You collect from farmers that have 500 to 800 cows, and it's very easy to collect that milk because the milk goes from the udder right into a milk chiller, and then the dairy processor collects it twice a day, and it's all automated. In India, there are thousands of farmers that have maybe two, maybe five cows. Those farmers put the milk into stainless steel buckets that they carry on bikes. They walk it to the village center. They aggregate that milk to maybe get 300 to 500 liters of milk in that village per day, but because there are two milkings a day, the dairy processor has to collect that milk in either rickshaws or flatbed trucks and race that milk to a chilling center that's maybe 20 to 30 kilometers away. A lot of this milk comes through the dairy processing plant without being refrigerated at all. The reason for that is they don't have reliable electricity in those villages to install refrigeration systems. The only alternative they have other than collecting the milk twice a day and not refrigerating it is to use a conventional milk chiller with a diesel generator. Our solution, on the other hand, removes this need for this diesel generator backup. The thermal energy battery, that's its main purpose, is to provide backup for those hours when electricity is not available. Usually that power comes when they're sleeping. It never comes when the milk arrives in the morning at 8 o'clock and in the evening at 5 o'clock because that's when the major cities are using electricity. The villages get the leftover trickle-down electricity typically when they're sleeping at night and that's when we're capturing that electricity in the form of cold storage. It's similar to an electrical battery but rather than storing electrical energy, we store thermal energy. And the way we store that is through a material that changes phase, basically freezing and melting material. And then there's another material, a fluid, that rounds around this phase change material and extracts the energy or puts the energy back in the battery. That fluid is what we produce and we send out to a separate device, a chiller, a rapid milk chiller that actually cools the milk. It's a win for the dairy processor because they can collect more quality milk. It's a win for the farmers because they make more money for their milk and it's a win for the consumers because it's healthier milk.


Hi, I'm Vest van Woerden and thank you for joining Eurocrypt. In this video I will be discussing our recent work on advanced lattice sieving on GPUs with dense encores. And this is joint work with Liudica and Mark Stevens. So we'll start with a quick overview of her work. So most of the NIST post-quantum crypto finalists are based on hard lattice problems. And practical crit analysis is important to understand the concrete security and to pick concrete parameters for these schemes. Usually lattice sieving algorithms have the best practicalness for the runtime to solve these hard lattice problems. And our question was how fit are different sieving algorithms for specialized hardware, in our case GPUs. And this includes the more advanced sieving techniques that give large practical gains. So our contributions. We presented the first GPU implementation using all state-of-the-art sieving techniques. This improves both the runtime and energy efficiency by two orders of magnitude compared to a CPU-only approach. And it significantly improves several lattice problem records. So we are able to solve these problems in much higher dimensions. And additionally we present the first optimized implementation of the asymptotic best-known sieve, better known as BGL, both for CPU and GPU. So what's lattice? Well, a lattice is a discrete group that's generated by some bases. And of course, lattice can be generated by multiple bases. But if in any basis, the shortest vector problem asks to find a shortest non-zero vector of the lattice. And in high dimensions, this becomes a hard problem. The later problem is the balanced decoding problem, where you are given a target that lies close to its lattice vector. And the goal is to return this lattice vector. So to give an indication of concrete hardness of the shortest vector problem, we have the TU Darmstadt lattice challenges. So as a challenge, we are given a random d-dimensional lattice. And the goal is to find a vector that's at most 5% longer than the expected minimum length of the shortest vector. And we want to solve this problem using lattice sieving. So what's lattice sieving? Well, the idea is to start with a big list of exponentially many lattice vectors. Then inside of this list, we try to find pairs of vectors that are close to each other, such that their difference would give a short lattice vector. And what we then do is replace a long vector in our list with this new shorter vector and repeat this until we have only a bunch of short vectors left. So we would like to execute these lattice sieving algorithms on specialized hardware. In our case, graphics processing units, better known as GPUs. So on this small part of a GPU, you can already have 64 floating point cores, 64 integer cores, and 8 tensor cores, about which I will talk a bit more later. So in total, on the GPU, you can have thousands of cores, which is much more than the dozen or so you have on the CPU. However, on the CPU, each of these cores can work independently and execute their own instructions. On the GPU, all the cores have to work in batches of at least 32 and execute a single construction of all of these cores at the same time. Also, each of these cores have a very limited amount of registers that you can use and memory that's local. So what are these tensor cores? Well, originally they have been developed for machine learning and they can do one thing very well, that is efficient low precision matrix multiplication. So what do I mean with low precision? Well, I mean 16-bit floating point precision, while normal float would be 32 bits or double would be 64 bits large. However, the 16-bit precision is good enough for our purposes. And with efficient, I mean that on these old models we used, we can have up to 108 16-bit teraflops on a single GPU. And on the newer models, we even can reach more than 316-bit teraflops. So when we compare this to a current best CPU, these would reach with like 64 cores would reach at most about five teraflops. So you can already see the order of two matrix improvements from these GPUs. So let's discuss the pros and cons of these GPUs starting with the cons. First as I already mentioned, these things are not first though. 