This video will introduce you to pan-sharpening techniques using ArcGIS. Pan-sharpening is an image processing technique to spatially enhance your imagery. The process is typically carried out using a slightly coarser resolution multispectral image and a higher resolution panchromatic image. The pan-sharpening process creates a new image in which it has the spatial properties, that is the resolution of the panchromatic image, but the spectral properties of the coarser resolution multispectral image. For the demonstration, I'm going to use a Landsat 7 ETM plus dataset. Each band is represented by a separate GeoTIFF file. So my first step is going to be to stack the bands that I want to pan-sharpen together using the composite bands tool. I'm going to add bands 1, 2, 3, 4, 5 and 7 to create a 6 band image composite. And I'm going to save the output as a GeoTIFF file. The output from the composite bands tool is a single raster in GeoTIFF format that contains all 6 bands. Now we're ready to perform the pan-sharpening. We'll go back into Arc Toolbox and launch the create pan-sharpened raster dataset tool. The first thing we need to specify is the input raster. This is the 6 band multispectral composite image we just created. Then we'll move through and confirm that the red, green, blue and infrared bands are set correctly. Next, we'll give a name to our output raster dataset. Once again, we'll save it in GeoTIFF format. Finally, we need to specify the panchromatic dataset. This is the raster that's band 8 from Landsat 7. With the settings correct, we'll accept all the other defaults and click OK to run the pan-sharpening tool. Once the pan-sharpened tool is finished running, we'll have a new 6 band multispectral image. So this raster has the number of bands from the multispectral image but the pixel size from the panchromatic image. Let's do a quick comparison between the original multispectral image and the pan-sharpened image. We see the original multispectral image has a 30 meter resolution and a size of about 325 megabytes. The pan-sharpened has a pixel size of 15 meters and a size of 1.27 gigabytes. Moving over to Arc Map, we can use our swipe tool to evaluate our inputs and outputs. First, we're swiping the multispectral image over the panchromatic image, so these were the two inputs to the panchromatic tool. Now we'll swipe the multispectral image over the panchromatic image. There are both benefits and drawbacks to the panchromatic process. On the benefit side, we have increased spatial resolution, which allows us to more easily identify and resolve narrowly configured features. On the negative side, we've altered the spectral qualities of the image somewhat, so if we were using classification techniques that are heavily reliant on spectral signatures, they could be hampered by this process. There exist a multitude of pan-sharpening algorithms, some of which perform better on certain sensors than others. When you're carrying out the pan-sharpening process, it behooves you to do some research in the peer-reviewed literature and also to play around with the pan-sharpening algorithms and the various settings to determine the optimal output.


Thanks. Good morning, everyone. My name is Xiaowang from Universal Maryland. Today I'm talking about faster secure two-party computation in the single execution setting. It's a joint work with Alex Molotov and Jonathan Katz, my advisor. So let's first give a high-level view of two-party computation implementations. So on the one hand, we have a lot of implementations and systems for semi-honest 2PC, and we have developed tons of applications, including privacy, preserving machine learning, matrix federalization, genomic computation, and all this stuff. On the other hand, when we take a look at the malicious 2PC, we only have AES, Xiaowang and Xia256, and that's all. So why this is the case? So we can summarize it in two points. So on the one hand, malicious 2PC is much slower. It's less efficient. On the other hand, it's very hard to scale to large input. And when we actually, according to our experience, when we directly scale it to large input, it becomes much, much slower. So in this real work, we want to partially address these two points and make it better. So let me first have some kind of a high-level view of the previous paradigms for 2PC. So we have circuit-level card and choose, which is based on a couple circuits where we do card and choose on a circuit level. It starts with India Pincas at 2007. So the most recent line of work is able to achieve a row number of circuits with a row statistical security parameter. And we also have gate-level card and choose, start with TCC09. So this line of work is also known as LEGO, and we also have other protocols for the amortized setting and preprocessing setting. So in this work, we focus on the circuit-level card and choose. And we notice that for our previous work, we either need a large number of public keys proportional to the input, or we need another execution of a 2PC protocol that is outside of this protocol to do the input recovery. So here in our work, we are able to reduce the number of public keys to Kappa, which is essentially what you always need for the base OT. And also, we don't need an additional 2PC protocol. So let me start with a semi-honest 2PC protocol based on a couple circuit. So let's assume that Alice and Bob want to compute a function with only one input from each side and one output. So the first step is to compute a couple circuit with its related garbled keys. So here the solid bars are for one bit and the empty bars are for zero bit. So Alice can first send the garbled circuit, send the labels corresponding to her own input, and then they can do a protocol called oblivious transfer. The color is not showing very nice. There's a box. There's supposed to be a great box around oblivious transfer. And then the protocol allows Bob to learn his own label and without letting Alice to know the value of Y. And then Bob can just evaluate this and get the output. However, this is obviously not maliciously secure for many various reasons. And so one of the paradigms is to use majority-based cut-and-truths. So in this case, instead of just sending one circuit, Alice is going to send about a three-times row number of circuit. And they can still do oblivious transfer to let Bob learn the input labels. And Bob can now do a cut-and-truth and roughly check half of the circuit whether they are correctly generated or not. So if any of them are not correctly generated, then Bob knows that Alice is cheating. And if not, Bob has a high-level idea that Alice is mostly being honest. And then from a high level, Bob can evaluate all the circuit that is not checked. And so Bob is expecting that actually the majority of the output is going to be the correct output. And the mess actually works out that if we send a three-times row number of circuit, everything works out. So more recently, there's a new paradigm called input recovery-based cut-and-truths. So in this case, the difference is that we only need a row number of circuit, which is kind of what the best we can get. And in this case, Bob is still going to evaluate all the circuit and some of them is going to give some other result that is flipped. So it essentially means that Bob is able to get both of the labels for one of the wire. And then they are going to run a protocol, something that Bob can input the evidence that Alice is not behaving correctly. And the protocol will allow this small box, input recovery, will allow Bob to learn Alice's input X. And then Bob can compute the function directly locally. So in this case, we just need to