What's good Josh boros back again with another video so apparently see him punk has broke his silence on his Instagram story and After he made this post he ended up deleting it But you know when it comes in the internet internet is very quick to screenshot stuff and it's all over Twitter right now So we're gonna check this out see what see him punk had to say Apparently he he took shots at a Tony con John Moxley Chris Jericho and Dave Meltzer, so I'm really trying to see what's going on here We're gonna go through this story appreciate all loving support man Let me pull it up for you guys and let's check this out He starts it off with a sigh I Wasn't clear to come back to wrestle yet. Then plan was then plan was to wrestle at the pay-per-view I sat and listened to Moxley's Rocky 3 idea. I explained how I've never seen a Rocky movie. That's hilarious And I and thought the idea sucked But if the boss wanted to do it, whatever he said he wouldn't lose to me I'd never experienced someone refusing to lose to me. I just laughed I asked Tony if this was what he wanted He said yes, he's the boss. So I said, okay, but I need to be cleared first They kept saying it could just be a squash. So I didn't need to be cleared I scoffed at that.  The crazy thing about this I always thought thought this was weird on How CM Punk ended up losing to John Moxley when they were gonna try to find out who's the undisputed AEW champion at the time because John was the interim champion and CM Punk was still the champ But he was injured So when they booked this match for dynamite and everyone's hyped about it and then it turned into a glorified squash match for five minutes To this day is one of the craziest weirdest booking decisions. You could have ever done to your top Talent and now we get some context on why this happened Apparently John Moxley didn't want to do the job, which is kind of crazy to me John Moxley didn't want to do the job even though they were gonna have a match at the pay-per-view anyway Honestly, this match shouldn't have even happened to be honest. It should not have happened It was such a convoluted way to get ratings at the time Then apparently he has some issues with Dave Meltzer and Chris Jericho calling them both fliers Calling Jericho a stooge and he basically calling out Tony Tony Connie's like yo Tony Are you cool with this move and Tony was cool with it So it to me is it's just it's very interesting because it's like I Don't see why John wouldn't want to do the job for CM Punk Considering he still ended up losing to see him punk in the end He lost at the pay-per-view so I don't get why well. What was the issue? Not losing to him. I get it I guess it makes sense to not lose to him twice, but they should have even just had to match They really shouldn't have even had to match at all. I think the match At least not on television. I think they could have held out I know they were trying to get a rating pop, but I think they should have held out honestly It's like I don't know it's like see him punk You would think would be in a very good space in AEW because you know he has a little bit more freedom there to be himself and He can kind of move how he needs to move and and he can be creative, but it seems as if It's like that's not the case It seems like there's been always some issues backstage as soon as he got into the company, which is wild to say You know you go from a company where Vince McMahon was meticulous and controlled every little thing So you go to a company where Tony Conn is kind of more chill It seems like more lackadaisical and a lot of times I feel like that can be a problem because you pretty much just let people say and do whatever the hell they want because Why not? So he goes from something strict to something Unrestrictive and now people now I want to do this now. I think this would be right There's no it doesn't seem like there's some middle ground here and seeing punk look like he was trying to play Ball as best he could but he wasn't trying to put his health on the line or whatnot to uh, I guess you can say um I guess Play Kate somebody's ego here. I don't know he wasn't trying to put his health on the line for a company Which I can totally understand and agree with so this is crazy And it part of me, you know, definitely it's not even part of me I believe this is definitely how he felt this wasn't no hacking situation He posted it and deleted it probably was someone said hey, you may want to take that down But he definitely has his grievances and I wouldn't be surprised if seeing punk says some more things because to be honest with you I know There's some people saying oh, you know, maybe he's coming back. I don't know if he is We we don't know the question is will it even really be best for I mean, I think it obviously would be best for awp if he did come back You have another main event player but at the same time Will it be a conducive working situation for everyone in the back when you know He seems like he got a lot of enemies back there. So I don't know This is crazy But he pretty much took shots at Tony put took shots and moxley for not wanting to go You know not wanting to lose to him and Jericho and Dave Meltzer So it's gonna be interesting to see if any of these individuals say anything man But if they do I definitely will make a video about it. So comment down below Let me know does this insight change your perspective on CM punk at all Or are you still among the camp of saying screw CM punk? He's he's the problem He was the problem in AEW. He was the cancer and good riddance Or do you feel like maybe this, you know, kind of clarifies where punk's Fustrations and where he was coming from the entire time But I appreciate all love and support you guys have shown on the channel row to 150k And I'm still here on the speed of youtube wrestling champion the world and in the close world heavyweight champion Appreciate your kicking me. See you next one. Peace


Today's holy, this means recognizing that essentially life is a very exuberant process. But because of human inexperience of how to deal with their cerebral capabilities, the most fundamental two faculties of memory and imagination, people remember everything that they should not and they forget everything that they should and they imagine all kinds of things that they should not and they cannot imagine something that will make their life beautiful. So holy, if those of you who are not aware of this, on this day all over India, people throw colors at each other. They drench themselves in color. This is the day people are head to toe in all kinds of color to symbolize that the essence of life is exuberance and the reason why it becomes so serious is because we remember things that we should not and we imagine things that we should not. Just to stop this preoccupation with your own rubbish endlessly, most people think it's impossible. This is because they have not stopped identifying with their thought. Once you identify with it, you can't stop it. There is no need to stop it. You just have to dis-identify with it because the content of your thought filled in from outside, isn't it? The content of your thought is not yours, borrowed. If you just see this, not mine, that's all you have to do. So holy also means burning up all unnecessary things in our life.  That's enterprise. All kinds of things that they don't need, they pile it up on the street and burn it. This is not about burning old clothes. This is about burning the memories of the past one year so that today you can be like a fresh life exuberant on.