32 cores have to execute the same instruction, while on a CPU every core can do what it wants. Secondly, these GPUs are an external device. And this means that the main memory has a slow connection to the GPU and the main memory of this GPU. This means that the connection which goes via PCI bus in, for example, our server is limited by only 16 gigabytes per second to send data from the main memory to the GPU. Now you can imagine if you have these GPUs that can get a performance of 100 teraflops per second. Then you have to execute a lot of instructions. You have to reach a lot of flop, thereby transfer to this GPU. This very much, and otherwise you get very much memory bottlenecked. Also inside of the GPU, memory bottlenecks often limit the actual performance. And lastly, it's hard to adapt algorithms for many algorithms that are in principle serial or maybe can be paralyzed on multiple CPU cores. It can still be a whole research study to see how you can adapt them to GPUs, such as our work. But everything, if everything works together, then the pros are clear. You can get incredible performance, hundreds of teraflops instead of just a few teraflops of a CPU. And additionally, these things are very energy efficient. So you're going to get this incredible performance at only the same energy cost or maybe a factor or two more. So we didn't build our implementation from scratch. We extended the so-called general sieve kernel, which is from a warp from 2019. And this is an open-source sieving framework and implementation. And this implementation combines all the advanced sieving techniques, including all implementation tricks that you can think of. And it's fully parallel on CPUs for on a single machine. And they solved the CSP record at dimension 155 in about two weeks. And this was only run on like 72 CPU cores and 256 gigabytes of memory. So if we compare this to the old way of solving the sort of factor problem with enumeration, which is asymptotically inferior methods, but does only use polynomial memory, hence it's trivial to paralyze. So for this method, the old records were at dimension 152, however using more than 800,000 core hours. So it's clear that the sieving methods, both asymptotically but also in practice, significantly improve on the enumeration. So now it's time to discuss these advanced sieving methods and how we adapted them to GPUs. So in general, our sieving process goes as follows. We have our big database of lattice vectors, and then we have three phases that repeat. First we have a bucketing phase where the database is partitioned into multiple buckets. Then we have the reducing phase where inside each bucket, we check all pairs for closed factors to give a shorter factor in return. And then we insert these short factors back into the database and we repeat this. So we start by discussing this bucketing, then the reducing part, and then we'll discuss some additional advanced techniques. So first the bucketing. What we want to do is we want to partition the sphere and thereby partition our database. So suppose we have some bucket center C, then we want to find all the factors in our database that are somewhat in the direction of C. And then this gives us a bucket, and then what we want to do is we want to only check for pairs within each bucket if they are close to each other. And because they are already pointing somewhat in the same direction, this heavily increases the reduction probability per pair. So let's discuss some bucketing techniques. So first as a benchmark, suppose we have no bucketing. Then we have to check between all the pairs. So if we have database of n vectors, then we have to check n squared pairs. And this gives you a time complexity of 2 to the power 0.415 times the dimension. Now the first bucketing method I want to discuss is the so-called BG1 related bucketing method. Here we have random spherical cones that form the buckets. And we have square root of n of these buckets, and each has size square root of n. And if you then do the computation, because of this improved probability to find reductions, the time complexity goes down to 2 to the power 0.349 times the dimension. Additionally, you can replace these random directions with random lattice factors. And then because these centers of the buckets give our lattice factors, we get two additional properties. First, you can immediately also find reductions with this bucket center, because you're already computing the distances. And secondly, this bucket center can be used to not only check for pairs, but also for triples that might give you a sort of factor. And note that to compute, to see in which bucket a factor belongs, we need to compute any products with lattice factors.  So now let's discuss another sieve, which is the BGL sieve. And this one uses structure spherical cones, and the structure comes from a product code. And using this structure, we can much more easily find a appropriate bucket for each factor. And as a result, we can, for some parameter k, we can have much more buckets that are of a smaller size. So in the end, depending on this parameter k, we can get a lot of small buckets, very cheaply. And this results in a time complexity of 2 to the power 0.292 times the dimension. If you take this k small, let's say, logarithmically in the dimension. For our implementation, we have some additional tricks, namely instead of using explicit product codes and explicit factors, we use implicit directions. We take a lattice factor, and then we use permutations and how the mart transforms, and then just read off the coefficients of this to do the bucketing. You can read in the paper more about this. And the nice thing about this method is that it's very suitable for factorized CPU instructions, and also for GPUs. And actually, we have a AVX CPU implementation, and we merge this upstream into the general sieve kernel, the open source implementation. And this is currently the fastest CPU sieve on there, and it beats all the other sieves around starting dimension 80 or 90. And I also want to mention that this PSG1 sieve was the one that was used by the general sieve kernel to set their old records at dimension 155. So we implemented these different bucketing techniques on the GPU, and they have the following practical quality. And what this I mean is that the number of productions we find in each side of the bucket compared to what we would expect optimally in theory. So the black bars here indicate the idealized performance if all of these buckets would be perfectly random. The second, the blue bars show the our PSG1 implementation, and the red and orange ones show the PSG1 implementation for k equal to 1 and k equal to 2. And what we see is that the PSG1 and the BGL for k is equal to 1 are very performant and come close to the idealized performance. And for the case 2 BGL version, we see a slight deterioration in performance, but note that computing these buckets is much and much faster. So let's now discuss the reducing part. So suppose we have a bucket of a lot of factors, and for each pair we want to check if they are somewhat close to each other. Now if we write out this distance, then if we pre-complete all these lengths, so that we actually need to compute this inner product between these two factors. So we need to compute pairwise inner products, and this is exactly what tensor cores are good at. Additionally, we need sparse output and only return the successful pairs. If we don't do this, then we get a clear memory bottleneck. But this means that we can't use any standard methods that have already been implemented. We need to implement our own highly optimized matrix products. Noticially that the number of computations we need to do to compute all these pairwise inner products is squared in the bucket size B, while the data you need to send to the GPU is only linear in the bucket size B. So this ratio between the amount of computation you do and the data you have to transfer improves for larger bucket sizes. So what does this mean for the performance? Well, if we exclude the overhead for transferring this data to the GPU, then we already get good performance at about bucket size of 4,000. So we get about 65 teraflops at this size. And this is very well compared to the theoretical limit of 108 teraflops, because we didn't count any of the instructions here to move the data within the GPU. However, if we include the overhead of sending this data to the GPU, we see that the performance drops significantly at these bucket sizes. And only at the bucket size of about 16,000 or higher, we can get reasonable performance. So in short, for small buckets, sending the data to the GPU is the actual bottleneck. And we need large buckets to reach optimal performance. However, large buckets go against the whole idea of the BGL bucketing scheme. So what does this mean for our implementations? Well, on a CPU, only