guarantee that at least one of the circuit is good. Therefore, we need like a much less circuit. We just need a row circuit. So there are mostly three common issues in these kind of protocols. One is to how to design an efficient input recovery protocol, I just shown. And also, there are also attacks that we need to address efficiently. So I will first talk about input recovery. So input recovery is this little box. Again, please imagine that there is a great box around. So prior works either require a large number of public key operations and another malicious 2PC protocol to compute this input recovery. So in this paper, we design a new protocol that is based on DDH and it just requires six exponentiation per circuit and then like four of them is fixed based, actually. So from a high level idea, so it's kind of follows the non-interactive 2PC protocol by AMPR, actually. So first, Alice is going to have keys and seed for each of the circuit and he's going to use all the randomness that I derived from the seed and the stuff that is supposed to be learned from the evaluation circuit is encrypted by the key. So they are going to run oblivious transfer such that Bob knows the key for the evaluation circuit and knows the seed for the check circuit. So very different from other previous works, we are going to let Alice run oblivious transfer where Alice is the receiver and Bob sends some random labels M0 and M1. And then after we are done with this once for each of the circuit, Alice is going to kind of mask the input, mask the random labels she received in the previous oblivious transfer and send the encryption of this masked value. So in some sense, this is, in some sense, a commitment to the X because Alice does not know the other label so she cannot really flip the label. And Bob can only get such value for the evaluation circuit but not the check circuit. After that, they can run like the remaining part of the protocol and at the very end, they can run a small protocol that is actually based on DDH such that if the value of delta and omega are the same, then Bob can learn the seed otherwise Bob learns nothing. So here, so if Bob learns the seed, it means that Bob learns seed another key for one of the circuit. And at that point, then Bob can actually strip out, decrypt and strip out the mask to learn the X. So this is just semi-honestly secure and there are more effort that you need to do to make it maliciously secure. However, it turns out that everything can be incorporated into the giant cut and choose efficiently. So more details, see the paper. Well, so the next issue is how to do this selective failure attack. So this is not like a new attack. It's very famous and everybody know this kind of attack. So essentially, the attack is that the gobbler can actually... So when trying to let Bob know the input labels, gobbler at least can kind of corrupt one side of the labels and such that whether Bob can evaluate the circuit depends on his own input. Because let's say in this case if Y is one, Bob learns the solid line which is valid, but if Y is zero, Bob learns like a corrupted red bars like garbage. So mostly the way to prevent selective failure attack is to use so-called probe matrix. So in the following, I'm going to introduce our improved probe matrix. It's kind of a dress like some hidden non-cryptographic overhead that is largely ignored in the previous papers. And for just like 64K bit input it actually has a 1,000 times improvement in terms of the running time. There are also other ways to address this kind of attack but that requires a large number of public keys. So let me first recall probe matrix is a matrix such that if we choose any subset of the rows and XR them, then they are at this row number of ones in the result in the row. So once we have this kind of probe matrix and given we have the input Y, we just need to find a random Y prime such that the matrix vector multiplication holds. So once we have this, this is the old protocol that is not secure against selective failure attack. We have Y, we have E. E is public and Y is known to Bob. So Bob can first select a random Y and feed Y into the oblivious transfer. And now the garbage circuit and instead of computing FXYs it will first recover the real input in the circuit and then do the following computation. So if the row probe matrix translates N number N input into N prime input so previous works we say that okay so we need about N prime number of OTs and we are happy because computing the row probe matrix is free thanks to free XR. However if you actually calculate carefully actually number of XR is quadratic to the input. So I mean XR is free, it's Y instruction but it's still N square and what's worse the size of the row probe matrix is also quadratic. So it means that in order to compute row probe in order to do this computation in the circuit we need to compute N square number of XR and in each step we also need to look up a N square number of size matrix to find out whether we should XR or not. So for example if we have again 64k bit input it's about 4 billion XR operation and the matrix of 4 billion bit assuming that you are packing all the bit very compactly. So this is our next task we want to reduce the size of this row probe matrix make it efficient and still secure. So I mean first idea is let's just chunk off the unnecessary part. So I mean so ordinarily it shouldn't be secure because I mean we lose a lot of randomness. However so notice that if we make sure that each of the small green part is due a row probe matrix then the large one where we just put aside everything together is due a row probe matrix. This is because I mean if we select any number of rows it's either in the same row probe matrix which is fine or they are in different row probe matrix which is even better because it won't have any overlap. So now the problem really just reduced to one single problem how to construct this small probe matrix with a particular size because we can reduce the same row probe matrix on this diagonal. So now we have this small row probe matrix. So inspired by Linda and River 915 we first add a identity matrix which replace the first part of the random matrix to our identity matrix. So this is actually very nice in the analysis because we now know that if we just excel like L number of rows we are guaranteed that there are at least L ones in the matrix because the identity matrix already contribute you L number of ones in the result. So now when we want to calculate the probability that this is a row probe matrix we don't need to consider the case where we select more than row number of rows because that's guaranteed to have a row number of ones. So now we only need to consider the case where they are smaller set where the size of S is smaller than row and very nicely even in this case the identity matrix contribute a lot of ones already so the bound is actually much tighter. So actually after we find all the concrete numbers and stuff so this is our final construction so we kind of chunk the input size into sizes into multiple of 232 bit and for each of them we have a blue up of two in this case. So compared to the prior best work we kind of have 1000 times speed up in terms of running time with like 65 k bit. So notice that actually here so the cost of our row probe matrix is actually much, is slightly higher than the previous probe matrix but that is fine because you can see actually the cost is dominated by computing this giant probe matrix actually row number of times. Okay so the last issue is how to enforce input inconsistency. So actually in the input recovery kind of paradigm there are two input consistency