would now look like this So after this P3 would run Mercifully it only runs for one time unit So it would be done and our queue now gets shortened to P1 P4 P2 so after that Then P1 would run P4 would run and Hopefully at this point I don't have to redraw the queue because it's just going to go around the round in the order 1 4 2 1 4 2 until eventually they're done so at this point with process 2 it has run for a total of Three time units so it's thankfully almost done So we go another round of P1 P4 P2 and now P2 is Mercifully done and our queue would look like P1 P4 So now we just cycled between them P4 P P1 P4 P1 P4 and now they are mercifully all done So this is as hard as it gets. Yep. Yeah, so Right here if if we could take like fractional So like time three and a half our queue would just be P2 on our queue Right, that would be the only thing in our queue So if it's the only thing our queue When time four rolls around well We just put P3 the newly arriving one after P2 and then the re-queue of P1 at the very end Yeah Okay, so now we have to do the fun part Average waiting time Thankfully I showed you a shortcut which is much easier to do when you are writing it than if I am explaining it to you so That's 16 P4 ended 15 P1 ended To do This is 14 13 so at 12 P2 Ended So average waiting time so P1 was around from 15 to 0 So it was around for 15 time units its burst time was 7 so it waited around for 8 time units Process 2 ended at 12 Started at 2 so it was around for 10 time units minus the 4 it was executing 4 so it must have been waiting around for 6 time units Process 3 while we can just look at it up there. It arrived at time 4 started executing at time 5 So it was only waiting around for one time unit Then process 4 it ended at 16 came in at 5 so it was hanging around for 11 time units 11 minus the 4 it must have been executing 4 means it must have been waiting around for 7 So divide that by 4 processes our average waiting time is now 5.5 So which compared to before? Hey, it went down a little bit. So from 7 it went to 5.5 Next fun one is the average response So in this case P1 started executing immediately its response time was 0 P2 also started executing immediately it came in at 2 started executing at 2 so its response was 0 Process 3 while its waiting time was 1 so that was also its response time It was just waiting around for one time unit Last one is process 4 which process 4 we can see here was just whoops wrong color It was just waiting around for two time units came in at 5 waited for two time units So if we crunch the numbers here our average response time is now 0.75 which is way better than before which was 2.75 So last thing we have to get is The number of context switches which is real fun So number of context switches Same thing will use our good old red line and start counting So 0 doesn't count so and also Changing from process 1 to process 1 that is not a context switch So the scheduler would be like same process not a context switch. So Whoops, so this is our first context switch here. So our first context switch one two three four five six seven eight nine ten eleven twelve thirteen fourteen So fourteen context switches So that's as hard as it gets so lots of context switches here But we can see the trade-off of more context switches, but our response time was way better. Yep So remember we talked about context switching just being essentially a waste of time so For real systems context switches will be less than like 1% of the total time Execute Yeah, which is fairly negligible, right so So if you if you're tweaking the time slice number and then you see that hey 50% of the time is spent doing context switches Well, you're not in the 1% range So you should probably increase your time slice because you're switching way too fast So there's always going to be some type of trade-off here. Whoops. So any questions about that? Fairly straightforward something we could do in our exam get some free marks feel good about ourselves Much better than the the forking examples where I can just like throw a billion forks at you. Yeah, so this is like This is the easy part That shouldn't be too bad and then you know, you'll get a fork question and then while you're thinking about it You can do this question so Could go through do it again if you really want quantum length of 10 units Guess what that becomes first come first serve if you want to count the times again I don't suggest it, but if you want to practice it go ahead and These are the numbers you should get for when it becomes essentially first come first serve So like we said before so the round robin performance depends on the quantum length and the length of the actual processes so Generally is low response time good interactivity. It's fair low ish average waiting time when the job lengths vary But the performance depends on the quantum length. We have to set it. It's kind of difficult to set in practice Too high becomes first come first serve too low Too many context switches which is just overhead and in general it also has if you simulated the bunch It has poor average waiting time if the jobs are all fairly similar length so That's it scheduling just involves a bunch of trade-offs We saw some different algorithms if you could even call them that first come first serve Which is a valid scheduling algorithm if you just have to make a decision Then we saw a shortest job first if we want to have no preemptions and minimize waiting time And we added preemptions tweaked it a bit got shortest remaining time first then saw that that Minimize waiting time then we saw round robin which is more fair. We can implement it. Yep So the cause of so overhead is just the amount of time Wasted not doing useful work. So if you were the kernel your useful work is running applications The context switch on your system is pretty short, but if you get to pick the length you can pick wrong If you get to pick the quantum length you could pick wrong, right? So you could pick the a quantum length that's too short where the context switch time matters So you don't want to do that So yeah, any other questions? We can end early take a sigh of relief some of you have been working on lab to see you can do that. Yeah Yeah, so shortest Shortest remaining time first not going to be used because we don't know the first time of processes for one and then another reason is that starvation is an issue So we wouldn't use it and you wouldn't actually know the times ahead of time So it's like kind of an after-the-fact theoretical thing. Yeah So it just restarts a new time slice because if you knew the quantum length you could essentially game the system where say it was like Ten time units you can just wait till you're almost done your quantum length give up the CPU then some other Process just gets it for like one time unit something really short And then if it's just you another process you get it back and you get a full new time slice And then you use it almost all of it. Let them get the remainder then you use all of it I punched the microphone again.  Yeah, so in general for round Robin.  Yeah. Yeah, you don't want anyone gaming the system or doing anything weird So with that, oh, yeah, yeah, so this is assuming a single CPU core So we'll get into more advanced stuff tomorrow, but for scheduling everything's just a big trade-off So there's no right answer for scheduling. So just remember pulling for you. We're all