implementation, we don't have to have these very large buckets. And if we then compare the BG1 sieve, which is indicated by the red line here, and we compare it to our BGL implementation on the CPU with k equal to 3, then the BGL implementation becomes much faster at higher dimensions. And the crossover is already at dimension 90. Actually, in the implementation that we later merge into the general sieve kernel, this crossover already lies at about dimensions lower even. However, for the GPU, we have these minimal bucket size to get optimal performance. And as a result, the BG1 implementation is actually faster than the BGL implementation in all the dimensions that we actually could sieve. And we estimate that the crossover point is maybe only at dimension 150 or 160, the sieving dimension here. So here we see a significant difference between the CPU implementation and the GPU implementation, that we get different trade-offs and a different crossover point for when these asymptotic best sieves actually take over from the more practical but asymptotically worse sieves. So we have discussed the core parts of our implementation, now discuss some of the more advanced parts. Namely, one of the techniques that is very important in practice is the dimensions for free. So instead of sieving in the full lattice, we only sieve in projected sub-letters. That's projected away from the first, say, L basis vectors. So we only sieve in this part. And then after sieving, we have a big list of short factors in this context. And then we lift it back to the full lattice using the bi-lifting. And because you have this many factors, you can show that you can still find the short factor of the original lattice for L that is about the overlocked T. And we can combine this with so-called progressive sieving. Instead of fixing this L a priori, we can start with an L that's pretty large. And so a sieving dimension that's pretty small. And then just slowly step-by-step decrease this L until the lifting actually finds the shortest factor on the full context. And this also means that if we do on-the-fly lifting, namely we just lift any shortest factor that we encounter, then we are much more likely to find this short factor. So this means that we can have an extra problem, namely can we officially detect if any barrel factor within a bucket, we take the difference of these, if this factor might lift to a short factor inside of the full context. And actually what this gives us is a BD problem on this small context between 1 and L. So one way to solve this BD problem is using bi-lifting, as you mentioned before. However, this has a quadratic running time in the lifting dimension. And that's why we introduced the so-called dual hash. So given a lattice, you can define a dual lattice by all factors in the span that have an insert in your product with all the lattices in the primal. So if you pick one of the dual vectors, you can partition the lattice in hyperplanes, where in each hyperplane, you have all the lattice points that have a certain insert in a product with this dual vector. But now if we get a target that lies somewhat close to the lattice, then as a result, this inner product with the dual lattice is also expected to be somewhat small. So our dual hash doesn't consist of just a single dual factor, but multiple dual factors, C1 and Ck. Then the dual hash consists of all inner products between the target and these dual factors. Now if the distance of the target to our lattice is very small, then we can also expect the distance of this dual hash to the insert lattice to be small. Now note, to compute it first, we need the bi-lifting, which is quadratic in the dimension. For the second part, it's clearly linear in this case, if we have already computed the dual hash. But we don't apply this to the field dimension, we only apply this to like a smaller lifting dimension of say 16. And then just using 32 or 48th dual factor seems enough for a very strong correlation. And note that we can officially compute this dual hash of the difference of two targets, because this function is linear. And last but not least, this is very suitable for GPUs. While bi-lifting is very serial in computation, this can be easily paralyzed. So what's the correlation that we get? On the left, we have the value of our dual hash. And on the bottom, we have the distance of our target to the lattice. So if you get a random target, then most of these targets lie very far from the lattice. And only just a few lie close enough that we're interested in them. If we set our filter at as high as this, then we don't have to compute the Bhabias for all of these pairs here. And we only have to compute it for these few pairs right below here. So that saves a lot of computation. So last but not least, we have to discuss memory. Let the sieve algorithms are very memory-intensive. And in our runs, we had to store up to half a billion of vectors. So every byte we can save per factor saves us tens or hundreds of gigabytes in the end. However, the general sieve kernel stores lots of information per factor. For example, the coefficients in the base representation, the so-called Kramp-Schmidt representation, the length of the factors, a unique identifier, this lift target is pre-computed. This dual hash is pre-computed if we use it. But also a pop count that's being used to quickly decide if two vectors are somewhat close to each other. And we want to remove all of this except this x, this length, and the unique identifier. And this means that we reduce the memory by more than 60%. So why can we do this? Well, this is actually an additional benefit from the fact that these bucket sizes have to have a minimum size of, say, 16,000. The overhead of computing all these things on the fly for inside of a single bucket is just linear in the bucket size times, say, the d squared or d to the dimension. However, after this, we have to check for reductions. And that is quadratic in the bucket size. So over these large buckets, the overhead of pre-computing all these things on the fly on the GPU is negligible. So what does this GPU implementation give us in terms of the two Darmstadt lattice changes? Well, we got a new record at dimension 180. So here on the bottom, we show the dimension of these changes. And on the left, we show the wall time, which is just the real lifetime that these experiments run. So the purple points here represent the enumeration that are clearly very slow. Note that these are very slow while still running on the supercomputer. The new records by the general sieve kernel up to dimension 105 are represented here. And they are already significantly faster. And these run on a single machine. And now in our work, we see that we get an order of two magnitude speed up over the old results. While still executing all of this on a single machine with a few GPUs. And even less CPU cores than were available on this for the record for the general sieve kernel. We stopped at dimension 180, which ran in about 51 days. And we didn't stop because we didn't want to run it longer, but we stopped because we reached our maximum RAM size of one and a half terabytes. To go any higher, we need more RAM, or maybe you need to save more memory. So to conclude, let the sieving algorithms can efficiently be implemented on GPUs, including all these more advanced techniques. And the memory bottlenecks disappear when the buckets are large enough. And this has the extra benefit of saving lots of memory with negligible overhead. However, it's important to note that BGL beats BG1 in practice on CPUs, but the crossover for GPUs lies much and much higher. So thank you for watching this video. And here are some of the cetaceans if you want to know more.