So the two lines of discussions I'm going to paraphrase some of the topics that you guys sent me. The first one has to do with visualization and the questions are all related to each other, but I'm going to summarize them here. What are the community needs for virtual reality or augmented reality techniques for neuroscience research? What is the potential for virtual reality as scientific visualization to enhance neuroscience research knowledge and education or training and what brain network visualization limitations need to be improved upon or what problems need to be solved with respect to visualization and what kind of risks should we be aware of when simplifying network visualization. So these are mostly topics for the speakers from earlier this morning, but I believe that others of you have dealt with visualization through modeling as well. So I'll open the floor. Feel free to comment on these as I start. Hi. So basically virtual reality, sorry, and augmented reality is quite early days in terms of technology. I know it's been around since 70s, but it didn't have time to grow into a mature technology. I think there's huge potentials. We still need some more hardware to come along. The haptic devices that I was talking earlier, I think there will be an important element in there. And I think in terms of simplifying the models basically that we visualize. I think that there's the other side where as the hardware is going to improve. And we can develop new techniques of visualizing where we improve the rendering times by optimizing the way we render rather than tweaking the model itself. I know we're talking about a lot of data here that needs to be visualized, but I think with a lot of work on that side, we can preserve that data integrity and just work on the back end where we're optimizing it in that way. I think this topic actually is relatively big. It probably can hardly be discussed within a few minutes. But I do like to actually kind of use a recent example, this popular Pokemon Go example to highlight the importance to use augmented reality and maybe also virtual reality for our research. So I have a seven-year-old. And this little boy actually kind of like simply gets addicted to Pokemon Go. And he probably can play it all day long until eventually I force him to sleep. So what's actually really interesting out there is that about the game itself, from a developer perspective, because I did write some of the I show actually in my talk. I developed some of the large-scale visualization stuff myself. So from a developer perspective, I kind of feel the novel part is really about the game is that it let you be able to overlay any of these gaming components, including all the reward on top of geographical information. And then you can actually form some sort of community. I think that actually this instead of kind of like use this just for the gaming purpose, I think that this could actually also become a very useful learning tool. For example, in this kind of like a conference environment, if we actually kind of everybody actually kind of start to play that, and we could actually use the same mechanism to have a much better way to illustrate the ideas. And if we overlay some of the presentation, the brain network we care about. And then we could actually form some sort of very more vivid communication with the colleagues, and then to share ideas. And while one guy actually doing the presentation and the other guys actually could actually really get a much better idea, one executive wants this presenter means and maybe receive some feedback or this kind of thing. I think this is another form of the augmented reality, I think it could actually bring onto the table. And along this line, I think that's why actually the big company like Facebook like Google, they actually want to put a lot of kind of like investments. And I think this probably will also be the next kind of generation of the social platform. I think we from the neuroscience perspective, certainly one thing we could think about is that to understand the neuron basis about this thing. So that's obvious. But on the other hand, I feel that it's probably more important to let the community start to develop some of the useful tool for ourselves, so that try to make it much easier to communicate, especially scientific ideas, okay, to exchange the data to share our, you know, resource to make everybody more productive. It's really like the idea of walking around the brain and say, I found an axonic cell that was really rare, you know, 10,000 points. More comments on anything from Pokemon Go. Quite Pokemon Go, but just to probably add to what Esther said. So I just spoke about this tool BluePiOpt that we released to it's basically like a Python environment to model a single neuron physiology. And one thing about BluePiOpt is that it's cloud based. So you can run it on a cloud. So for example, I mean, you raised the point of hardware visualization. So one way to go is probably to run all this on a cloud, you know, so to really leverage the advantage of cloud computing perhaps. I don't know if this looked into so I'm by no means a visualization expert. So just to add to that, basically, I think Microsoft data test, they've done a test with Xbox one where actually they run a very high, very intense rendering over physics simulation from a local machine. And basically, I think it will stand forward when they run it from the cloud. So slowly, the infrastructure is coming into place, but we will be able to run these kind of elements from a cloud. So like a point of all the data sitting there collaborating and being able to be verified at the same time. And just we're just going to have a set of devices that's going to filter through all that data. So maybe if I can ask just one question to the fellow people in the panel, I definitely see the aspect that you were talking about that just by enhancing collaboration, making more fun, basically more exciting, you create creativity and you motivate people. But I was wondering that there's quite a drive to virtualization now. And in some of those cases, I can see the point, especially by, you know, bringing people closer together. In other cases, I'm not so sure. So I wanted to ask you if there was I mean, I can understand that VR is making it more fun or easier, more efficient to interact with the model. But would you say there are actually some aspects where some discoveries were made by having a VR version of the brain, rather than just accessing it the normal way or trying to analyze it the normal way? So that's a great point. Okay, let me summarize a little bit once he was asking exactly, he was asking, okay, in addition to kind of like, you know, some sort of like data sharing purpose, or maybe even entertaining kind of like, you know, aspects for the technology, okay, that would bring into the field. Okay, once this could actually really become an enabler or some sort of game changer, let us be able to kind of like generate better discovery, maybe at a lower cost, or maybe, you know, something like that. Right. So I actually definitely kind of like vote for it. Okay, so, so this become this comment become a little bit more technical. Right. So normally, the reason why actually, just like that, you know, I forgot, you probably just, you know, show in your VR thing is that okay, normally when people actually generate the 3d visualization, and then actually put into a VR environment. So that's pretty much based on that, you know, the generation of certain model, okay, the geometrical model, okay, consist of like a surface object or this kind of thing. Right. So, and so one of the limitation, okay, for that actually pretty big limitation is that in the process when people generate the model, okay, normally there will be information loss. And also, because we only visualize the model that already been generated. So that's basically anything that's related to discovery probably already been discovered, you know, already in the process of the model generation, but not through the further interaction with the model, you