Thank you. So good morning everyone, we changed totally the topic. So I'm fluvial geomorphologist, and I use models, and very shortly develop one so I will start just by defining what industry in large wood is, and why it is important. So why do we care about this, and then I will focus on the first numerical model that was developed to simulate the transport of wood in rivers called Iverwood. I will show you how we validated this model using data from flume experiments and field observations. I will show you also two main applications. So how we use the model to explore wood dynamics in rivers and better understand rivers in general. And also I will focus on the related hazards or potential hazards related to wood transport. So industry in large wood refers to down trees, trunks, root buds and branches that fall in a river. So they are the base of the rivers organic matter supply and in some rivers together with the living trees. They are a major reservoir of organic carbon. So industry falls in a river. It triggers a lot of processes so it creates an abstraction that is a source of flow resistance and energy dissipation which is affecting the hydraulics so it's usually lower in the flow velocity, increasing the water depth and affecting the flow resistance and so this has an effect on the sediment dynamics and is influencing the sediment storage and sorting, and it's also affecting morphodynamics so it may enhance channel mobility shifting and it may influence channel widening river bank erosion. So wood adds physical complexity to rivers and so improves habitat diversity. And this is not happening just at the very local scale. It's actually influencing river processes, forms and functions across scales. It may affect channel and flat plain connectivity and eventually in the long term is influencing fluxes at the catchment scale. And so wood is also affecting the water chemistry and temperature. It provides a hard substrate for the biofilm to colonize. It provides food shelter and nursery areas for aquatic species. It's also influencing the establishment of vegetation and the vegetation sections and the development of islands in rivers. And it's also the combination of the or the interactions between the flow, the sediment and the water provides better conditions for many species and not only aquatic species so wood is influencing the entire food web. And so this is why wood is a key element sustaining river, rivers health, and also providing resilience to river ecosystems. And this is the reason why after centuries, removing wood from rivers, we are actually seeing an increase in the rain production of wood to rivers worldwide. So wood is increasingly used for river restoration. Although most of the time wood is stable in river channels as sediment, during flats, large quantities of wood might be transported. And so this is an example from a river in Utah. And so where this wood goes, it might encounter obstacles like critical sections like bridges or dams, and so make us some hazards. But what is really important to highlight here is that none of the hazards posed by wood are inherent to the wood itself so the wood is not the problem. The problem are the infrastructures that we designed to not allow the wood to pass. And so we are artificially enhancing the trapping efficiency of rivers and the transport capacity. And I will go back to this later. So there is a vital need to understand wood dynamics and integrate this understanding in the way we manage rivers. And so this is why we recently defined the wood regime in a similar way than was done for the flow and the sediment regimes. But compared to the flow and sediment regimes that have been studied for decades, even centuries. The science or the research in wooden rivers is relatively young. And we face a very important limiting factor is the lack of data. So there are no gauging stations monitoring would transport. This is also why we still face major scientific questions that might sound quite basic but we still don't fully understand how wood moves in rivers. How much good we can expect to have during a flood when might be transported where it goes and where it's deposited and what are the potential impacts and hazards. So numerical modeling is a powerful tool to explore these questions. And so before talking about either wood, let me introduce you either. So either is a 2D hydraulic model that was developed. So to simulate the free surface flowing rivers and estuaries. It was developed mostly by them by scientists at the University of Catalonia and La Coruña in Spain. It is distributed for free. You can download it on this website. It is both in English and Spanish. And the first version was released in 2010. So here I listed some of the main characteristics of the model. So hydrodynamics are solving the sandstone and equations in two dimensions using an explicit finite volume scheme on unstructured measures. It solves of critical and supercritical flow. It's conservative. It includes several models for turbulence. It solves morpho dynamics and sediment transport. And it is integrated in a user friendly pre and post processing interface. So as with any other hydraulic model either has been applied for studying hydromorpho dynamics and assessing flat hazard and risks. But also for eco hydraulics and fish habitat and water quality and it has many modules that allow the model to solve or to study other processes like dam reaching hydrological processes, pollutant distribution, or it also allows the simulation of non Newtonian flows like no avalanches or debris flows. So a few years ago, already 10 years ago, we coupled a module to simulate the transport of wood and we did this by coupling a Lagrangian scheme so a discrete element approach to the Eulerian model. So I will not go into all the details but so we simulate individual pieces of wood and the incipient motion of these pieces of wood, assuming them as cylinders with or without groups is based on a force balance and so we have the driving forces, the drag forces and the gravitational forces and then the friction the resisting forces and these are the main parameters that are considered in this equation in this force balance. So we have the density, the size of the wood, the water depth, the flow velocity and some coefficients the friction and the drug coefficients. So with them, we have two different types of transport mechanisms so wood may slide or drag on the riverbed or may float float. And so the model also includes interactions between the wood and the geometry so the morphology of the river interactions between the wood themselves by assuming elastic or an elastic collisions. And interaction between the wood and internal conditions like bridges. I said that the module is fully coupled. So this means that the presence of good has an influence on the hydrodynamics and the hydrodynamics are controlling the motion of the wood pieces and we did this by adding a drag to the sample and the question. So, since we started developing this model, as I said more than 10 years ago, we used observations to validate the model so the first validation we did was in a very simple setup in a very straight, small channel with some obstacles and constraints to get some recirculation songs, and we focused on the trajectories of floating cylinders. And then we compare their trajectories rotation velocities. And so here I show you one example of this comparison and as you see the model was capturing the behavior of surf in the flow. Recently, we added more complexity to the model. So we validated the behavior of not only the floating logs but also these dragging logs. And so we use a braided flume with a complex morphology, which is characterized also by shallow waters. So in these systems, the wood is not only floating is also dragging top of