Hi guys, welcome back to my channel. Today's video, I'm going to be talking about materialism, and influencers, content creators, YouTubers, whatever you wanna call people on the internet that make content. So if that interests you, then just keep on watching. If you guys are new here, make sure you're subscribed, and also follow me on Instagram. Turn on your post notifications, all that jazz. Thank you to those of you who do have your notifications on. It means the absolute world to me. And let's just get on into my rambles. So I wanna first start off with this concept of halls, try-on halls, unboxings, all of this stuff. Because I will tell you, from a firsthand account with making content online, stuff really helps bring content, like create content, because you have stuff that takes up time. You have the sounds, like let's say ASMR. You have so much stuff to fill time with in your video when it comes to certain things. Clothing, so much has to do with clothing on the internet and shopping at Target and everything like that. I mean, let's just say a good percentage of people's channels is having to do with consumption. And it's really interesting. I will admit that when I have stuff to show or unbox, I'm like, yes, I have content and that's horrible. Like I'm the type of person that likes to save a lot of money so I can like dump a lot of money, you know, on the new iPhone or a Gucci bag. But then again, I'm like, oh my gosh, I can do an unboxing on this Gucci bag. Maybe write it off on my taxes. There's like a fine line when it comes to materialism and content creation. And I just wanted to talk about it with you guys. There's been a lot of talk about people buying things, doing a haul with the tags on it, try on hauls and stuff, and then taking it back to the store, such as like Target to return it and get credit. And I think that's really kind of a shitty thing to do because it's not being honest, but I understand why it's because they need content and stuff to share. Also, if they have affiliate links or anything to go with that item that they can link in the description box, it's really easy for people to do that. And then they make money off of that item, but they've like returned the item. So I find that to be like really interesting and definitely kind of shady on the part of, you know, the person you're watching. But that goes back to the mindset of like, you can't take what you see online at face value because it's only a fraction of reality. Yes, there's some reality, but it's not all reality. If you get what I mean, take like a Lou Lemon Hall. Like that's great and everything, but it's just crazy that people use materialistic things to share with their audience, but it's not really genuine. Don't worry, mine's like genuine, but again, I admit that it's like easy to buy things and like talk about them. It's like really easy content.  I'm like, what am I gonna film today? Like what am I gonna talk about? And I have a lot of thoughts and everything. So it's not too difficult for me, but it is like a cop out. Like when I know something's coming or I know that I have this to talk about so when I don't, it's a little like, eh, it's hard to come up with something, but I always have something to talk about. I also wanted to talk about writing off taxes, writing off material possessions for tax purposes.  I think there should be some new like laws because I mean, I only write off like the really big things that I do dedicated videos for and then I can like prove like, hey, I made a whole video about the Chloe Ting resistance bands. Like I bought those to review, to use. I mean, I use them for personal use, but I mainly bought them for the video. I bought the hula hoop for a video for exercise. So those are, I perceive to be right offable items, but I think a lot of YouTubers take advantage of, you know, showing things in videos so they can write them off on their taxes. Like what about a grocery haul in like a video? That's not the entire video. Well, maybe it is. Are they writing that off on their taxes? Like that's a little ridiculous. The average family like can't do that, but then again, they're not putting their life online and have a business. I don't know. It's just really weird. And I think that there should be like some regulations on like what you can and cannot write off on your taxes when it comes to material possessions. Like what if someone wrote off their car as a business expense, which is legitimate in some instances. So I don't know that it's just really confusing. And I just see that stuff and spending money can benefit YouTubers because of the tax benefits. So that's another thing that I wanted to bring up. I wanted to start this conversation about materialism because I've seen a few other people talk about it and I just wanted to bring it to my community and just like be honest that it's so easy in like a relief when you have like a product to talk about. But I also like doing product reviews. I genuinely do, especially when they're like not sponsored or whatever, because then I can tell you like, I get into the real nitty gritty details because like that's what I would wanna know if I was a consumer buying something. So I don't know. Like I have think materialism is like horrible because people are just buying things for videos. But the other side, it's like, oh, if that product, like if I see that product and like I want that product, like I want an in-depth review, you know, like of a bag of a makeup product, of a hair product. There's just, I don't know. I just wanted to chat with you guys about this because it's been on my mind for a while and I just feel like it's such a big thing online. Whether it's Instagram, TikTok, YouTube, people are just consuming so much and buying so much. And it's like, oh my gosh, people, relax. I also wanted to touch on the fact that YouTubers always say, you know what? If you work so hard, you can get this Lamborghini. If you work so hard, you will be able to achieve like everything that I've achieved. You know what? They're lying to you. Like, okay, just take a firefighter for example. Like they can work so hard at their job but that job in no way is going to pay more than the like set salary. Like I am just saying, maybe it's different for a business but if the business is a shitty idea and like people don't like it or it's not really necessary and it's overly priced and like no matter how hard you work or like spend your days at that job, it doesn't mean you're gonna get any further. You know, hell, like I've been doing YouTube for I don't know how long, since 2015. And it's not like I've amassed a million subscribers. I know I haven't niched down, but that's fine. But like YouTubers are lying to you. If you're a police officer and you are the best police officer there is, you're not gonna be like affording a Lamborghini like these people. So don't listen to them. Like obviously work hard and achieve your dreams or whatever, but just know, oh, if you work so hard at your job or this, that like you can get this, no you can't. I don't like teachers. Okay, you can be the best teacher that there is out there. Students can love you. But does that mean your salary is gonna get raised? No, I feel like some of these YouTubers and content creators, because they can afford this lavish lifestyle, afford a $3 million house, afford so many like luxuries that they're like, well, if I can do it, you can do it. Not really. You struck an algorithm at the right time. You knew the right people. You were consistent with your content. There's a lot of other factors that did take hard work, but that doesn't mean that someone else can achieve that. And that's just like life isn't fair and that's just how it is. Some people have more and some people have less. There will always be someone that has more than you and there's always gonna be someone that has less than you, so whether you work hard or not. So obviously I'm not saying not to work hard and like do your best, but I'm just saying, don't listen to these YouTubers that are giving false narratives of like, if I can do it, you can do it because blah, blah, blah. Like that's just not true. That doesn't mean to give up. Like I still haven't given up somehow, but I genuinely enjoy doing this.  And I mean, it'd be cool if I could do YouTube full time. But again, I'm lucky that I even get two cents from this website and I'm very, very grateful for that. So I think that's enough rambling for one video. I hope you guys enjoyed it. Make sure to give it a thumbs up. Don't forget to subscribe to my channel and I'll see you guys in my next video. Bye.