know, in the visualization process. So, but on the other hand, okay, so the one thing I didn't highlight in my talk is about the watch your finger is that we could actually visualize, okay, a lot more information without pre-generating such a model, okay. So actually that's a very important thing actually in this kind of like watch your finger enabled, you know, scenario is that we could actually start with the multi-dimensional density data, which is an image data in this particular case is that once you actually let the, you know, use certain kind of like, you know, camera taking the picture in multi-dimensions, okay, and even such multi-dimensional data could also kind of like from other domain, for example, like a brain network or something like that, okay. And this new technology let you be able to navigate, you know, at the unprecedented, you know, speed, okay, and the accuracy, let you be able to do a lot of kind of exploration and for the knowledge discovery and detecting the pattern, which has not been kind of like, you know, modeled, use a surface object or something like that. So I think if in the next generation of this augmented reality engine, sooner or later, it will come out of something like this particular type is that for the real, real world kind of like a picture, which has not been segmented and modeled using a surface object, how you would be able to really directly manipulate the objects, the semantic objects there directly. In addition to watch your finger, I recently I read another kind of article about some lab actually at MIT actually try to also do the similar things, okay, probably in the last year also, okay. They, for example, they just take an absolute picture and there is, for example, a car in a picture. They try to actually kind of like, you know, let people be able to directly move that car around without pre-segmenting it, okay. So that idea intrinsically kind of like similar to what I talk about, you know, in watch your finger case, okay. But we actually, you know, deal with the multi-dimensional, they only deal with the two-dimensional. But basically that's the idea, okay. Certainly that can enable a lot of new discovery because you kind of like directly do many other things. But on top of that, an application that could be kind of like, you know, generated is about this kind of three-dimensional free-form surgery, okay. I want to, I do want to mention a little bit, that potentially also have some relationship to some of the researchers here is that, for example, for the brain surgery, right. So right now, if someone has a brain tumor, okay, they use this kind of like a gamma life or something like that during this brain surgery, right.   So we actually use this watch your finger technology actually to create something called the three-dimensional free-form surgery. We already implement such a system actually for the laser ablation experiment, actually reporting our paper, so that you can actually kind of like use the focused laser beam to cut through anywhere in the neural circuits.  And I kind of like personally believe this could become a very, very powerful thing, okay, in the future to understand the brain function and lead to a lot of discovery. I see Jeff has been holding on the microphone. I just want to answer Klaus's question in a different way. That was a really interesting answer. But thinking of it from, you know, my robotics standpoint and how to discover. So, you know, we did robotics for a number of years because the virtual reality tools and augmented reality tools weren't good enough. We wanted the complexity environment.  So we were doing this to close the loop. Now looking at some of Marius's work and some of the others, I think now you can do that discovery. You can put good models on there, but then you can put them in different environments that they wasn't exposed to and then get discovery that way. So I think it's important that we're getting to the stage where we can close the loop between the brain, the body, and the environment. Can I just one more element to that? Sorry. One of the big differences between rendering in 3D on a flat screen and stereoscopic rendering, basically, is actually the way we see things in 3D on a flat screen is how the light bounces off those objects. So that's how the rendering, that's how we know an object is in front of another object. With stereoscopic vision, we don't have that kind of issue. We will see the object in front of others. So we will be able to select accurately not based on a shading algorithm that we write. It's basically we're going to see actually on position on the world, on that particular axis. So that becomes even more crucial in something like surgery or identifying different parts of a complicated network. And that's why I think both augmented reality and virtual reality will have a big impact once the technology matures enough. Expand the topic here with a second set of questions. And we're close to philosophy, but I think we're still grounded in science. So it's a second set of questions from the more general to the more specific. What could be considered an appropriate level of biological detail in large-scale brain model? How could large-scale detail and simplified models inform us about brain function? What are the most relevant data for understanding brain structure and function that should be systematically collected? The challenge is weeding out spurious correlations in big data analysis. And lastly, how can we make large-scale complex models stable? The brain operates over a wide range of scales, but our models take forever to tune and are often brittle. This is kind of a complementary aspect of the conversation for large-scale as well. A couple of years ago, Chris Elia Smith brought out this paper. I think it was in the Trends in Neurosciences. He called it the use and abuse of large-scale brain models. So there, Chris posed a question. What's the appropriate level of detail a model should capture? Of course, this depends on the kind of questions you want to answer. But on the other hand, if you had a framework to really try and like a scaffold framework that captures much of the structural complexity, then it would be possible to then use this scaffold, this complex framework to simplify as you please. Because then it gives you a reference. Otherwise, I don't know. In a simplified model, like how could you say if you already start off by building something that's very simple, then how do you be able to say that, oh, it's this neuron type that supports gamma oscillations. It's this neuron type that supports beta oscillations or something like that. I guess there has to be some kind of a convergence between these bottom-up detail models and these top-down models in more of a systematic fashion is what I believe. I don't know if you have to say something about this, Jeff. Yeah. The question always comes up at these roundtables. What's the proper level? And I guess the answer is always, it depends on the question you're asking. But I think we're at a stage actually getting, I've been around for a while now. And it used to be everyone said why are you making this model with so many neurons? I can do this in 10. And now it's gone the other way around. Like, why can't you make a larger model? So it's interesting how the feeling has changed. But I think now we're at the stage where we have both of those. So I think you can take large-scale models, hopefully, and extract some principles to get some more top-down models and vice versa. The top-down models can form the bottom and it depends, as I said, on what you're trying to answer, if it's the detail connectivity like Klaus is doing or if it's something that's tied to behavior in real-time like we would be doing. We have real-time constraints that have limits. And I guess while I have the mic, I was the one that brought up the tuning. And at all these stages, I don't care if it's top-down simple or very detail models, but the problems get worse detail models. Once you have something that's brain-like with recurrent structure, you have incredible instability. And