the bars and on the channels. And so here we focus on validating travel distances, the accumulation of wooden jams and the effect of roots. So this is an example of one of these runs that we need to reproduce the flume experiments. And here I show you in this box plots, we compare the flume observations with the numerical model, focus on the travel distance for different scenarios using different sizes of wood. And so you see that we, or the model, again reproduced the patterns of surf in the flume, of course with some differences and the main difference here was that the variability of surf in the numerical model was much smaller than in the flume. Still, we have some ways to add kind of stochasticity to their numerical model, but I will not go into details. So I said that we lack of surveillance so we are trying also to fill this gap, and we are doing field surveys. So we are using metal and plastic tax to track boot in rivers but we are also using radio frequency transmitters. We use drones and cameras, and we also use citizen science so we use videos of people just recording plots with a lot of wood. And so we extract quantitative information from these videos, and we are currently developing machine learning algorithms. So we are using deep learning to automatically detect wood, detect and track wood on the drone imagery and videos from video cameras. So we use this information to get the positional patterns and wood fluxes that we can use to validate the model. So two main applications now, one application to better understand wood dynamics. So we use the model in a multi-scenario, multi-run approach, and so we compare wood transport in contrasting river morphologies. So we compare two river reaches, one single thread channelized and another one multi-thread rated river reach. And so the first thing we can observe in this graph is based on the transport ratio. So meaning one means wood transport, so all pieces entering the ring, the reach are going out. So we observe a significantly higher transport capacity or transport ratio in this channelized river reaches versus the rated morphologies. But we also observe that the capacity to transport larger pieces was larger in this straight channel. So here we again have the results from different scenarios. And so we observe that in these straight channels, the flow is able to move very large pieces of wood while in these rated systems, all the small pieces are mobilized. And so by analyzing rivers, what we are doing is artificially increasing the transport capacity and competence of the flow to move the wood and potentially enhancing hazards downstream. We also analyze the position of wood. So by again running multiple scenarios, we compute it at the positional probability. And so we observe that this probability to, so this is the positional probability changes with these charts. And during high flows, the natural place for the wood to be deposited is the flat length. And so again, by modifying the connectivity between the channel and the flat plane, we are decreasing the position of wood and then increasing the transport downstream. In this other example, we analyzed, we simulated the confluence of two rivers, one of them supplied with wood, and we explore interactions between the flow, the wood and sediment. And so in this graph, you have water elevation and critical diameter, meaning the size of the sediment that the flow is able to move. And so if we compare to scenarios with and without wood, and so if we focus on the water elevation, so the gray lines, we observe that the presence of wood increased the water depth downstream from the confluence and lower the velocity. And now if we look at the critical diameter, we observed also decreased in the sediment size that the flow is carrying. So wood influences the flow transport capacity. Regarding the potential assets, one of the first works we did was to use the model to identify critical sections. So in this river crossing a town where several bridges are located, the model allowed us to identify the one that was more prone to trap wood. And we, for the first time, used this to include wood transport in a risk, in a flat risk assessment. And so we quantify the potential losses, and we compare the scenarios with and without wood, and we observe that the blockage of bridges cause up to 50% more economical losses for the same discharge. So the bridge clogging or the blockage probability depends on many factors. So the model allowed us to explore these different factors, and one important factor is the discharge. So again, running multiple scenarios for this river in Poland, we computed this blockage probability and we observed that in general, the blockage probability increases with discharge, but until a certain threshold. This means that for very extreme floods, the blockage or the presence of wood might not be so relevant. The flood is huge anyway. But for intermediate floods, the transport of wood may significantly increase the flooding. Not only the discharge also, of course, the amount of wood and the size of the wood is important. And again, here we found address hole, which is related to the size of the wood relative to the size of the channel or the bridge. There are other processes that might be happening when a bridge is blocked, not only this backwater effect or this flooding, but processes like erosion, so a scour or widening. So we explored this in this river in Chile that is heavily affected by volcanic eruptions. So there is a huge supply of sediment and wood. And so we again use the model to explore this flow of wood sediment interactions and we observed that by blocking the bridge with wood, we enhanced the scour, so the erosion we see in this graph here. And the model also allowed to see that among the different piers, the bridge piers present within the channel, one of them was actually more prone to trap the wood and this was observed during a past flood in 2015. So just to finish, I would like to stress that, of course, the model is just a simplification and we are trying to use a deterministic model to reproduce a quasi stochastic process. And so still there are many limitations and at the same time, but we can use the model to test hypotheses, run multiple scenarios, and we can use it to understand processes that are very difficult to observe in rivers. And with this I finish and I thank you for your attention. This is an excellent overview for flu field of flu field your morphologist and I think it's linked well to Kyle's talk of yesterday. We'll allow for one question before we move on to the next talk. If there is one. Thank you for the great presentation. So I have the two questions. The first one is related to the model so your model use different types of measures I mean like a mesh for the fluid other one for the sediment for the good. And the other one is what type or technique you use for analyze the fluid in the experiments. So the model uses two different measures one for the hydraulics and the morphodynamics and one for the wood. You'll see Salagrandian mesh. So yeah, we have to couple the two measures. And the other question is the techniques about the technique for analyze the flow in the flow in the experiments. P I B or something. Yeah, we use measurements of like flow velocity measurements, ABCP. And, for example, in the first experiments I show this we did ourselves in the lab, the other ones in the brightest flume. And before and then we use the data afterwards so I cannot tell you all the details but some of the experiments were recorded and we used also the video frames and the recording. Thank you so much.