issues. So why is that how to ensure that the input is we are using the same input across different circuit in the cut and choose and also we want to ensure that so when we do the input recovery the input is the same as what we embedded into the cut and choose. So from high level this is the old version so the problem is that this X can all be different and we want to ensure that some consistency among them is true. So the idea of prior work is to kind of construct a zero-knowledge proof that takes this X is input and proof to Bob that they are secure they are consistent in some way. So the intuition is that I want to show that all these lines are consistent. So is it really like necessary actually no. So in this paper the high level so the intuition is that okay so we are not going to enforce this strong consistency instead we just want to enforce that at least one of the good circuit is consistent with the one in the input recovery and we just use whatever that we extract from the input recovery as the input to define that as the input of Alice. And it turns out that it is much better especially when we incorporate this consistency with the input recovery protocol I introduced previously because again this can be done very efficiently in cut and choose altogether with the input recovery. Okay so I mean now we have practical MPC of course we need to implement the protocol so we implement the protocol and it's a part of the EMP toolkit and everybody is welcome to try it. So we try so we run the experiment on a reasonable machine of this Amazon size but I mean however we just do a single call so it doesn't really matter that much and we use ADS and AI. So network is about 2.3 gigabyte and I mean all the experiments in the following assumes 40 statistical security parameter and 128 computational parameter. Okay so first three examples with various size the first one is as the circuit everybody uses ADS it takes about 65 millisecond and for modular explanation of 2000 bit it's a much much much much much much larger circuit with about 4 billion end gate and I mean we take about 5 hours to run in this case most of the cost is for the circuit is for sending the circuit and we also run another circuit that is very very wide and with reasonable depth for this circuit it's so this is a circuit that we can to sort 1000 integers each of 32 bit so it's about 10 million end gate circuit and takes about less than a minute so this is kind of some other experiment we do so in this case we kind of fix the input and output size and end gate and only change one of the input and see how our protocol works along with in terms of each of the component and it is actually linear for the input and output and the circuit size so it takes about 20 microsecond per bit and about 4 microsecond per end gate and so it's like a side story that actually so it is this experiment that we realize that the cost of Ropro matrix is not linear it's quadratic because when we run this experiment we find that the line in terms of the P2's input it's not the line it's a quadratic function okay so compare with the prior work because most of the work just is AES so we compare against AES so in this case we can see a trend here that it's getting better we are hoping that we are getting even better from this point so like concurrent and subsequent to this work we have a ton of improvement to malicious 2PC so we are actually working on one of the extension of the protocol that support MSE checking this is also done together with Semmel and Lucy and we are also able to drop the DDH assumption that we only need OTE here and like they are also like concurrent and more recent results on LEGO which actually improves function dependent cost so for example these papers put most of the computation to the face without the two parties does not know what function they want to compute and however for these kind of protocols they all need a large number of memories because like a large number of memories that is kind of proportional to the circuit size are even worse however for the circuit level we don't really need a large number of memories everything can be pipeline and the memory footprint size is only proportional to the statistical security parameter so for example for a billion size circuit the size of the memory you need to make sure it is constant run is going to be like hundreds of gigabytes unless you want to use disk okay so that's all for my talk and thanks and here is the code you can go and try and yeah we have time for a quick question when you were talking about the rope rope matrix for the selective failure attacks or against the selective failure attacks there was this S I guess there was a set but I'm not sure what S is there S is a so in analysis you want to show that for any subset it's going to be fine so that's the set and then you need to apply a union bound across all set and in this particular case because we don't need to consider set with more than row number of rows so the union bound is exponentially smaller randomly it comes from when we choose Y prime we choose Y prime randomly and have Y equals to E times Y prime E is a public matrix yes just curious you had this modular explanation circuit did you build your own or so in the EM thanks for the chance to give me to advertise the the framework so in the EMP toolkit we also have a on the fly circuit compiler so you can write so it's like we have like integers and the floating point and all the other stuff so I built a small program in that to compute modular explanation and it works out directly so you didn't try to optimize the modular expression just compile it it's like textbook version of modular explanation


From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. The past 24 months have seen cloud spending based dual headwinds of macroeconomics and the ability to dial down resources as needed, i.e. cloud optimization. Nonetheless, the big four hyperscalers clocked in between 170 and 190 billion in IS and AS revenue last year. Really, depending on how you factor in the leaked court documents suggesting that Azure is much smaller than previously believed. Nonetheless, hyperscalar growth continued to outpace most markets, accelerating between 18 and 19% in revenue terms last year, despite their enormous size. Now, as we progress into 2024, IT decision makers are cautiously optimistic about spending levels, especially for the second half. Moreover, all hyperscalers report that cloud optimization is slowing. But pockets of cloud cost-cutting remain and while AI gets all the headlines, its contribution to revenue is still a small fraction of the overall spending pie. Hello and welcome to this week's theCUBE Research Insights powered by ETR. In this Breaking Analysis, we update you on our latest hyperscale cloud spending and market share data. We'll analyze the ETR survey data on cloud optimization, assess the AI lift for the big three US cloud players and look at some of the industry trend data on cloud spend by each of these platforms. Now, before we get into that, let's review the macro spending climate. It's worth doing, even though we've talked about this before the inflation data came in this past week, hotter than expected. And of course, it sent the stock market reeling. But the interest rate sensitive NASDAQ continues to climb out of Tuesday's lows. This chart from our predictions post shows just how tied spending expectations are to the economic data. As you can see back in late 2021 as we exited the isolation economy, IT decision makers were expecting a whopping seven and a half percent spending increase for the coming year. Those annual spend expectations continued