it boggles my mind how the brain stays stable over a wide range of environmental constraints, both internal and external. And I don't think we have an answer for that. And I think I'm starting to get a hint from some people that do like Carl Friston and others that are doing more theoretical ideas. But it's still a very open issue that we have to deal with if we're going to go forward, because we're trying to make more and more complex things. And the brain is able to somehow have this complexity and still operate. So it's something to think about. That strikes me thinking about these two topics is that I would expect perhaps the computational modeling and simulations and the visualization to be more integrated with each other, with much more in common. And things such as measures of complexity, graph theoretic analysis, and both top-down and bottom-up approaches to the relationship between structure and function will be natural points of interface between visualization and models. And the fact that we're now running models on GPUs, and those are really driven by visualization from the game industry, et cetera, are we going to get to a point where we can, in real time, create models and visualize them vice versa, affect the models from visualization in an interactive way? And maybe that's too forward thinking. I just want to add some short comments on that. We have been kind of like thinking about how this end-to-end on-board experiment basically starts with imaging in the functional screening experiment. So basically image some area of the brain and then in the same system, we want to run some sort of real-time analysis and identify the regional interest for certain neurons as a compartmental level or something like that. And then run the simulation, and modeling the simulation there, and then predict what would actually happen. And then go back to the experiment, in the same experiment, and then change the type of stimuli, and then do some sort of iterative hypothesis generation, and then verification type of thing. And certainly this will need to use the entire process of data collection, acquisition collection, management visualization, and the simulation modeling, neural reconstruction, and so on so forth. And also quite a bit of the data analysis out there. But such a system, as far as I know, have not been successfully implemented yet. We got this kind of conceptual idea for a while. But even for ourselves, I still feel we haven't been able to really kind of put the modeling and the simulation, that's a critical step actually into the entire loop yet. Why? Because when you actually really kind of dig into the problem you actually find, many of the modelers, the actual model they work on actually is very, very different from the data directly come out of the experiment. Okay. So there are a lot of assumptions about many parameter adjusting, not actually prevent us from directly kind of like, you know, include such model in the experiment. So that's probably the major reason why this haven't been done yet, okay, as far as I know. So I guess the question was that can we put all these things together in the tools? And I think, well, this is a big, this society has been very good about sharing, right? So and there's huge open source communities outside of us. And I think that's what's driving a lot of these things. So a lot of us have talked about GPUs. That's because it was driven by the video game industry. And then you were talking about Pokemon Go and augmented reality and smartphone technology and cloud computing. So my advice is just keep watching what the trends are and leveraging what the community is doing, keep sharing. I think that we're going to get there very soon. I mean, because the tools are in place, we're developing some, but there are a lot of people developing them for a lot of other reasons. So that's what we've been trying to do is keep our eye on the pulse and not try and develop things in-house, but use as much shared stuff as possible. And then I think all of us have been very good about sharing our data and ensuring our models. And I think that's key to getting there, too. Making sure everyone have their say. Esther, do you want to have any closing thoughts? I'm not that much into visualization, but as I'm doing analysis of brain networks, I do face problems with visualization. My networks are quite big. And when I visualize in 2D, what we all tend to do is just apply a threshold and forget about those connections and just try to figure out what we can see with just 2D images. So I think the virtual reality will make a big difference in perhaps doing this sort of analysis. I always think that an ideal world would be to have the network visualization, like the idea of Google Maps, where you can see everything from a very far point where you only see the most relevant thing or the more relevant stuff for you. And then as soon as you get closer, you see these little, little, little connections that sometimes we're just applying threshold. There is a risk that we will lose this information. So I think virtual reality will make a big improvement in this sort of visualization. So probably multi-scale is a good term for both visualization and modeling. You know, Klaus has been on this for many years. I think we're closing almost in time, and I know Matthew has announcements for a slight change in schedule and two more sessions. I would like to thank all the participants of this panel and speakers for this morning.


Hi, I am Ustujit Ghoshal and this is Joint Work with Joseph Jaeger and Stefano Tessaro. Very broadly, this work asks whether we can prove time memory trade-offs for authenticated encryption. In general, concrete security theorems attempt to prove an upper bound on the advantage of an adversary in breaking security in terms of the resources of the adversary. In most prior work, the resources considered are an adversary's time complexity and data complexity are the number of queries. In this work, we additionally considered the adversary's memory as a resource. This is important because the feasibility of a certain attack might be seriously affected by the amount of available memory. Hence, concrete security analysis should take this into account. Even though this question is clearly fundamental, only very recently we have seen some prior work on this, and it has generally taken one of two approaches. The first gives time memory trade-offs for symmetric encryption, while the second focuses on either giving or proving impossibility of memory-tied reductions. We will encounter more precisely what these things are later on in the talk. The former approach has mainly focused on confidentiality of encryption, while the latter has focused on public e-crypto. In this work, we shall combine elements from both lines of work for the first time. We ask whether we can lift the time memory trade-offs for the confidentiality of encryption to the nonce-based authenticated encryption setting. Very briefly, the takeaway message from our work is, it's complicated. We provide both positive and negative results. Since this talk is about nonce-based encryption, let me quickly refresh what I mean by that. A nonce-based encryption scheme consists of three algorithms. The key generation algorithm returns a key. The encryption algorithm takes as input a message and a nonce, along with the key, and returns a ciphertext. The decryption algorithm takes as input a nonce and a ciphertext along with the key, and either returns a message or the bottom symbol indicating failure. A nonce-based encryption scheme guarantees security only if encryption is done under distinct months. There has been a long line of work proving concrete security of nonce-based authenticated encryption. These works, of course, ignored the memory of the bursary. Now, the question here is, can we extend them to take memory into account? Well, the problem has already been very hard just for the case of confidentiality, without the additional property of integrity that we need for authenticated encryption. To illustrate this, here is a toy example