Robotics, autonomy and the future potential of machine intelligence are areas of common interest between the United States and Japan. A delegation from the Japanese Association of Defense Industries visited the U.S. Army Research Laboratory October 5th at Aberdeen Proving Ground, Maryland for briefings and demonstrations on the future of autonomy. The purpose of the visit is to understand what technology will feed into the third offset strategy. The DOD's vision of the third offset strategy includes advanced projects in areas like robotics, miniaturization and manufacturing that are meant to ensure the United States can maintain military superiority. The Army Research Laboratory has been involved in autonomous systems, artificial intelligence by another name for many, many years. The group listened to briefings on human-robotic interactions and perception in small robots and then gave a briefing on Japanese unmanned systems research. The visit also included technology demonstrations. The laboratory seeks collaborative partnerships with industry and academia across the globe that lead to knowledge building and research breakthroughs as part of its open campus initiative official set for ARL TV. I'm David McNally.


An interpreter is a person who conveys orally the meaning of the spoken word from one language to another. You can use interpreters more effectively and achieve better communication with a non-English speaking client by ensuring both you and your client understand the role of an interpreter. I'd like to make a doctor's appointment, please. Okay, when would you like your appointment to be? I'd like to see the doctor on Tuesday, please. Yes, we can book you in for Tuesday. Would Tuesday morning be okay? Yes, that's fine. Tuesday morning is okay. The interpreter's role is to accurately and appropriately convey the whole message from one language to another. To allow the interpreter to do this, you need to pause often to allow time for the interpreter to interpret and for your client to respond. You should also speak directly to your client. For example, in this scenario, the receptionist asks the client when would you like the appointment to be? Always make your client the centre of your attention, regardless of whether the interpreter is in the room with you. In an on-site interpreting situation, speak directly to your client and maintain eye contact with your client, not the interpreter. You can arrange seating to facilitate communication between you and your client. If you are using an on-site interpreter, a triangular seating arrangement is ideal. When using an interpreter, your role is to conduct and manage the interview. It is your responsibility to ensure a free flow of communication. The first step in any interpreting situation is to introduce yourself to the interpreter and to brief the interpreter on the situation. As part of the briefing, you can describe the type of telephone you are using. You should also allow the interpreter to introduce themselves to the client. Hello, interpreter. Hello. Hi, my name is Mrs Edwards. I'm a high school maths teacher. I'm currently here with Mrs Aiden. Mrs Aiden's son Dennis is in one of my maths classes and Mrs Aiden is here today for a parent-teacher interview. Right, OK. Could you please introduce yourself? Hello, my name is Aiden. I'm a high school maths teacher. I'm a high school maths teacher. I'm a maths teacher. Hello. OK, we're ready to go. Go ahead, please. Wonderful. Great. Thank you for coming, Mrs Aiden. How are you? You can assist the interpreter to accurately recount what you say by using some simple strategies. Keep sentences short, limited to one or two ideas per sentence. Use simple language and avoid jargon and pause often to allow time for interpreting. Be patient with the interpreting process. Sometimes one short sentence in English may require several sentences in the other language. Dennis wants to go to university in study accounting. Are his grades good enough for that? Or should Dennis be doing more homework, you think? OK, well, I'll have to answer that in two parts. Firstly, Dennis is receiving excellent results in all tests and assignments. He's actually one of the best students in his class. And as he is receiving such good results, I don't think it's necessary for him to do more homework. And if he continues to work hard in class, I think he won't have a problem getting into an accounting course. Very good news. Thank you. Fantastic. You should maintain eye contact with your client to show that they are the center of your attention. However, eye contact is a great example of how cultural differences in body language can affect communication. In some cultures, looking someone in the eye indicates honesty and straightforwardness. While in others, it can be seen as challenging and rude. It helps to be aware of these differences when engaging an interpreter. In any interpreting situation, your role is to conduct and manage the interview. Make your client the center of your attention by maintaining eye contact and speaking to them directly. To assist the interpreter, keep sentences short and simple and pause often. By utilizing the tips presented, you can help bridge the communication gap between you and your client.


Have you ever been in a toxic environment? I want you to know that this is not your fault. You cannot change how the people around you act, just as you cannot change the toxic environment. All you can do is build up the courage and strength necessary to leave. Think of your toxic environment like an oil spill in the ocean. Will the fish stay there and get sick? Or will it try to swim away? It may be difficult to see which way is the way out. You may have been in this environment for so long that you start thinking it's comforting because it's all you know. Or maybe you've been manipulated into staying, but you have to keep swimming. After all, you can't heal in the same environment that made you sick. If something out of your control is stopping you from leaving this toxic environment completely, then try to spend as much time as you can outside of it. Go to the gym or a coffee shop, try to focus on your internal environment as well, by self-reflecting, journaling or meditating. Remember, the only way out is through. Just keep swimming.