Block me three percent, I'll touch that. It was just a play I was manned up on the end, and I've been waiting on that all week. Praying that they, that's how they were running, and they did, and I was, you know, like I said, manned on him, and I made a mid-play. Kyle, on the 39 yard to get that last drive started, you know, buff us through that play? Good. Uh, it just was a, uh, like a play-action play, and, you know, Matt took the chance on me and threw the ball down the field. Matt was talking, he's crediting you with just staying ready and learning and building and so forth. How's that process been for you to see, you know, big time results here today in London? Uh, that's good. That's just something he's been preaching, just staying in the course every day, day by day. Trying to be the best player I can be, and, you know, the preparation will come out during the game. And how does it feel to, you know, put one of these big games together?  It's definitely exciting for myself and for the team. You know, the team we have is a great team, and we're going to get things rolling.  How do you feel that it was you today? I'm pretty blessed, but, you know, I'll be ready for those guys to come back. Thank you. Kyle, it seemed like you had a big group over there that you went and said hi to up in the stands after the game. Who all was there? That was my family, mom, dad, sister, girlfriend, and my cousin. I mean, that's a lot of people that come across an ocean. What does that mean to you to have them here and to have this kind of game in your first touchdown in front of that big group? That means a lot. That's my support system, so to just be able to do it in front of them and just, you know, even better for myself. We've asked you a bunch, you know, about being patient and waiting for those big games to come. And you said it's no big deal for you, but is there kind of a sense of relief or something to get that big first game? How do I get that first touchdown on the board? I would say it's a relief just to be able to just start there and, you know, know that they can go higher from there. So just reset next week and when we go and play Miami, just have a better game. Have you seen the coverage of the plays? Have you seen that coverage on Tate? It's been very exciting. Yes, I've seen it on Tate, but, you know, sometimes on Tate, they don't run the same thing on the game. So just, you know, they stayed true to it and I was pretty excited when they came out like that. It was definitely a great environment, great time. I got to go out and see the town and eat some different foods, see some different places, so it was a great time. Just, you know, having to get off that flight and then going to practice, you know, staying in the courts, still sharpen our details in practice and being able to go out and perform the best we can. Thank you. Awesome. Thank you, Kyle.


to welcome tonight for our first talk on the Dark Universe, Professor Sarah Tuttle at the University of Washington going to tell us about dark energy. So please welcome Professor Sarah Tuttle. Before I get started, here's what I need all of you to do. I need you to put on your clothes classes. Are you ready? Are you putting on your clothes classes? Yeah, putting on your clothes classes. But yeah, it's not that hard guys. If you don't need directions. You ready? So if you wear those, you won't know what's happening. Alright, so tonight, let me get my buttons. We're going to be talking about the entire universe. I think we can get through that in like 15, 20 minutes, right? You guys ready? Also, you've been primed by the quiz. So I'm assuming you all got that right. So we'll do a review in the middle because we're out for a visit and we have to do that one. So, hopefully you're ready. Dark energy, dark matter, and honors. Thank you. There's so many honors it almost wouldn't transfer. Alright. First of all, what is the universe made of? Honors. Honors, good. I like how you think. Anything else? Hydrogen. I see some people are taking this exercise seriously. Thank you. Yes, indeed. There are lots of things in the universe. Many of them you have mentioned. We have molecules and atoms and fear and honors. To be able to keep track of what you've noticed, and many of the things are things that we interact with on a regular basis, right? So if I were to ask you, like, make a list of the top 10 things you interacted with, I know, honors would be on the list, but, you know, it's like the table and my clothes and the ocean, right? So that's fantastic. We like counting things. That's one of what astronomers do best. In fact, you can think of us a little bit like the universal accountants. Doesn't that make us have even more exciting? That's right. It's not quite dark enough. Maybe we'll revisit this once the sun sets. We've got awful northern lands. But this is the universal energy budget. Now, you might think if you have, for example, recently done your taxes, your budget should be more complicated than this. It is not. It takes a pie chart. I know. I'm so sorry. So here you can see the three main constituents of the universe. We have dark energy. There's nothing there because we don't know what we're talking about. Chocolate cake, also known as dark batter, and honors. So honestly, I think I could stop the talk here because this seems like both adequate and everything we need. Dark energy is almost 68% of the universe. Now, if I were you, I would be concerned because I've just told you that most of the universal energy budget is dark energy and we don't know what it is. It could take more than 15 minutes. Dark matter, we can measure and observe, but we don't know what that is either. But we do have some honors. So that should be really good. So let's walk through what we're used to and what that means. And we'll talk a little bit about dark energy and dark matter. One of your key takeaways from this talk will be being able to differentiate between dark matter and dark energy. So the next time you're at a cocktail party and someone gets it wrong, you can come down on them like a ton of bricks. But even 5% of the universe, you know, the universe is big. There's a lot there. That makes a lot of room for honors, right? You're feeling retro and pretty good about it. You've still got like some honor space. You guys, there's not that many honors in space. I'm not actually my disappointment. So, I'd be making a few. There still could be space honors. I'm looking for graduate students. So if someone would like to complain me, space