of a nonce-based encryption scheme based on our n-bit block cipher. It just encrypts the nonce using the block cipher and exhausts the message. One can prove this theorem, which is a corollary of two previous works, one of which makes connections to communication complexity. It proves an upper bound on the advantage of distinguishing ciphertext from random, which is the standard notion for confidentiality for nonce-based encryption. In particular, the advantage of an adversary can be upper bounded in terms of its memory and number of encryption, and the advantage of breaking the underlying block cipher as a pseudo-random perpetrator. The first term here shows that there is a time memory trade-off for breaking the confidentiality of the scheme. The reason we want to have such trade-offs is that it gives us exactly how many queries a memory-bounded adversary needs to make to break security. For example, for adversaries with memory less than 2 power n by 2, we get beyond both the security here. So we ask whether we can prove similar results for Ae security, in particular for widely used schemes like GCN. We target the notion of Ae that combines confidentiality and integrity. The usual approach is to prove separately a bound on Indar security, that is, indistinguishability from random ciphertext and a bound on c-text security, that is, ciphertext integrity, and then combine them to show Ae security. This can be cast as a concrete security theorem that upper bounds the advantage against Ae security in terms of the advantages against Indar and c-text security. The question, however, is how does this result look like when we take memory into account? Ideally, we want a memory-tight reduction. That is, we want S1 and S2 to be very close to us. Unfortunately, the known reduction is not memory-tight. Let's see why. Let me be a bit more precise. This combined notion of security of authenticated encryption considers a real world where the key is generated and the adversary is given access to two oracles, one issuing encryption of messages under a chosen nonce, and the other one decrypting ciphertext under a chosen nonce. This is compared with an ideal world where the encryption oracle provides random ciphertext and the decryption oracle provides only decryptions if the message was previously encrypted with the same nonce. We need to upper bound the advantage of an adversary running in time t, making Q queries, and using memory S of distribution between these two worlds. The traditional approach first introduces an intermediate world in which the adversary has access to the encryption oracle from the real world and the decryption oracle from the ideal world. It is actually easy to upper bound the advantage in distinguishing the real world from the intermediate world by invoking c-tech security, and this reduction is actually memory tech, meaning that the resources of the c-tech's adversary are almost same as that of the a-a adversary. The real problem occurs in upper bounding the advantage of distinguishing the intermediate world from the ideal world. Here we need to assume inner security for an amount of memory that grows with the number of queries. We shall see why that is the case next. Remember that inner security only gives us the corresponding encryption oracle. If we want to simulate the two worlds that the a-a adversary is trying to distinguish, we would need to simulate the ideal decryption oracle which requires remembering all the ciphertexts. So the memory grows linearly in the number of queries. Well, this was one particular proof. The question is whether we can come up with a clever proof that bypasses this problem. That brings us to our contributions. In a nutshell, our results are centered around the question whether we can make the reduction memory type. First of all, we show that we can indeed make this reduction memory type in the restricted setting of channels. This setting captures usage in protocols like TLS. We introduce memory adaptive reductions, a new technique for giving memory type reduction where the memory of the reduction depends on the memory of the adversary. Secondly, we give an impossibility result for a memory type reduction in the most general setting of non-based authenticated encryption. Next, I shall introduce the channel setting and talk about the memory adaptive reduction. The channel setting is motivated by the typical use of authenticated encryption as a means to establish a secure communication channel as in TLS. In particular, only certain restricted adversarial interactions can offer. For example, nonces are implicit, they are used as a counter, the receiver aborts upon the forced encryption failure. One goal in particular is to ensure in-order delivery of messages. The channel setting that we use captures this exactly. A bit more precisely, a channel consists of three algorithms, state generation, sender and receiver. The state generation algorithm generates initial sender and receiver states. The sender algorithm takes as input the sender state and message and outputs the updated sender state and a ciphertext. The receiver algorithm takes as input the receiver state and ciphertext and returns as output the updated receiver state and either a message or the error symbol border. Correctness of a channel requires that if the receiver is given the ciphertext sent by the sender in order and without modification, that is if the receiver is in sync with the sender, then the receiver will output the same sequence of messages that were sent. In terms of security, we want to look at the setting where the adversary is responsible for delivering messages from the sender to the receiver. Obviously, the adversary should not learn any information on what is being sent. But also, we want to prevent the adversary from delivering messages out of order. For example, if an adversary collects ciphertext C1, C2 and C3 and delivers them out of order, security requires that the channel receiver returns bottom as soon as the first out of order ciphertext to receive. Therefore, in addition to confidentiality, the A security for channels has a strong additional integrity requirement that as soon as messages are delivered out of order, decryption fails. Again, we shall formalize this using our distinguishing game. In this case, the real world runs the state generation algorithm to get initial sender and receiver states. The encryption oracle inputs the queried message along with the current sender state to the sender algorithm. The sender returns an updated state and a ciphertext. The oracle outputs the ciphertext as its answer. The decryption oracle, in this case, inputs the queried ciphertext along with the current receiver state to the receiver algorithm. The receiver returns an updated state and a message. The oracle outputs the message as its answer. In the ideal world, the encryption oracle produces random ciphertext that are only decrypted if delivered in order. Concretely, the encryption oracle returns a random ciphertext and enqueues the message ciphertext pair. The decryption oracle first dequeues a message ciphertext pair. If the dequeued ciphertext matches the queried ciphertext, then it returns the corresponding message. Otherwise, it declares itself out of sync forever and returns bottom. The advantage of an adversary against A security for channels is it's advantage in distinguishing between the real and the ideal words from it. Our main theorem shows that suitably defined notions of confidentiality and ciphertext integrity tailored to channels imply A security in channels in a memory type. In particular, we prove an upper bound on the advantage against A security for channels in terms of advantages against Indar and Ctext security for channels, but this time the reduction is memory type. In the literature, all memory type reductions use a small amount of memory independent of the underlying adversary. Crucial to our result is that we use a reduction whose memory grows linearly in the memory of the adversary and this is enough to prove memory types. Let me give some intuition about how the proof of our