to contract as the Fed tightened. And those forecasts bottomed out at 2.9% and came in at 3.4% for the full year 2023. Now, the other good news is IT decision makers, ITDMs we call them, expect 2024 to come in at around 4.3% growth. But the caution is it's very much back loaded with Q1 and Q2 spending in 2024 expected to increase 2.4% and 3.1% respectively. And we think the reality is companies are staying cautious because they don't really know precisely what's going to happen. Uncertain visibility remains the case for most CFOs, hence the back loaded expectations from CIOs. The other key trend worth mentioning upfront here is when you look at the sector spending, it clearly shows that AI is stealing from other sectors. This chart shows the distribution of responses from more than 1700 ITDMs in the latest ETR spending survey, which took place in January. The data on the vertical axis represents net score or spending momentum. And the horizontal axis represents pervasion within a sector, for that sector within the data set. It's a proxy for relative account penetration for each sector. Remember, this is a percent of accounts doing certain things, not a revenue projection. That red line at 40% indicates a highly elevated spending level. Now we're seeing cloud rebounding. Let's start by focusing on the cloud computing in that squiggly line. We've seen a steady deceleration in cloud spending momentum relative to the January 2022 survey, when cloud computing was well above that 40% mark. And that deceleration trend is hard to see, but it reversed in the latest survey, bringing the sector back above the 40% line. And this is consistent with what we saw in the revenue data, which we'll review in a moment. Now AI continues to steal budget from other sectors. That's the other key takeaway from the data. AI has gained notable momentum on both dimensions since the announcement of chat GPT and other survey data that we're not showing here. Indicates that approximately 40% or higher of new AI projects are being funded by taking money from other budgets. So that's why you saw a number of those sectors get depressed in the previous chart while AI shot up. So where are we today and what is the impact of cloud optimization going forward? I want to share some comments from the leaders of the big three U.S. cloud vendors. The first one, we continue to see the diminishing impact of cost optimizations. As these optimizations slow down, we're seeing more companies turning their attention to newer initiatives and re-accelerating existing migrations. That's from Amazon CEO, Andy Jassy. Satya Nadella said that period of massive optimizations only and no new workloads has ended at this point. So what you're seeing is much more of that continuous cycle by customers, both whether it comes to AI or whether it comes to the traditional workloads. And then finally, the cost optimization in many parts of the world are something we have mostly worked through. That's Alphabet CEO, Senghar Pachai. Let's look at what the survey data says about cloud optimization. The data confirms Jassy's comments that cloud optimization has attenuated. The percent of customers indicating that cloud optimization is the primary cost cutting method has reduced dramatically from one year ago, as shown here. However, in the all-important financial services sector, we continue to see customers citing cloud optimization as a primary method. That's 12%, up from 5% in the October survey and 15% of retail customers say it's their primary method. The point is underscored by this anecdotal comment which says the customers got good at dialing down and optimizing cloud. They've developed some sort of muscle memory here and while they're tuning or turning more attention to innovation, optimization is still an important tool in the kit. But again, it is down significantly from a year ago from the mid-teens down to 7% of customers saying that that is their primary method. Now, it still could be the secondary or tertiary method but nonetheless, this data supports the comments of the three CEOs. Now, of course, the other big theme we listen for in Ernie's calls is gen AI revenue. Here are some comments from the big three that are worth reviewing. Quote, if you look at the gen AI revenue we have in absolute numbers, it's pretty big number, no doubt but in the scheme of $100 billion annual revenue run rate, it's still relatively small, much smaller than what it will be in the future where we really believe we're gonna drive tens of billions of dollars of revenue over the next several years. That's Amazon CEO, Andy Jassy. Azure and other cloud service revenue grew 30% and 28% in constant currency. That's the number that we use in our forecast including, this is astounding, including six points of growth from AI services. Says Microsoft CEO, Satya Nadella, we'll come back to that in a moment. And then some other AI is definitely something which is driving interest and early adoption. And as you saw, greater than 70% of gen AI unicorns are using Google Cloud. That's something that Google has trotted out as a key data point. I don't know how many of those unicorns are so-called zombie corns. But, and the other point is who knows what other clouds they're using but it's a proof point I guess that Google wants to put out there. All right, so let's take all that and look at the market data. Here we're showing data for the big four hyperscalers both quarterly and full year. And this data, it comes from a combination of our interpretation of the financials. Remember we're trying to pull an apples to apples comparison. We also look at ETR data and other information that we get from our community. For the quarter, these companies, these four companies, we include Alibaba here along with the big three US. For the quarter, these companies generated nearly $50 billion in revenue. AWS and GCP growth accelerated in the quarter and Azure growth was flat which now I come back to, that's pretty remarkable given the six points of tailwind from AI services. So the saying essentially it would have been 22% without AI or who knows, maybe it's stealing from other budgets. AWS is now just about a hundred billion dollar run rate business, amazing. Now Azure is a different story altogether. If you go by the public statements of Azure growth and the statements that Microsoft made saying that Azure comprised 50% of Microsoft's $110 billion intelligent cloud business last fiscal year, you can get to $71 billion in IaaS and pass revenue in 2023. But if you factor in the poorly redacted court documents which we covered on breaking analysis last year and you try to map to those figures, you can't. Azure on apples to apples with AWS's business by that metric comes in at 52 billion significantly lower. Now our hypothesis is that Microsoft agreed with the courts in that Activision case to define Azure in a more identical manner with respect to AWS's business perhaps including certain security and other line items maybe for instance, certain legacy on-prem Azure stack revenue which may be included in the Azure figures that Microsoft reports. So if you adjust for the court leak, you can't map to Azure's publicly stated growth rates nor can you map to the 50% of the $110 billion claim. So that takes Azure market share when you try to do a true apples to apples comparison from the big four, it takes it from 37% down to 31% and bumps AWS up from 48% to 53%. I know it's nuanced. We continue to dig into this issue and to get to the bottom of the story we actually maintain two models. One is that one that maps to Microsoft's public statements about Azure and the other one that tries to map to the poorly