honors would be great. So what are we looking at in this picture? This is part of the Hubble Ultra Deep Field. I have a test for you. Are you ready? Now, not counting the stars that are in galaxies, but there's billions of stars in every galaxy. How many foreground stars, so stars that are in our galaxy, can you see in this fantastic image? How many? Oh, we've got hands up. You guys, I've got some zero guesses. What else? Two. Zero. That's not a four. There are two. Two. All of the rest are galaxies. All of them. Isn't that ridiculous? This is the size on the sky of a dime held out at your arm's length. You know, assuming you have like standard arms and not unusually T-rex arms. There's some air in there. But fall for it. Okay. So, for a second, we're going to delve into the honor portion of the budget. Since I have exposed the lack of otters so early on in this talk, but hopefully you've had enough fear that you're going to sit for a little longer. So here, oh, you can't even see that. That's tragic. Okay, good. Well, you'll never know what you're missing. Is there a missing baryon? I'm sorry. So, most of the baryons and the standard stuff in the universe isn't the stuff we're used to at all. Even in that little 5% otter sliver, it's mostly diffuse, warm or hot gas. This gas, which is so warm and so far apart from other stuff, it can't collapse to form things we're used to, like stars and galaxies. So that's most of it. There's a little bit of cold gas. It's like leaky refrigerator over here. And this is galaxies and stars and otters. And then there's the missing baryons. Okay, so we're like ten slides here. What I'm telling you is we don't have any idea what we're doing. I'm so sorry. That's not really true. We know some things. And it keeps us busy if we don't know things and that's cheaper than jail. So, when we say missing baryons, what we really mean is we're getting better at measuring them all the time. That's the optimistic way to look at it. And what you can imagine is that since we're used to finding things that are bright and perhaps nearby as in clumps, looking for stuff that's diffuse and often faint and far away is much, much more difficult. So this is really, just imagine, this pie is slowly getting more filled in as we get our ship together and build better telescopes. So have faith with us. We're working on it. Okay, it's time to play a game. Some drink in should be ready to run around. So can I have like two volunteers? I need runners. Who's going to come up here? The best part about this quiz is that many of these are overlapping answers, but you don't know what the answers are, but you might get them right. So I need people to run prizes. So can I have two volunteers up here?  Yes, representing the standing room seats. Come on back. The titles will be back there. Well done. I know, I'm so proud to have made it. Okay, what's your name? Thank you for joining us, Leana. On a scale of 1 to 1,000, how's your astrophysics knowledge? One. Perfect. Danielle, nice to meet you. 1 to 1,000, astrophysics knowledge? Negative. We're going to explore in our space here. Here's how this is going to work. Some of my students, they know. So, here's how this is going to work. We're going to run around, but wait until they answer the question. So we are going to play a quiz, Dark Matter, or Dark Energy. You ready? You've got to be psyched. Is this Dark Matter or Dark Energy? Shout it out. Otherwise, this is just cheating. Okay, good. Here's the bullet cluster. It is one of our favorite demonstrations of Dark Matter. Well, you can't entirely tell there is here. Can you see the purple? Yeah, good. Okay, stick with me. So this is demonstrating the hot gaps and the concentration of matter from the bullet cluster. The bullet cluster is actually two clusters that are passing through each other. One is that more standard matter, like gas and stars, is kind of electromagnetically sticky. So it kind of sticks together when the clusters crash together. And the Dark Matter is like, see, you sucker. Which you can see demonstrated in this slightly blurry and not very bright video. The book. And then here is the Dark Matter. That's the only appropriate response. Now, this is useful because we're observing Dark Matter, and a tricky thing is that we don't actually know what Dark Matter is made of. I'm telling you, there's so many questions. Okay, Dark Matter or Dark Energy? You have to yell out loud. We're the loud people. All right, so this is a Dark Matter detector from an experiment called Mop Ground. We have various undergrounds. If you don't get suspicious, we're trying to blow stuff up. We're totally not trying to blow stuff up. Probably. So part of those come in, they mix around, they get detected in the detector, they shoot potters out. It's all very exciting. Okay, Dark Matter or Dark Energy? Keep that powder dry. Okay, this is Apple, they're fiber optics. Use an experiment I'll talk about that is for detecting Dark Energy. You have to take a little shouty break and drink some beer. Dark Matter or Dark Energy? I don't know. Dark people in a bar would be good at shouting. All right, those of you who said occasional lensing. This is the actual model of the distribution of the matter. If you have a very keen eye, what you'll see here are these kind of blue, skinny, stretched out galaxies around the edge. That is a distant galaxy that is behind a giant cluster of galaxies.  I hope you notice that. It's kind of the same as if you hold your glass and look at the water, except not at all, and giant, and it's space-time. All right. So, our conclusion from the end of this part of the talk, there are many, many positive particles for Dark Matter. This is a lovely demonstration from Symmetry Magazine that I enjoy a lot. So we know a lot about particle physics, particle astrophysics, but there are still many suggested particles from the process of measuring them and trying to figure out what it could actually be that is Dark Matter, how it's interacting with everything around it, because it is the dominant form of matter in our universe. Okay? You ready? It's probably not an honor, by the way. Okay. Now we're getting to the serious portion. You can tell because there's a lot. There may be as many as two more plots in this talk. So stay strong. I know. I know. You're going to make it. I have faith. So, one of the tricky things about Dark Energy is trying to convince you that we are not total lunatics for telling you that Dark Energy is a thing even though we don't know what it is, nor that