theorem proceeds. We follow the same pattern as before. We introduce an intermediate word with the encryption oracle of the real word and the decryption oracle of the ideal word. Again, we can upper bound the distinguishing advantage between the real and the intermediate words using Ctext security. This is easy and I'm not going to talk about this reduction. The core which I shall be talking now about is going to be to look at the other reduction. The technical issue that we face when trying to give a memory type reduction is that the size of the cube grows with the number of encryption queries the adversary makes. For example, if the adversary makes three encryption queries, we need to store three message ciphertext pairs in the queue. The size of the queue shrinks only if the adversary makes a decryption query. The key idea to make the reduction memory type is that bounding the queue size does not change the behavior of the experiment. What do I exactly mean by that? Suppose, for example, we store at most two plaintext ciphertext pairs in the queue. Now, if the adversary makes three encryption queries on messages m1, m2, and m3 and receives ciphertext c1, c2, and c3, only m1, c1, and m2, c2 shall be stored in the queue. Now, if the adversary makes decryption queries on c1, c2, and c3, it would receive m1, m2, and bottom when you really should have received m3 instead of bottom. This seems like a problem. However, in order for the adversary to cause this problem, it has to remember three ciphertexts c1, c2, and c3. Otherwise, it could not have caused this. In general, if the memory of the adversary is not much larger than the length of the queue, the adversary will not be able to cause this problem. In particular, we can show that the adversary has very small probability of causing this problem if we set the length of the queue to delta, which is proportional to the memory of the adversary. We shall show this by reduction to an information theoretic game which captures the essence of what the adversary needs to do to cause this problem. In this game, l and delta are parameters and it's played by a two-state adversary. The first stage of the adversary is given as input, l-bit string r, and it outputs a state sigma of size at most s and an index i. The first i-bits of the string r and the outputs of the first stage are given as inputs to the second stage of the adversary. In order to win the game, it needs to guess the next delta bits of r correctly. Setting delta to depend linearly on s with a fairly straightforward compression argument, we can show that the probability of an adversary winning this game is small. I shall refer you to our paper for more details of the proof and move on to the main application of our result. We have shown that a security of a channel can be reduced to its constituents in r and ctec security in a way that preserves memory complexity. This of course is only meaningful if we have channels for which we can give provable time memory trade-offs for their in-dar and ctec security. We prove such time memory trade-offs for GCM, one of the most widely deployed encryption schemes. CAU, an abstraction of GCM, is an encryption scheme from an n-bit block cipher e and an almost-exhaught universal hash function h. We prove a memory-sensitive upper bound on the advantage of an adversary against a security of the channel induced by CAU. In particular we show that the advantage of an adversary against a security of this channel is upper bounded in terms of the memory and the number of queries of the adversary and the advantage of breaking the underlying block cipher as a pseudo random permutation. The second term here shows that there is a time memory trade-off for breaking the a security of this channel. Since CAU is an abstraction of GCM, we have essentially shown our time memory trade-off for a simplified version of a channel obtained by using GCM in TLS. Now I shall briefly talk about our second contribution. The impossibility result for a memory tight reduction in the general nonce based encryption setting. Our result is in line with prior work proving impossibilities for memory tight reductions. I would like to explicitly note that our result rules out not only memory tight reductions that use memory independent of the underlying adversary but also memory adaptive reductions like the one we gave for channels. These results provide quite a bit of evidence that some restrictions are necessary to prove a security from Indar and CTAX security in a memory tight fit. I shall now state our theorem for the negative result. We show that for all Indar and CTAX secure nonce based encryption schemes NE, we can construct an inefficient adversary A star against a security of NE making Q queries that has advantage close to one. We show that for all efficient black box reductions, if the reduction given access to A star uses memory in the little O of Q, then its advantage against Indar security of NE is negligible. Additionally, for all efficient black box reductions, the reduction given access to A star can achieve only negligible advantage against CTAX security of NE. The A star that we construct is low memory. Its memory grows only logarithmically in the number of queries so this rules out even memory adaptive reductions. There are a few caveats however we can prove this only for a restricted class of reductions. The first restriction is that the reduction is faithful. That is if it answers an encryption query with a ciphertext C, it has made the same query to its own encryption oracle before and had received C as the answer. This restriction is natural and we are not aware of any reductions made in it. The second restriction is that the reduction does not ask for encryptions with the same nonce if the adversary does not do so. This is very natural for nonce based encryption scheme since the security guarantees hold only if nonces are distant. The final restriction is that the reduction either does not rewind the adversary or is fully rewinded. That is it can rewind the adversary at any point but can only restart it from the beginning. This restriction is milder than those in some of the prior impossibility results where only straight line reductions were ruled out. Handling more general rewinding strategies seem to require new proof techniques. I shall briefly talk about the basic idea behind the adversary A star that we construct. It has our challenge rounds. In every round it asks for the encryption of few random messages m1 through mu each l bits long under distinct nonces and it receives ciphertext c1 through cu. It chooses an index j star among one through u at random and asks for the decryption of cj star under the same nonce with which it had asked for the encryption of mj star. If the decryption query returns an answer different from mj star it just abounds. If it did not abort in any of the r rounds it tries to inefficiently break the scheme which helps it distinguish the real world from the ideal world with probability close to one. The main intuition here is that a reduction using k times l bits of memory succeeds in each round with a probability of at most k by u. Of course making this precise requires lots of work. I refer you to our paper for details of the proof to conclude. In this work we proved memory sensitive bounds for a security of channels and subsequently time memory trade-offs for the a security of our tls-like channel. We introduced a new technique of proving memory tightness where the memory of the reduction depends on the memory of the underlying adversary. This technique might be of independent interest. Additionally the we proved the impossibility of a memory tight reduction in the most general setting providing some evidence that restrictions might be necessary for giving a memory tight reduction. There are a few open problems. One open problem here is proving memory sensitive bounds for other practical examples of channels. Another is finding new applications of memory adaptive reductions that we introduce in this work. The full version of our paper is available on eprint. Thank you.