Our next speaker is our leader. But also, full professor of food science and technology, Jose Angel Rufian. He's going to talk about the Stanford Health Trials. Jose Angel, thank you very much. Me again. Thank you, Franti. Well, here am I again. I'm really sorry about that, but I love being the protagonist of everything on this project. So I repeat, I repeat, I repeat the game. So this is my way of behavior. In this case, what I'm going to talk about is about the trials about personalized nutrition we are currently doing in our project. I'm not going to move deeply on this, but before that, I'm going to introduce you why we mixed the gut microbiota nutrition with the human nutrition, OK? Thanks, Pilar, for not explaining everything in your slides. So I will try to review this. Here you are. You know that us human beings, we were moving in Africa and wherever many millions of years ago, our previous dominates, OK, were moving there, running before the lions and trying to spread, OK, or to live. Well, what has been happening here in this diagram? You are going to see how the human health status improved in the same manner that the human evolution we had through the diet, OK? So what happened? Sometimes many years ago, we suffered the first nutritional transition where the human beings discovered the fire and how they can use the fire against the lions, but also to cook different meats, OK, to cook different foods. And so they allowed them to extract more nutrients for food and their brains became bigger, OK, and they became more intelligent. What happened with that? They developed different technologies thanks to this, and then they had a second nutritional transition. You can see here that they developed agriculture, farming, and these allowed human beings to have enough food the whole year. This allowed them to spread across the world, OK? So the health status was even increasing and increasing and increasing. But what happened two centuries ago, OK, we obtained a third nutritional transition, OK, strong modification of the foods, OK, regarding the use of processed foods and also the modification of the food environment. We are surrounding of that extremely different food environment compared with that we had previously. What happened here? For the first time in our history, in the human history, the health status, the general health status decreased because of there was a strong modification of our nutritional habits, OK? So we tried to modify this. How? We tried to modify this, sorry, modifying using the personalized nutrition and also to empower consumers to adopt these strategies of personalized nutrition, OK? This is what we tried to do. Oh, sorry. I think I moved it. This one, yes, now it's here. So this is why our strategy was try to modify this, OK, through personalized nutrition. This is why the stands for health is today here, OK? Do you have this in mind? Right? So we have here a biologic issue, OK? Human beings, where the evolution took a lot of time, OK? And during that time, the human genome was adapting to these modifications. But what happened? The third nutritional transition took time in a very short time of period. The gut microbiome is able to modify quite fast to the modification of the diet. But the human beings, the human genome is not able to adapt to this, OK? So our gut microbiome has been modified, is modified, adapted to the current diet, OK? Which is not, is completely different to the previous healthy diet we had. And so here we are talking different languages, the human being and the microbiome counterpart, OK? And so we have a fight against these different languages and we need to speak again the same language. This is why we have a biological issue here. Well, so our main question was, will be this modification of the gut microbiota part of the responsible modifications in the health effects? The decrease on the usual health status of the human beings could be? Well, this could be, as Pilar talked previously, you know that we are in a symbiotic mutualism relationship with our gut microbiota, OK? And both the human beings and the microorganisms obtain a benefit, OK? Each other, we provide nutrients to these microorganisms and the gut microorganisms and all the microorganisms we have in our body, OK? Provide us with some health properties. Well, this is what it is supposed to do. So we need to keep changing our mind because of about the relation of me, all of us think of me, the human being, OK? But we need to start to think about us. Me and my microbiome, the whole microbiome. In our case, in our project, the gut microbiome, OK? So just think about nutrition and professor of human nutrition, so I need to talk about nutrition, OK? Me, what do we know about the nutritional rules of the human being? We know a lot, OK? About the amount of proteins, fat, vitamins, whatever. We are discovering about the polyphenols or whatever. We know a lot of things, and we are studying a lot of things. But we know what do we know about us, the nutrition of us, the human olivion, our microbiome? What do we know about this? Nobody knows. This is a clean new research field, OK? We don't know almost anything about the proper nutrition of our gut microbiome, OK? Yes, data refiber or whatever, but data refiber is just a very wide concept, OK? We need to focus more, more precise, yes. So what we need is to propose a paradigm change on nutritional sciences, just moving from anthropocentrism to olivion centrism. That means that the human nutrition should move to think about the nutritional rules of the whole olivion, OK? Not just on the human being, of course. We love cookies, plenty of chocolate or whatever, but maybe for our gut microbiota it's not the best food, OK? So we need to change our mind and to try to unravel what is the best nutritional route and foods for the olivion, right? Well, how can we achieve that? As I told you before, with this scheme of the Stanford Health Project, trying to unravel the different interactions with the foods of the gut microbiome and the human being, whatever, and integrating them in our app and trying to unravel how these functions in the human intervention, which is what I'm going to talk about. Well, don't worry about the data here, OK? It's just to put it that what we are going to do in one of these groups is to perform an intervention with adults, healthy adults, OK? Because if you know that depending on the different pathologies that a human being can have, the gut microbiota can be modified, OK? And so the rules for one pathology could be different to a different pathology or for a healthy human being, OK? So we are focusing on healthy adults. Overweight could be, OK? Or lean adults, but not to talk about this, OK? So in this case, what we are going to do in this sense is try to move to the next slide. If it wants to, yeah. Yes. What we did first was to register the intervention we are going to perform with adults and also with children in the next one. And what we are doing here is to split our participants in two different intervention levels. The first one, first personalization level. Using the app, OK? To follow the rules we have been developing, OK? The human