redacted court documents to be continued. Okay, here's a graphic of the growth rates for the big three US hyperscalers. AWS is at the bottom line and the blue GCP is the gray and Azure is the orange. You can kind of see AWS reversed the decelerating growth rate trend and actually grew sequentially from last quarter to 13.2% up from 12.5% in Q3 and again, that's for $100 billion run rate company which is insane when you think about it that they can actually accelerate growth at that size. Azure was flat despite that six point tailwind and GCP grew by our estimate which again, Google doesn't give estimates for GCP. They give their estimates for their overall cloud business and they kind of sometimes give hints. They haven't given hints for the last couple of quarters on GCP and part of that was all the analysts just wanted to talk about search getting disrupted and so they really didn't focus on GCP but by our estimates GCP grew but for its size and maturity it should be growing much faster in our view. If you go back and look at where AWS and Microsoft were at Google's cloud size, they should be growing much, much faster and I guess that's the penalty you pay for being number three in the three horse race. Now our assumptions basically follow the macro trends with growth holding and maybe softening a bit in the first half but picking up in the second half bolstered by an ever accelerating contribution from AI. Now speaking of AI, let's look at the spending patterns in 662 AI accounts from the latest ETR survey data in January, the spending intentions data. This chart shows net score or spending velocity in the vertical axis and overlap the penetration within those 662 AI accounts on the horizontal axis. You can see in the lower right that we insert a table that shows how the data are actually plotted with the ends and the key points are from a momentum standpoint AWS and Azure well ahead of the pack in this important AI sector and the only two firms above that 40% mark, Google and Salesforce are close. Salesforce is actually doing really well with its data cloud and Einstein and Oracle is respectable with a 17% net score. Now we filtered this data on platforms with N at or greater than a hundred so it doesn't show for example at the IBM cloud or the on-prem hybrid crowd. This next chart filters the data on 267 financial services accounts inside the data set. And the key points to stand out are AWS actually takes the lead in net score within this all important sector. Financial services is a mainstay for Amazon. Google also does well as does Microsoft but it's quite notable given the early cloud backlash from the financial services industry look how far above the 40% line all the US hyperscalers sit within financial services and in this cut we've not filtered the end and you could see the other players that hit the chart. And the last vertical we'll dig into is retail which tells a far different picture or a different story especially for AWS with retail rivals like Walmart not using AWS. Both you see both Azure and GCP show higher net scores on the vertical axis than AWS in this picture but AWS still has a big presence in the sector but it's below that 40% mark from a momentum standpoint while the other two US hyperscalers are above that. So look that's a quick run through of all the latest data and so much more that we're discussing with our private clients but if you have specific questions let us know and we'll try to help. I want to wrap with some final thoughts. First of all cloud optimization is less of a factor we think the macro will still drive consumption trends optimization remember it means a feature of cloud it's not a bug and it cuts both ways meaning what goes up and still come down and a lot of firms got really good in the last two years of dialing down cloud and optimizing clouds. Again that's not a bug it's a benefit that won't always accrue to the cloud vendor and that's okay they're doing just fine. Now we're looking for flat ish cloud growth into Q1 with a more favorable outlook in the second half of 2024 driven by AI and stable but sticky macro trends meaning the inflation data really was no surprise to us and we don't expect the Fed to just come in and rescue the market with Zerp anytime soon. They're going to be very, very cautious and so that's going to be in a large part dictate how companies decide to allocate capital. So that means AI ROI is going to have to be a tailwind but we think ROI is going to remain elusive in the first half of 2024. There'll be some gain sharing in the back half but this idea that AI is so easy we think it's illusory. It's like any new tech takes time to adopt and figure out the right use cases and specifically in this case to govern properly with all the legal and privacy concerns. Now the other theme you'll hear this year is so-called private AI. That is the idea that domain specific smaller LLMs are going to be applied and deployed within industries like healthcare, like financial services with proprietary data even on-prem. And as well the other factor here is open source LLMs are becoming more viable and are going to be a bigger factor and an option for customers is privacy concerns and edge deployments factor into the decision making. Open source models like Lama could really commoditize the big language models so the key is how the developers of such models deploy them. Microsoft's success with GitHub co-piles could be repeated with productivity software but early reports are using co-piles for things like PowerPoint and Excel is still clunky and Google has a lot to lose with the 10 blue links on every page and it's got its auction model which optimizes revenue has created the greatest monopoly in the history of monopolies. That's perhaps not as relevant to the cloud but it's an example where AWS and Microsoft they don't have to worry about protecting their golden search goose they got other challenges but that's not one of them. In fact the Amazon Rufus was really intriguing to us that's essentially they've applied generative AI to help shoppers. I'm shopping for some new headphones or some new sunglasses and here's what I'm looking for what are the best ones as interaction with you like a shopper assistant. Well, if that's effective I'm not gonna have to go to Google to research or purchase as much as I used to. So these sort of uncertain vectors right now are coming into the market in the short term and it makes things a little bit more confusing. Longer term the picture is bright and like most of these waves will likely exceed initial expectations even though they're probably overhyped right now but in the near term expect some disappointments along the way. All right that's it for now I wanna thank Alex Meyerson and Ken Schiffman on production and Alex does our podcast as well. Kristen Martin and Cheryl Knight help get the word out on social media and in our newsletters and Rob Hoef is our EIC over at siliconangle.com. Thank you all. Remember all these episodes are available as podcasts wherever you listen just search breaking analysis podcast. I publish each week on thecuberesearch.com and siliconangle.com. You wanna get in touch? Got a question? You got a pitch? Email me at david.balante at siliconangle.com or DM me at dbalante. You can always comment on our LinkedIn posts please do. Make them thoughtful. And please do check out etr.ai for the best survey data in the enterprise tech business. They continue to innovate. Great partnership that we have with them. This is Dave Vellante with theCUBE Insights. theCUBE research insights powered by ETR. Thanks for watching everybody and we'll see you next time on breaking analysis.