we can measure it. Which, you know, might test even the most enthusiastic of you. So, what I'd like to demonstrate is that there are a variety of observations that are extremely different. They might cover different distant ranges. They'll cover all different sorts of astrophysical properties. We are able to pin this that the universe is expanding and accelerating and we're not yet quite sure how to explain that. So, this plot just shows us constraining some cosmological parameters with three different experiments. The cosmic microwave background, there was a question on that. Did you get it right? Type 1, a supernova, that one too, and the baryonacoustic oscillation. No one asked about that because it's already confusing. Okay, that's what you'll notice. What you'll notice here is that those overlap actually in an extremely narrow part of the supply. So, that should be telling you something and it's not just that people are good at drawing. So, first, we're going to talk about the cosmic microwave background.  When you turn on your television in between channels, you get snow. And about 1% of that snow is the cosmic microwave background. It is a less over-graduation from clearly beginning of the Big Bang. So, there you go. Next time someone gives you grief, just be like, I'm an observational astrophysicist in the Big Bang. It seems like I'm just surfing channels but it's an experiment. So, the cosmic microwave background is exciting for a bunch of reasons. One reason is this really fantastic cartoon that hopefully everyone here has experienced. But the plot that spawned it is amazing because see how there's this really nice curve? There's dots on that curve? The error bars on those dots which are observations are so small, they're basically the size of the line which is the theory. So, for one time, our measurement and our theory lined up smack on. Shocker, people want a Nobel Prize. It spawns sassiness, science, it works, bitches. You know, sometimes. So, the cosmic microwave background is seen here so we've set up many satellites to measure and map the cosmic microwave background. Here we have slices from codees which went up in the 90s that really gave us this initial confirmation. WMOP in 2003 and Plunk in 2013. What you can see here is we're getting better sensitivity and more spatial resolution. The fact that the colors are different over here is just because your hands don't follow direction as well. The cosmic microwave background is interesting because it lets us know that the universe, in fact, is expanding. Which, if you think about it for just a second, it's kind of weird. It's weird that the universe is expanding. I think it's weird. It came up with a big bang. They expanded the universe and surprised it. It's also a good place for my favorite observational astronomy story. So there was a brave and intrepid theorist who bought up the cosmic microwave background. He was like, we're going to find it. They start building an experiment. We're going to find it. Down the street there are some good-for-nothing astronomers and they're trying to do an experiment with a radio telescope. And the radio telescope was hosed and it wasn't working, which was a bummer. And they tried everything. They went below the switches and they turned it on and off and they plugged everything in and they looked it and it didn't work. So finally they went out to the telescope. It was a radio telescope so it's a big horn.  And the horn was filled with pigeons. You guys, pigeons are not good for science. I mean really any science as far as I can tell. So, they shot the pigeons. They cleaned up the pigeon food. Then they were just really sad about it. They lay on the floor and then eventually they were at a bar much like this, chatting. And someone said, oh, it's weird that you measure that because the guys down the street thinks that's the beginning of the universe. And then they wanted to oppress. Totally how it happened. So, roughly 30 seconds to be okay with the idea that the universe is expanding. And like maybe you can talk yourself into that. Right? There's an explosion at the beginning. So, unsurprisingly, people were a little mixed on that. And so, they started making other measurements. This is a measurement using supernovae. Take one in supernovae. They're interesting because they're standardizable candles. That means we know when they go off, they're sort of like 100 watt light bulbs. We know how much energy is coming from them. So, if I took some brave soul here and handed you 100 watt light bulb and said, walk away. You could walk away. And then we could take images and figure out how far away you were by how dim that light bulb was. You know, and how mean we were by how long we let you walk. So, you can do this with supernovae. And so, of course, in the 90s, we've been working on it, but it's hard because if you look here, these are great supernovae. People did this. Huzzah! Everything looks fine. These lines that are super faint that you can probably barely see because we're in a tent. These straight lines are predictions to confirm the expansion of the universe. And there were several teams working on it. And what they found was that it wasn't fitting their predictions, which is awkward, especially since the last people won a Nobel Prize for fitting predictions, you know. You don't want to go too far astray. But it turned out, and here you can see, these are galaxies before and after supernovae. But it turned out when these measurements were confirmed that the universe is not just expanding, but is expanding and accelerating. So, I think that, like, you probably need more of this to make sense. The idea that the universe is expanding and accelerating means not only that everything is moving far away from everything else and faster. So, sometimes, people talk about dark energy like vacuum energy. Like, something is pulling from the outside because it's really difficult to understand what might be causing this kind of acceleration. Now, I know, isn't that the cutest? You can imagine a balloon. The author doesn't have to imagine a balloon because they've already got one. We use balloons to invoke this idea of centralist expansion, although balloons have centers and you can draw galaxies on them, but that doesn't really make sense because it's still really weird. What's even weirder when you think about centralist expansion and acceleration is that it's not always the way that the universe has been. So, the universe, not only is misbehaving, but is changing with time. So, we