Yeah, thank you for having me. My name is Alex Gore. I'm a PhD candidate at the University of Arizona. And before we get started, I'd like to thank my collaborators, Luke McGuire and Ebergen Francis Rangers for their help on this project. But today I'd like to talk to you about a model that we developed called ProDF. ProDF is a reduced complexity debris flow inundation model. And today I'd like to focus on its applicability in rapid hazard assessment scenarios, particularly following wildfire. Now, before I talk about the model, I first want to refresh our memory regarding debris flows. I know we had some talks regarding them yesterday. But just as a refresher, debris flows are fast moving mixtures of water, boulders, trees, and other debris. They are characterized by having a high sediment concentration, typically greater than 40% in a boulder dominated flowfront. They are found in mountainous areas across the world, but they are particularly common on steep slopes that have recently been burned. This is because wildfires have the capability to alter soil properties, as well as decreased vegetation cover making these slopes particularly susceptible to post wildfire debris flows. Now post wildfire debris flows are becoming more and more prevalent across the Western United States as both wildfire frequency and severity continue to increase. Furthermore, the Western United States is one of the fastest growing reaches in the country, meaning more people are at risk to the hazard associated with post wildfire debris flows than ever before. This really underscores the necessity for having a well established hazard assessment framework in place that can be used to estimate the impacts of these events. Now currently we have established methods that can be used to predict how likely a debris flow to occur is to occur following wildfire or how large that event might be. This information can be combined to create hazard maps like we see here that assign risk factors to watersheds depending on whether it's likely to produce a debris flow and how large it might be. However, these hazard assessment frameworks rarely if ever consider the downstream impacts of debris flows. And this is because up until this point there has not been a well established debris flow inundation model that has been tested extensively on post wildfire data sets. Furthermore, existing inundation models like some that I have listed here aren't necessarily applicable in rapid hazard assessment scenarios. For example, many are processed or physics based and require really detailed information regarding flow properties that might not be easily constrained, especially during a rapid hazard assessment. Furthermore, they're very computationally expensive, which means they might be less applicable in scenarios where hundreds or even thousands of burned watersheds are likely to produce debris flows. We had this issue at the forefront of our mind when we wanted to develop a reduced complexity debris flow inundation model. We really wanted to prioritize having a model that had as few inputs as possible was computationally efficient while maintaining accuracy that is offered by some more complex process based models. And then a model that can be easily implemented into the existing hazard assessment framework. And so with those goals in mind we came up with a model called protea for the progressive debris flow routing inundation model. We developed protea primarily against the inundation scenario from the 2018 Montecito debris flows in Southern California. So this event occurred in January 2018 shortly following the Thomas fire in Southern California. The heavy rainfall in the area produced multiple debris flows that ranged in size from about 10,000 to nearly 300,000 cubic meters in scale. And in the days following the event a team from the USGS gather detailed information regarding the inundation extent of these flows and that's shown here by this hash polygon on this figure. Now these flows, particularly the five that I have outlined here, which we developed the model using a Montecito Oak, San Ysidro point of vista and Romero creeks were particularly devastating for the community of Montecito. They killed 22 people injured 108 more and destroyed over 400 homes. Once again, underscoring the necessity for having a method in place that can be used to delineate the downstream hazard zones associated with post wildfire debris flows. So I mentioned earlier that we wanted to develop a model that had as few inputs as possible. Protea requires for the first is input topography so this is a hillshade of the pre event DM from the Montecito study area. It also requires starting points for the debris flows. So these are pretty similar to initiation points, except that their user defined and the user can place them anywhere upstream of where inundation is expected to occur. They necessarily have to be where the debris flows expected to initiate. It also requires values for debris flow volumes. So for this for this scenario we used observed sediment volume made in the days following the event and the volumes shown here are the volumes for each of the five watersheds. And finally, the model requires to flow mobility parameters. And so they are the flow resistance coefficient and the yield strength and these are parameters that at this point need to be calibrated for each site. And the debris flow volume and the flow mobility parameters are particularly important for the model outputs I'm going to focus a little bit more on those throughout the talk. So the debris flow volume which is represented by M here can be used to calculate the discharge of the debris flow using an empirical relationship first introduced by Rick and men in 1999. Then using relationships that relate discharge to flow velocity and topographic slope we can solve for flow depth as a function of discharge using the second equation here. And then note the presence of Chi which is the flow resistance coefficient one of the two flow mobility parameters is used to calculate flow depth. And then the third equation here is equation for shear stress so it's density times gravitation times topographic slope and the flow depth and so this is a stopping criteria which tells the model when to stop the flow. So like I mentioned earlier there's a yield strength that is prescribed by the user prior to a simulation. And so the flow is only routed when the shear stress is defined by equation three is above that prescribed strength. And so the flow or the flow volume is routed using the progressive flow routing algorithm, which is a variation of the multiple flow direction algorithm. The traditional MFD algorithm routes flow based only on topographic slope and does not consider flow depth which makes it more suitable for low flow conditions. If we don't have floods or in our case the free flows, there may be scenarios where the depth of the flow exceeds the depth of the channel, but the traditional MFD algorithm artificially constraints constraints, the flow to the low flow path flow condition flow path. So we employ an iterative variation of the multiple flow direction algorithm to better capture scenarios where the flow over tops the channel banks or evolves out of the main channel. So this is an example of that here so this is San Yuzichir Creek one of the five watersheds we looked at on the Montecito debris flows. At the start of each simulation the user prescribes a set number of iterations, we found that 100 typically works the best. And so the first iteration the model routes the debris flow volume using the multiple flow direction algorithm and it kind of identifies those low flow paths. And it keeps a fraction of that flow depth so one over the number of iterations in this case one 100th of the flow depth is added to the original topography to create an updated routing surface. So then the second iteration will be routed over the updated routing surface. And as you progress, the main channel will progressively be filled, and then it will eventually be routed over a surface where there is no channel because it has been filled, and you'll start to get some lateral movement which is shown here the warm colors and later iterations. So this phenomenon is shown here in a cross sectional view. Once again the early iterations are shown in the cooler colors and they are mainly confined to the main channel. But as we increase the number of iterations as we progress the simulation we see that the flow progressively fills the main channel and then over tops and begins to move laterally. So once the model is given the inputs and goes to this progress iterative progressive routing algorithm, we get a model output that looks like this. So not only does protea provide information that regarding the extent of an indication, but it also gives us information regarding peak flow depths, which could be particularly important for identifying which downstream areas are the most susceptible to severe impacts from post wildfire debris flows. It's important to note here that protea does not provide information regarding where sediment will be deposited, but rather identifying which areas will experience some amount of flow throughout the event. So we quantified model performance using several metrics for the extent of an indication we use this similarity metric introduced by Heiser and others in 2017. So a mega here is a value that I will call the similarity index, and it has the parameters alpha beta and gamma, which represent model overlap underestimation and overestimation compared to the map extent of an indication respectively. This metric is fixed between negative one, which is the value when the modeled extent of an indication does not match the map extent of an indication at all and positive one which represents a perfect fit between the two. So we use the