I just left the fleet. I was the commanding officer of USS Preble and I could tell right off the bat who were JOD graduates coming to the ship. The ship handling skills that they had, the basics that they had were amazing and we got to jump right into more advanced ship handling. It's amazing to see them the day one when they first show up and then watching their OD competency check or whether they've finished their comprehensive exam at the end of the course and seeing the amazing work that they've done along the way. So we just stood up the Mariners Skills Training Center and that's going to house the basic division officer course and the junior officer of the deck course which is going to turn into the officer of the deck phase one course in the summer of 2021 and then we're also adding the officer of the deck phase two in 2022. So as we bring these new courses online and we improve the training, we get some feedback from the fleet as to what we can do better. I've got extreme confidence in their ability. I think we're providing a great service to the fleet. Your ensigns that are coming out to the fleet are very well prepared. I'd say push them. They have the basic skills that they need and really once they get them on the ship, push them to go even farther.


All right, the first question is from Kyle D. Thomas. I know you're not fans of wrist straps because of losing the potential grip strengthening. What about chalk, liquid, or powder? My hands tend to sweat a lot and the chalk helps with that, but I don't want to hinder my grip strengthening. Yeah, no, chalk isn't going to hinder your grip strengthening. It's going to help you actually be more connected to the weights because the weight isn't slipping, especially if you have really slippery sweaty hands. I remember the first time I used chalk. So obviously growing up in the gym business, all the gyms I worked out in the early days banned chalk. So I actually never had access everywhere and never seen them, right? Never seen chalk, never really used it, worked out in the gym. If my hands would sweat, I'd wear gloves sometimes, but otherwise I didn't use anything at all. And then I went to a hardcore gym and saw that they actually had community chalk on the workout floor. I used chalk on the floor. I loved it. I was like, oh, this is cool. Let me try this. I've never used it before. Used it on my hands and I had a much better workout. Now it wasn't, and I use chalk, by the way, if I'm pressing even. So chalk, a lot of times people say, oh, it's to prevent your grip from slipping so you can hold on to more weight when you deadlift or pull heavy, which is true. But it also gives you better connection to the weight in my opinion, which is why I like to chalk my hands when I do presses or when I do laterals or any movement. It just makes me feel more connected to the way I get the best workouts with it. And I used to have all my clients chalk their hands when I would train them, even my elderly clients. Well, I want to address too that we're not anti-risk straps either. Like I've talked about using them. I think it's just, it's being mindful of it, right?  Like it's like now become a fashion thing where you wear straps or you wear a belt all the time. Like that's what I think all of us would agree that we're anti, right?  Like you should, you should definitely train yourself to be able to squat and deadlift without a belt too. In fact, you should be able to do that 80 to 90% of the time without a belt. It doesn't mean that there isn't a place for you to utilize some of these tools. These tools have value and there's a lot of different examples where you can use them.  And that's the point is to be strong in real life, not just when you go to the gym and it's for safety reasons to protect yourself.  Like, so for me, it's like 80% of the time you're, I'm not using tools like this, but 20% of the time to insert it into my training every once in a while, I don't have a problem with either one of those. Yeah, I didn't get exposed to chocks while I was in college and training, you didn't know what the strength coach is. And I saw a chalk there and I used to use actually wrist straps a lot when I used to power clean because that was, you know, something that I saw everybody do and I thought that was just kind of a part of it in order to, you know, keep a nice tight grip. But it was very surprising to me what chalk provided in terms of security of my grip going, especially barbell training. So if you haven't done chalk with barbell training, it's a completely different feel where you do feel like you have a lot more control of the lift. Yeah, liquid chalk is pretty good too. It's not bad. It's not quite as good as powder, but you're not allowed to use powder chalk. I know and hardly any gyms. Yeah, none of them. So I mean, I work out my garage so I can use whatever I want. But when I go to the gym, I would get liquid chalk and some of them aren't bad. Some of them aren't bad, but it's not quite as good as normal chalk. Here's the other thing with wrist straps that you need to consider. Like a belt, it changes muscle recruitment patterns. It does studies will show that it changes how the patterns of your muscles work. So it not only prevents your grip potentially from getting as strong as it can. It also changes recruitment patterns that you get better at lifting with wrist straps and worse at lifting without them. This is not necessarily, this is not a good thing if you want your strength transfer into the real world.  Well, and I also found after using wrist straps for so long, you know, power cleaning that my wrist got really weak and dependent on just because of the support that it was also providing my wrists with these lifts. And so I had to retrain the way that I was able to catch the bar and get my wrists strong enough to then provide that stability. Yeah, now here's the thing too, your hands have tremendous capacity for strength. I think that there's a bit of a misconception that the hands are the weakest link.   You know, we did evolve from primates. Our hands can be tremendously strong. I've pulled my max dead lifts without wrist straps and it's almost never do my hands get in the way, but it wasn't always like that.  No joke of getting my hand, it took a while to get my hand grip and grip strength up to par to keep up with the rest of my body.