can't make a measurement here and assume that that's what happened in early times. And in fact, looking at this, so this is the big bang, isn't that clearly illustrated? This is time. So, initially, the universe painted like you intuitively might expect. It exploded. There was a bunch of stuff in it and it was going outward, but it was slowing down a little bit because, like, you know, that's how gravity and stuff and things works, right? And then something happened and it started to accelerate and that's where we live now. So, we call this in the matter-dominated time and energy-dominated time. And something that Ethan is going to talk about in the next talk is what happens next? You guys, we can't even find the otters, so who knows. But Ethan is going to try and give us some guidance about what happens next. That's one of the reasons we study cosmology because we want to know what might happen next. This is just one more illustration of the difference between matter and energy-domination. So here you can see the balloon is losing and the anvil is winning and then the balloons are winning. So if you remember nothing else, just remember, the balloons are winning. Okay, we're almost there. Who has played Guess Who? All right, you guys are heroes. Someone has Guess Who in the back. Jesus. Someone write an otter, you have Guess Who. Oh my God, I don't even know. I don't know these people, I promise. So, I use Guess Who as my example for how observation and theory work together. Okay? Sit with me, it's totally a stretch, but also I could fit in an otter so it's worth it. So, we know some things about the universe. Right, we've made some measurements. We can measure supergum, we can measure these things, and we've got all these theories that make predictions. And so what we do is go back and forth and we ask the universe, do you have glasses? Are those glasses red? Do you have long hair? Are you an otter? Things that don't work. We eliminate theories that don't work and we add in new theories that might match our observations. So in fact, when the plump results in 2013 came back, there was an entire field of theory that just stopped being good anymore.  So it's a brave life, being a theorist. And you have to be able to explain things like this. So for example, initially people thought that the cosmic microwave background would be homogenous. It would be flat. But what you can see here is that there's actually huge temperature variations. I mean really not that huge, but pretty big difference to what we're expecting. So if you would like to be a theoretical astrophysicist, please remember to explain this whole spot. Thanks. Last but not least, hence you think that all we do here is honors, I'm going to talk for three minutes about head-decks and experiment I'm involved with to measure dark energy just so you know that we are making a really good effort. Really good effort. Head-decks is the Hobby Everly Telescope Dark Energy Experiment. It's on this 10-meter telescope out in West Texas. There's not a lot, there's a sneak museum out there. They tell us that we should be astronomers to go to exciting places, but I've made some poor life choices. So this survey, well this is the focal plane. This is where the image from the telescope is projected. When it is completely filled every image will take 35,000 spectra from 35,000 fibers. Those of you that have been lucky enough to win the little wooly goodies, can you look through them right now? Do you see the rainbow? That's what a spectrum is, but it doesn't fit on your face. It's fine. So these are each fiber bundles with hundreds of fibers. They sit in the telescope and it's working, much to my excitement after six years. It's taking now about 8,000 spectra instead of 35,000 spectra. It's 35,000 spectra, but it's trying. It's making good effort. Detecting lime and alpha emitters. These are faint, but young galaxies that break just between two to four. And we're using them to try to detect the barrier acoustic oscillations. Now barrier acoustic oscillations are a standard ruler. Remember we have standard gambles. Now we have standard rulers. We like to make it sound like we know what's going on because we have somebody start this and missing that. So the easiest way to think about standard rulers is we're using the clustering of galaxies, a particular kind of galaxy to measure the distortion space-time. So here you can see these galaxies and otters are equally distributed. And if you have a very sharp eye, see how they're...  They would change how far away they are from each other. And I know, well you have to sometimes, you have to make your own thumb lock. And it would show you the shape of the thing underneath. So we use these galaxies to show us the shape of space-time underneath to expose how their energy changes with time. Okay. Also there's a lot of wrenches involved and sometimes things don't fit together. And the students are very, very brave. And many of them have stayed in astronomy against all odds. In the end there were about 150 of these built. And I know it was tiring. And they installed on the telescope. There's all the different pieces coming together. And so what I wanted to point out here is that, you know, I can make lots of jokes about dark energy, but these are huge international collaborations and they only do a small part of the work, right? And so our collaboration was many continents and we're only doing dark energy from wrenches of two to four in a particular way. There are many other experiments on going including E-boss, which is using the Sloan telescope at a packing point and the South Pole Telescope to see where the otter is going to play in the snow. Making different measurements. So what's important here is that we use a lot of different techniques to try to figure out what we're doing to expose what dark energy is. All right, so what is dark energy? Well, it turns out it's going to take more beer and more time before we can answer that question, but I will just leave you with this defensive otter Hopefully the next time I'm back to talk we can discuss how the universe has changed in the following years. We're going to take your questions. Please speak as loudly as you possibly can. So, Sarah, I can hear you from here. And if I can ask Sarah, would you please repeat the question when you hear it so that everyone online can hear the question too? Ooh, there's people. Are there more than 15 people online? More than 15? Wow, exactly. So, some low-online people are brave people. You don't even