nutritional rules plus the gut microbiota rules, OK? This is the first intervention level. But the second one include the use of this app, but also the use of a more personalized approach using in vitro diagnostics, diagnostics test in order to check how the personalized nutrition is modifying the metabolism of the human being, OK? The use of a wearable and a smart wearable to check the physical activity levels and so on. And also the production of different enriched foods, OK? Plenty of tannins in order to try to modify, to push the gut microbiota to a healthier state, OK? This is what we have been doing. And of course, we have this, oh, sorry. We have done this in two different groups, OK? With healthy adults. So we have the first level intervention, just using the iDiet app, OK? And of course, analyzing the composition of the gut microbiota, because we need to know before we start the intervention what is the composition of the gut microbiota. And depending on that, the app, an oncologist from Technum, calculate what kind of foods and what kind of cooking modifications are better for this specific gut microbiota so that the app gives you the best approach for you as human being and for your gut microbiota, OK? But the second level, the deeper level of personalization also includes the use of the wordable, the analysis of human hearing metabolites, and also the supplements, using, of course, a placebo or the personalized supplements, OK? Capsules given to the adults, yes. This is what we did. And of course, depending on the time of the intervention that lasted 12 weeks, OK, the participants check it and prepare different questionnaires in order to extract information from them. Also extracting, we are analyzing right now, the information from the app, OK, because if you can imagine the intake of the different foods for all the meals for all the days, OK, during 12 weeks. So this is what we are analyzing. And in the case of the group center in the study in the intervention with children, imagine an intervention more or less similar to adults, OK, but with a particularity. They are children. They cannot use directly the app, OK? So we need to adapt and to train their parents in order to use the app and to try to follow it, OK? So the engagement is different in this way. Well, in this case, this is what they are doing in Greece. Anyone here is able to translate this? Costas, you don't. Thanks, Costas. This is why, OK, Costas is here, but I didn't want to use him to translate. It's a concept for. Yeah, yeah, yeah. So this is what they are doing. But I translated some of the information, OK, for you. This is just to let you know that we split it in four different groups of the children, OK? We have normal children. I don't say a link, OK?  Yeah, OK, 100. And now children with different pathologies, you know, children, obese children. Now we have obesity, not overweight in the case of adults, OK, obesity, pathology. Children with gluten diseases, you know, celiac disease, OK, and also children with allergy to cow's milk proteins, OK? These are three different pathologies with a specific gut microleta composition and functionality and trying to move and to modify them. This is what we are doing right now, OK? And you'll see nice results on this because of the adaptation of the use of tannins and the personalized nutrition is different, depending on the kind of diseases we have, OK? So at the end, they are performing the same kind of intervention, OK? Control group, treatment group. But in this case, we are talking about children. And children, you know, are our future. So we are giving the best of us to all the children participating here. So all of them are having the analysis of hearing metabolism, OK? Because of the analysis of these metabolomics tests is able to detect different metabolic modifications they have from the input, OK? So they can be diagnosed size of different kind of metabolic disorders that is not able to analyze it in another kind of techniques. So we use for all the children. And depending on the group, they can be used the placebo, OK, food supplements with tannins, or using personalized foods. This was especially difficult to prepare this because we tried first to enrich these foods, for example, with riboflavin, you know? And what happened there? You know riboflavin. First, it's yellow. Imagine a cocoa bread stick, OK? Brown with the yellow. So you have a pink one. Bread, pink, it's not the same. But also the taste is horrible, OK? So but we unraveled that the production of riboflavin was enough for these children. So remove riboflavin, OK, for the food composition. And now try to add tannins. What is the best mixture of the different tannins we have been fighting on this, OK? But at the end, this can be done. And this is what we are testing. So this is just a regular overview of what we have been doing. Now, if Francis wants to stand up and to start a discussion or follow fast, OK? We are open to the general discussion or a question or whatever, OK? Thank you very much. No, you too.


Just about everyone involved in nutrition or fitness is Obsessed to some degree with superfoods or supplements. We put these things on a pedestal and Envision it as something that will make us like super healthy feel amazing yet reality is When you eat these foods, do you feel amazing? And no, I don't mean the sugar rush from your berry smoothie in the morning Cod liver out of any food lives up to this I'm sure everyone has a story of their grandmother getting a tablespoon of cod liver Shove down her throat when she was growing up as a kid and there's good reason We used to use cod liver to prevent rickets specifically cod liver oil and rickets is a vitamin D3 deficiency But cod liver doesn't only contain vitamin D. It literally contains every vitamin our body needs Giving cod liver oil to children during developmental stages of life Ensures that they're getting enough vitamin a vitamin D3 vitamin K2. They're getting plenty of omega-3 fatty acids They're getting iodine Nutrients that are essential for growth and skeletal development. What cod liver oil is missing is Really a B vitamin content But if you have canned cod liver as we do today, there is no food on this planet That is more nutrient dense than fish liver Beef liver is a super nutrient dense food But it does not come close to the omega-3 fatty acid content of fish liver And we have things like fish eggs that are great sources of omega-3 fatty acids But liver has an incredibly high vitamin a content as well If you could only choose one food to use as a nutritional supplement Cod liver would get you just about everything you need and depending on whether you use the oil or canned cod liver We'll dictate the protein amount therefore the B vitamin amount The real reason I talk about cod liver so much is because I think it's delicious And yes, you guys have seen me put some nasty stuff in my mouth and no I'm not talking about those dirty girls. We have in New York City I'm talking about eating raw brains raw liver, you know So whenever I taste something and say it's good, I'm inclined to believe that you guys don't trust me But as an I've worked as a catering chef in