in a big crowd of people because I am at the first extra extra magic hours for Star Wars Galaxy's Edge because Josh and Taylor if you're not sure what the extra extra early magic hours are they're actually for resort guests so they get to come into the park earlier Hollywood Studios is open at 9 a.m. but for resort guests they get to come in at 6 a.m. so they have 6 a.m. to 9 a.m. that is a huge gap and this is the first one for Galaxy Edge so very excited to see how quick we can get the smugglers run how busy it's gonna be compared to opening day opening day was just three four days ago so it's interesting I'm excited and you can see the line they're queuing people up we have about a half an hour before it's actually 6 o'clock and there's nobody behind us so this is not look that bad I think we got here just in time we got here around 5 30 and look at it's going all the way back to the main entrance so wow kudos for us our cast will begin to guide you further into the park for your adventure in a galaxy far far away we're moving forward first order right here Toy Storyland go to the right Star Wars go to the left oh man where to go look stormtroopers are actually leading the way look at how cool that is wow look at that I would say about 45 people in front of us we're gonna head right to smugglers run obviously I mean that's where we're going cantina is open at 6 no reservations need it look at those lucky first ones first ones of the day cast members are all up there waving bright suns bright suns wow look at this awesome the wait time says 120 but I'm gonna guess it's not but it's okay bright suns bright suns thank you actually like I don't think we're gonna have a line at all I think we're gonna walk right up to it I don't think we're gonna queue I think the only point we're gonna queue at is when they hand out the cards yeah look at it it's just a consistent moving queue I love it I love it I love it I love it last time I was actually here for the annual parcel of previews nothing was working and they just had everything covered with tarp so I'm excited this was my favorite part before but now that the AC is on oh yeah we are getting our own Millennium Falcon it is gonna be my first time being a pilot you don't see that often no rush red flight through hey Taylor that's me and you where the pilots I'm nervous to left right left right up down left right up down left right I don't know why the lighting in this room it's just so amazing all right oh boy we just got seen it only one engineer he knew profit your cut minus what you owe me for damaging the Falcon hidden Mickey I gotta say that the pilot is way different Way way different Smugglers run wall. It's a thing. I Am so happy we did good because look at this now the line is going all the way out So we got immediately off smugglers run and realized there is no line for the cantina no 5 No wait, no reservation Look at that oh they're going away I can guarantee you one thing you won't see is this actually you don't see it this like less crowded in here I'm loving it also note that all these people are drinking at 7 a.m. look at this unbelievable way to go is that the mind trick Josh so we got the white wampra air the jet shot the this pin the Jedi mind trick and the fuzzy Wow I got the fuzzy ta-ta and I have to say my lips are going crazy absolutely crazy it literally likes my lips are like bubbling right now I don't know what causes it to actually make that effect but wow so honestly I really just don't like the feeling on my lips the drink tastes pretty good but I don't like what's happening inside my mouth look at how empty it is in here usually you really don't have much room to walk around so that's why I'm appreciating it we walked into the cantina and it was still dark out and now we are coming out and it's full of light and the crowd levels have definitely increased look at this very minimum weight for the blue milk and the green milk that's a good thing to see but I do see that the photo opportunities are very very popular wow a lot of people are getting their chances with those and like I said they are very popular these photo opportunities so we're gonna get in line because it's probably the lowest that you're gonna see this line for at least a couple of weeks or months something actually caught my eye here because I think this is one of those super zoom photos it is so you stand here and they take your picture way up there what did you say about them pretty clever very smart now it's saying it's an 80 minute wait it's 120 when we got here that's gonna do it for me after we got those great photo opportunities done I think I'm gonna call it a day it's still not even nine o'clock in the morning Josh and Taylor thank you so much for inviting me to come hang out you guys are awesome check out their channel Walt Disney World Couple and I hope you guys have a lot of fun I mean honestly these extra magic hours really make it worth it you get in you get what you want done you need get out and you just enjoy your day so love the life you live we'll see you next time bye


How do you really become the best at what you do? I mean, if you look at any domain of life, the people who have always been remembered throughout history have been the masters, the Michelangelo, the Mozart, the sports athletes, the gladiators in the arena, the one in a million warrior on the battlefield. These people who have a rare depth of mastery of a field have always captivated human minds. But how do you actually become potentially world-class at what you do, or just improve on a skill set that's really important to you? Well, that's what we're talking about in today's video on the one, 100, one rule. Hey guys, Alex Hein, author of the book Master of the Day. Now the first link in the description I've included a free download, which is a free goal-setting worksheet to help you plan out getting better at things, mastery, and figuring out the goals you want to achieve this year. So you can check it out right there at the first link in the description. So here's what the one, 101 rule means. For one skill that you choose, you're gonna dedicate 100 days dedicating one hour a day to improving that skill. So let's break it down.  Maybe there are skills like chess, playing the violin, a sport, painting, or learning French. The problem though with building new skills and mastery is that you need to pick one sub aspect of the skill. So let's say for example, your goal is learning to speak, read, and write French. You wanna learn French. But the sub skills there, speak, read, and write, each of those are even different skills, and then listening is still a different skill. So let's say you break down the big skill into the sub skill. I wanna speak fluent spoken French. So now that's very different from saying I wanna be good at French. I wanna be a computer programmer. I wanna be a top athlete. I wanna be a chess master. Each of those domains of mastery has dozens of sub skills. So first you find the sub skill. The second thing is you commit to it for a hundred days. So you're dedicating an entire quarter of the year to one hour a day of deliberate practice in that skill and in that domain. Now what's crazy is that in one hour a day of dedicated mastery work, you can become ridiculous at a skill. The skill of writing, of podcasting, of being good at chess, of salsa dancing, or yes, of speaking fluent French. The dedication of one hour a day is really cumulative. And I found that rather than me, for example, going to salsa once a week for five years, instead doing it five times a week for one year or nine months actually is far better in terms of how good I get and how quickly I learn, but also how long it stays with me. And the third thing is super simple. You dedicate one hour a day to it, right? One sub skill of your skill for a hundred days dedicate one hour. Now, if you think that is far too little time to become good at what you do, yes, I agree.  You know, using this exact principle, not only did I get and maintain a six pack for the last 15 years, I built an entire business in just three hours a day at the start and still maintaining while doing my doctorate. I also managed to get this channel to almost 300,000 subscribers and I wrote multiple books before 30 and that's just kind of the beginning and I'm really not that special. It's by using this framework of one sub skill. For example, building a business, writing a book, building a business, creating content or French, spoken French, listening to French or chess, a certain chess move, et cetera or running and then you can do sprints, long distance, whatever. The sub skills for 100 days, a dedicated one hour a day and you would shock yourself at the goals you could reach or even just the hobbies that could turn into something really, really amazing with that dedication to mastery. So I hope that helps. You can check out the first link in the description which is for a free journaling worksheet that'll help you kind of plot out your most kind of impactful life going forward or reinvent it, kind of get your shit together, whatever you want to call it. But the first link there is for a free download that will help you and for a weekly email course. So check that out and then come check out my last related video on the exact topic right there.