extent of an indication and the similarity corresponding similarity index to calibrate the model. So this is a calibration for the entire five watershed days that at Montecito for this simulation we kept volume constant. And only varied to flow mobility parameters that I mentioned earlier so the flow resistance coefficient and the yield strength. So with the best fit flow mobility current parameters we have a similarity index of about 0.04 on that scale of negative one to one, or in other words it reproduced about 70% of the observed extent of an indication. Furthermore, this study area was about 40 square kilometers in size, and ProDF was able to run the simulation on a standard laptop computer in about 45 seconds. So once again highlighting the importance of computational efficiency for the purpose of rapid hazard assessment. And so this is promising for that purpose. We also calibrated ProDF to one of the individual watershed San Ysidro Creek in order to compare its output to the output of several other more established inundation models. So Beset Kieran and others in 2019 took a look at a process based inundation model and an empirical inundation model and calibrated them to the observed extent of an indication on San Ysidro Creek. So first they looked at Flow2D and established process based model, and we see that the similarity index for the best fit parameters for that model is about 0.28. And note that this simulation ran on the scale of about our minutes to hours. But when they looked at an established empirical inundation model, the RZ, we see that the runtime dropped significantly down to the scale of seconds. However, that came at the expense of the similarity index, both for non volcanic debris flow parameters as well as our parameters the similarity index was well below zero. To look at ProDF, however, using the same input topography in the same starting point we see that the best fit similarity index is about 0.25. However, note that the runtime is now on the scale of seconds instead of minutes or hours. Once again, very important for the purpose of rapid hazard assessment. We also looked at how the model performed against peak flow depths. So the model outputs information regarding peak flow depth, and the USGS team that gathered information regarding the extent of inundation also made several hundred observations of the peak flow depth of the flows that occurred. 317 of those points overlapped with the extent of inundation from the best fit calibrated parameters for the Monocita data set. So we looked at how the peak flow depths compared at these 307 points. We found that over 80% of these points, the model peak flow depth was within one meter of the observed peak flow depth and more than half it was in within 50 centimeters and nearly one third of the time the model peak flow depth was within 20 centimeters of the observed flow depth. I mentioned earlier that the volume in the flow mobility parameters are particularly important for determining model output. So here are two plots. These dots represent 3000 individual simulations that we conducted against Stania Zidro Creek. Once again, these are scenarios where we varied both the input volume as well as the two flow mobility parameters, the flow resistance coefficient and the yield strength. We see here. And so the color bar is the area. So we chose this metric because this may be something you would be interested in if you're using the model in a predictive sense. We see here that the warmer colors are areas are simulations that inundated more area and the cooler colors are simulations that inundated less area. And we see that the model is sensitivity sensitive to both the volume as well as each of the flow mobility coefficients. So for example, if you hold volume constant and move up the y-axis, only the yield strength of flow resistance coefficient, you can get very different answers regarding how much area is inundated. Same thing if you hold the flow resistance coefficient or yield strength constant and only alter the volume. Once again, you can get dramatically different answers. So this really underscores the necessity for having a well constrained volume that you can put in place and then you can focus mostly on calibrating the two flow mobility parameters. And so this is an example of where ProDF can be easily implemented into existing hazard assessment frameworks. So for example, you can use the existing post wildfire debris flow of volume model as the input for volume, and then you can really focus on the flow mobility parameters. Eventually we would like to have a system where we have the flow mobility parameters constrained. And so we are working on building a more comprehensive inundation data set. So currently we have eight sites at which we have calibrated ProDF. And as we continue to build this data set, we hope to identify which factors are the most important for constraining the flow mobility parameters so that you can use the model in a predictive sense. So we have seven burned areas which are shown here across Arizona and California. I also want to point out that we also have an unburned site in southern New Mexico at the White Sands missile range. So I've mostly talked about post wildfire debris flows today and how ProDF can fit into a hazard assessment framework, but it is important to note that there's nothing fire specific about the model. It can be employed for any debris flow where volume can be estimated. In conclusion, we wanted to create a model that had relatively few inputs and ProDF I believe accomplishes that it requires only an input topography, a starting point, a flow volume, as well as the two flow mobility parameters. Furthermore, it is computationally efficient running on the map in a matter of seconds, but it still maintains the accuracy offered by more complex process based models. It's really easy to implement into existing hazard assessment frameworks by using the output from one of the volume models as the primary input for the inundation model. And then finally, if we want to really get this up and running in a predictive sense we need to find a way to better constrain the flow mobility parameters and determine which factors are really important for constraining these. Before I wrap up I just want to point out that we just recently had a paper published last week that really goes in depth regarding the methodology and the case study at Montecito. So if you're interested in really learning more about the model you can go ahead and look there. But with that, I will take any questions. Thank you. So you said having a well constrained volume was really important. How easy is it to actually constrain the volume. It seems like something will be hard to do. So there is a pretty well established volume model that uses information regarding watershed morphology and rainfall characteristics. However, it was developed and tested almost exclusively in the transverse ranges of Southern California, which is very different than say Arizona. And so we find that the uncertainty associated with that model can be pretty substantial. So I think it really highlights the necessity for better constrain the uncertainty on that model as well as developing volume models that are more widely applicable outside of Southern California. A very interesting model. Your pictures of the damage are mostly about the sedimentation. Is that the next step. So, do you mean identifying which areas experienced deposition. Yeah. Yeah, so some previous studies particularly focused on Montecito have suggested that it's more important to have information regarding the depth, the peak flow depths that certain areas may experience than the extent of deposition itself. And so I don't believe there's a way that this model, I could is just the way that structured would be able to provide information about where specific deposits will be. But feedback that we've had is that peak flow depths may be more important in determining damaged infrastructure than the deposition itself. Not the best person for this. I can't see very well. Thanks so much. That was really interesting. Does the fact that 100 iterations seems to be the magic number tell you anything. So, like I said earlier, the multiple flow direction algorithm only accounts for topographic slope it doesn't count for flow depth. So we employed this iterative variation so we can progressively fill the channels that it overflows. And so the reason we chose 100 is because we found that if you chose, at least in this scenario, a lower number, you're going to be breaking the flow into depths that are still too deep. So if you break it into like let's say four iterations, you're still going to artificially constrain some of that where the flow depth is going to be higher than the depth of the channel. So it's important that you have a sufficient number of iterations where you can progressively fill the channel and then round the top. But we found that if you increase it any more than 100, it really doesn't improve model performance. It kind of, you know, comes to a point where it just increases the runtime of the model, but doesn't really offer any bad added benefits. And so at Montecito and other sites that we've looked at 100 seems to work fairly well for maintaining computational efficiency, but still capturing the lateral movement. I have a question on that volume. I imagine that the volume grows as debris flow moves from the source to the depositional areas. Now you need the volume for the entire debris flow, right, the whole thing. So then are you feeding that volume in one over 100 or one over any increments to the model. So right now it's just a fixed volume the model doesn't consider that deposition may occur or that more volume might be entrained as it's moving right now it's just a fixed volume that's routed down. Thank you.