the past I know when a food genuinely tastes good and is enjoyable and out of every single liver product I've had cod liver tends to taste the best and I mean fish liver in general is super tasty And the reason for this is what we spoke about a Couple videos ago when a food has a certain nutrient profile each nutrient in the food dictates how it tastes So if a food has a complete nutritional profile, the vitamin a is giving it a certain flavor If you omega-3 fatty acids are giving it a certain flavor I'm sure you guys have tasted grass-fed beef that might have tasted fishy before that's because of a specific Nutrient in the food and when you have a food that literally has the highest nutrient content in all aspects possible It makes sense that it's naturally delicious here. I have two cod liver products This is Rugenfisk a German brand that is Norwegian cod liver and here we have another brand that I see around a lot This is from Icelandic cod liver. This is the one. I'm currently offering on Frankie's free-range meat I will speak about that later, but we're going to taste these side-by-side and I'm going to give you my honest opinion Usually these products are fairly similar But there is a difference in salt content and sometimes they do season the fish the amount of calories in these cans is insane This is 800 calories of cod liver. What's amazing to me is Just by having one or two of these cans every single week you get all the nutrients your body needs essentially So if you spend anywhere from 200 to $300 on You know, maybe 50 to 100 cans of cod liver. That's all you need for the year Then you could just have you know, whatever else you want. That's what I like so much about this I used to buy these from a Russian grocer in Brooklyn and then when they stopped carrying these I couldn't get them anymore But now Frankie's got him Cod liver is almost always packed in its own oil. I've actually never seen it packed in vegetable oil or anything So the Ruben Fisk on the right has you know a lighter color looks like it wasn't heated as much The oil is a bit lighter and this on the left You know a little bit of discoloration. I'm not really sure what that's from All right, so I'm gonna taste this one first and a nice big pink piece looks good This is crazy This is like it doesn't make any sense when you taste it. The texture is like jello and The flavor is just indescribable. It's so fatty. It's rich. It doesn't really have much of a fishy flavor It doesn't even taste like fish. I can't describe this in The past I always used to say that cod liver was my favorite food And I stopped saying that I guess because I haven't had it in a while, but this is like It's insane.  I'm trying to tell what that new flavor is from The second cod liver had a similar taste of course because it's cod liver But it was more complex like I could taste more of the ocean almost like more iodine more depth of flavor Both of them incredibly delicious same almost jello like consistency Amazing texture if you guys haven't tried cod liver whether you decide to buy it from me or not I really encourage you to try it We're selling the Rugen Fisk on Frankie's free range meat calm International as well. So regardless of where you guys are Go to Frankie's free range meat calm look under the add-on sections or the internationally available section and order yourselves some cod liver We will be exploring some other brands in the future Trying to find the best one and our end goal is to make some our self You know, I believe that cod liver is a really delicious the healthiest food there is and If we're able to get a fresh product in handle it with care and provide it to everyone At a better quality that hasn't been done before that would be awesome And just to briefly go over the nutrient profile of cod liver again Retinol the animal form of vitamin A is only present in high amounts in liver. So we're checking that box cod liver as all wild fatty fish livers has a decent vitamin D3 content Of course, you have to obtain vitamin D3 from the Sun, but It's arguably the food that has the highest vitamin D3 content as well Not only that it contains iodine it contains high amounts of omega-3 fatty acids It contains vitamin K2 as all liver contains vitamin K2. It has minerals. It has elements It has high amounts of B vitamins It's absurd how many vitamins this food has not only that the vitamins that it has are very hard to obtain in most cases A lot of people have a hard time getting their DHA and getting their omega-3's and a lot of people can't get their vitamin A in they're having an issue with iodine This really is the ultimate solution. It's why I love this food so much So if you guys would like to support my future vision of being able to provide Fresh cod liver as well as our homemade canned cod liver check out Frankie's free range meat calm You don't necessarily have to buy the cod liver but Check out our mission. We're looking to do a bunch of other things in the future as well such as Getting you guys raw dairy wild game meats Really just any high quality nutrient-dense animal food that people could want at an affordable price Ladies and gentlemen, thank you guys for joining me today If you can please like the video subscribe hit that bell icon and share it if you can I did a bunch of other videos in the past on cod liver. So you guys can feel free to check those out as well I've talked about, you know oxidized fish oil in the past. I've touched on just about everything So I think I did two other videos So if you guys do want to learn more you can check those out or better yet Leave a comment down below Frankie boy reads every single one Again, thank you guys for joining me and enjoy the rest of your weekend I mean, I'm not the kind of guy to make any threats So I'm inclined to believe that you know cod liver tastes pretty good So if I was to force feed someone cod liver because they did not purchase it from me I don't think that would be a crime I think cod liver is such a delicious food that if you were to force it down someone's throat then Hey, we'll leave that up to the judge


Hi everyone, Joe for jasperjasecasebrakes.com doing a quick little randomizer for the Charter's Bangles, the random number block randomizer for those two teams for Pick Your Team number 25. Those are the instructions on the number block, how it works. These folks know how it works. Thank you for getting in. There are the numbers right there. Let's do it. Let's roll it, randomize it. 11 times, 6 and 5 for names and numbers. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 the final time. After 11, Matthew down to Preston. 6 and 5, 11 times for the numbers. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 and 11 the final time. After 11, we got 1 down to 2. Alright, so we have Matthew with 1, 9, 1 and 9. William with 8, Matthew with 0. There's no redemptions in Flawless, but there are blockchain redemptions. Those will go to you for those two teams. Matthew, PJ with 5, Matthew with 7, Paul with 4, Matthew with 6, Preston with 3 and 2. Let's sort by column B and in a separate video will be that actual Flawless break itself. We'll see you for that one a little bit later. Bye bye.