In this video I'm going to talk about a $13 stock which I believe is Incredibly cheap will go through it in this video. It's a stock that I see with a lot of potential massive upside I do see that this stock in three to five years time could reach very much so towards $50 Okay, we're gonna go through this in this video And why I do believe that there is going to be a run up with this stock at least in the short term But certainly in the long term and the growth potential and the opportunity of this stock and what it offers We'll talk about that long term But also the short term of what I believe is going to be a run-up soon Okay, and we'll go through it in this video now what we're gonna be talking about today is PSA So the ticker symbol is PSFE Okay, and you can see here that it's been taking a hammer in at least in the past month at one point It was at nearly $20 nearly $20 at $19 57 It was and that was not too far away That was actually fairly recent all things considering and it's taken a massive hammer in and you know The 52 week loves in terms of that is $9.60 52 week. I $19 57 does have a 2.39 billion dollar market cap So if you go off by the revenue at the moment its valuation is quite rich However, however, this is a growth company with that's involved in a lot of industries that are Consistently growing as well. So we're gonna go into this and this is also not a growth company in the sense that you know They are promising loads of revenue, but they don't have anything to back it up Yeah, this is a company that has got track history of producing revenue growth at good levels So this is always good to see right because you can get companies that try to promise the world in terms of their growth potential And the revenue, but I like to see companies also that are able to back it up Okay, and PSFE are doing that ticker symbol PSFE pace it right They're also releasing which is why I think that it may have a run-up beforehand So they're releasing the first quarter reining's results on May 11th, 2021 okay, so it's a They're releasing these earnings results now what why does this why is this important? Why does this matter? So what do I see happening with this? So I do believe that we will see a bit of a run-up before these earnings I think we're starting to hit to that level I think that this stock is a potential to go down to sort of $10 $11 around that level But I do think that there is potential upside before the earnings I do think they're gonna this company's gonna do really good do really well before the earnings Based on the track history of how they've managed to grow in the long term over the years, right? I believe also the earnings will be strong and it will lead to a long-term future as well And I also think that it will give people a bit of peace of mind knowing that this is one of these growth companies That is actually you know growing its revenue, okay, and we're going to hopefully see that in the results But I do believe that's what we're going to see now remember here. I mean look at this 1996 the revenue was 0.1 billion and you can see that they've got a increase of revenue Okay, over the the the creation of what they do here. So Okay, so they've got Commerce and our gaming they've got all sorts of different products in different geographies the significant Investment and so think about this. There's a lot of things going on with this company where basically I do believe that the revenue Is going to gonna keep growing. Let's have a look at what areas that they're involved in right? So the they're involved in the eye gaming area So stuff like e-sports online casino sports betting and there are fintech digital company They've got digital commerce both in digital goods fintech services traveling entertainment integrated Verticals driving global scale operational leverage unit unity platform global risk services and some of the areas there positions to win in market consolidation Okay, so what it is they're involved in a wide variety of industries We're at wide variety of sectors within this and I do think that that is it's very interesting, right? because I do think that helps with maximizing the revenue and It's in a growth industry in the facts that in the fact that it's a fintech company, right now also the targets as well $19, okay. This is in the next 12 months. So there's two Wall Street analysts Which put this a buy rating a moderate buy rating with in the next 12 months for the potential to reach $19. Well, I actually think that that could reach that in the next 12 months But the next three to five years this could potentially reach $50. So I just wanted to bring this stop to your attention. I thought he's got a lot of potential Okay, remember to smash the like button subscribe to the channel and do consider this one You know, you need to be buying these stocks when they've been beaten down in my opinion and again Remember, I'm not a financial advisor. I'm a business teacher. No, but no, but I know he's an expert on the stock market Okay, but I do think when the companies are beaten down This much you do comes a point where well, this is starting to look attractive Okay, and I think that this is one of them. So thank you for watching a video I hope everybody's doing well and I'll see you really later


So here is the structure of Rubisco. It doesn't look like a Pac-Man. It doesn't look like a Rubik's Cube. It actually is a quite complex. It's quite a large enzyme complex. The reason for that is because it's actually made of 16 different proteins. So we have subunits which are grey here, so you can see the difference. And then we've got green subunits at the top and the bottom. So together these 16 proteins actually constitute 4,800 amino acids to make up this enzyme called Rubisco. Now as I indicated, this is the most abundant enzyme in the world and for us it's also the most important. So the question is, how big is Rubisco then? Well in actual fact, if you lined up 70,000 Rubisco molecules side to side here then they would actually spread one millimetre. So proteins and enzymes are actually very very small, even those which are quite large like Rubisco. Now we also talked about the catalytic sites. Now the thing about Rubisco is it doesn't have just one catalytic site, it actually has 8 for every molecule. And the reactions carried out by Rubisco is... So what happens at each of these 8 catalytic sites is Rubisco initiates the conversion of carbon dioxide into sugars. And as we know sugars comprise carbon-hydrogen oxygen. So the carbon and the oxygen in these sugars comes from the CO2 fixed by Rubisco and the hydrogen actually comes from water.