have to be wearing pants. Yes. So, he asked that video of two things going through each other. What's that? So, that was two clusters of galaxies crashing into each other. And the part of the galaxy clusters that you're used to like galaxies and gas, they slow down and interact. And so, they stay together and the dark matter doesn't interact so much on the outside. Yes. Good. So, with two further question one, does dark matter interact with seeing gravitational light as other matter? And, dark energy can we measure how it's actual rate of acceleration? So, the first, second part, we can't measure the rate of acceleration. It's a thing people like to fight about a lot. Scientists need something to fight about. This is a good one. And, differently, gravitationally. Which is part of the different particle issue. All right. Yes. I see a hand. Go. The question is, can I explain how my experiment works in a slower and dumber fashion? I can't answer it. Well, I have to get in the zone. Space time, yeah, is flat. Dumber. Exactly. We expect it to be flat. If it's distorted, we can measure it. So, we use galaxies to measure how space time is distorted. Because of, because of the initial conditions from the Big Bang and just after. So, here's something to think about, because everyone needs an extra thing to think about. Is the universe smooth or bumpy? Bumpy. Right? It's bumpy. We're not smooth. No, no smoothness here. Bumpy. So, from something that's smooth at the beginning, because something that's bumpy at the end, and the answer is, for some reason, the universe started out bumpy. And so, trying to make those measurements over time is part of what we're doing. Yeah. Yes. Why does it stick together in the clusters before they go through each other's? Yes. That's a good question. If dark matter is hoping you guys have had more to drink, the same, gravitationally, why is as it crashes through each other? The short answer, and I want to leave some excitement for Ethan, will also be talking about these things. Yeah, I'm just drinking, so that should be good.  The answer is, it interacts with itself, because it's selfish that way, probably. And it does, it interacts differently. This is one of the running issues with dark matter, is, for once, the same way that we see dark matter come. Why doesn't it occur in the same places? So this is my theory and I hope this is this fight. Yes. So the question was, since we know so little about dark matter, couldn't it be, shouldn't there be lots of kinds of dark matter, couldn't there be dark matter, people and galaxies and things, or so that makes the second part of that question harder to answer. There could be certainly different kinds, right? We're discovering new things about the universe and, you know, as we improved the way of asking, I feel like dark matter people would be a little complicated. I could take the astronomy quarter and go here. There are some fundamental issues with the way dark matter that we observe it. And I feel like it would make life tricky. But if it was different, anything is possible. So, you know, I'm not going to vote down dark matter people today. I'm not going to be on the internet and vote that down. I don't need that to be my only one. One more question. Yes. Yes. Okay. Explore. If space time is expanding and accelerating, is, is time you can, like, everything going slower while we sit here. And it feels that way. So, what's going on in the time Oh, it's in it. We're everywhere. So, so, the way to think about, I don't know, you can do a good shield for the expansion in time. Yeah. Good. I'm going to pass that question to you because I think that you'll have a better ratio than I have on the fly. I mean, so, for example, it's so exciting, you guys. All right. Well, thank you so much. I will be around later if you have questions.


and the stick, and the left hand is the trouble, but there is also another lever by his or her left hand, but that is... 5,000 pounds of fuel, 35,000 pounds of equipment, and a specialized maintenance and support team of nearly 50 personnel. Ladies and gentlemen, number four, the slot pilot from Chesapeake, Virginia, Lieutenant Commander Jim Cox. Yeah!


NN. The Tiger. Financial News Network. Update. Welcome folks. Here are the down industries trading down 26 Nasdaqs up 45, S&Ps up 5. Gold. Gold contract trading up $5.10 at $19.79. We have silver up 4 cents, $23.67 an ounce. Light sweet crew down 44 cents, $71.71 a barrel, notes and bonds. So 10-year note. Up, down 3 ticks, $113.25, the 30-year flat at $127.29 and $Kingdala. $Kingdala up 125 ticks trading $104.27, the euros at $106, the Yens at $139, the British pounds at $124 to $1 US dollar. We get over and we get a sideways market out here folks. Oh, except, no, yeah. Well, we get a sideways market here. And well actually, let's start with the spy. The spy rejected lower price right now at 4.25, you're at 4.27 and you're going to have lighter volume. So this thing's not done yet. That higher swing point at 4.31 is game. Now let's get over to the IWM, because that's where all the bread's going, not all the bread, but bottom line, they got into the IWM in a monster way. Up to $4.50, it finally took out the bottom of the consolidation, which in fact the NASDAQ took out a long time ago, the S&P just took out bottom line, now you get the small caps going. And if you remember, that's the exact same thing that happened when we were going up in January also, the small caps were the last to perform, and then they actually went to a new high. Let me pull this back again. Yeah, they were the last to perform. They started moving. And what I'm talking about now is that this actually the run I'm talking about is that the run that started in 2020. And then that's when the IWM went from 146 straight up to 234. This is going to be intriguing, the whole thing is. Good old Kingdoll. Let's take a look at Kingdoll. So Kingdoll was up 254 takes earlier. You're up about 120 right now, 127. It's going sideways. We'll see if this thing can get that next leg going. My take is we're going to go to the 126. I'm going 106 in that and notes and bonds. We take a look at notes and bonds. Same setup. What we did yesterday is we rejected lower price in the note and bond market. Sideways move out here today. Stay right there, folks. We're coming right back. We're going to have, I'm going to do the first nine minute, 10 minute update. Then I'm going to have Fred Erneston from Vista. Then we get out, man. It's the Basil Chapman on come right back. Stay right there.