Last year, it seemed Simon Cowell was in the mood for cutting down his real estate portfolio. Only a month after selling his main mansion in Beverly Hills for $25 million, he ditched another 90210 estate in an off-market deal. While he let go of these homes, Simon still owns a sprawling $24 million mansion in Malibu, as well as luxury properties in New York City and the UK. In this video, we'll tour a few of Mr. Cowell's homes, and with his impressive real estate, you don't want to miss this. In these videos, we don't reveal any addresses, and even though I've done a house tour of my own place, please do not show up at any private residences because it's not safe for anyone. We all got to know him as the brutally honest and entertaining judge of American idol, Simon Cowell, but he is so much more than that. He's a TV personality who hails from England, an entrepreneur, entertainment manager, and record exec. Simon has found a major success, creating series like The X Factor and Got Talent franchises, which he's also judged on. This includes both The X Factor UK and the US, as well as America's Got Talent from 2016 to the present. After Cowell was a record producer and talent scout in the 80s and 90s, he came to public eye in 2001 as a judge on Pop Idol, a show that he successfully pitched alongside this creator, Simon Fuller. Since then, considering how well he's done, he's received awards for all of his major contributions to the entertainment industry. Simon often makes blunt and controversial comments as a TV judge. Even insults, but it's always made him memorable. He's also produced successful albums and singles for musicians he's taken under his wing, from Leona Lewis to One Direction to CNCO, you name it. At the time of this recording, Simon has a massive net worth of about $600 million. So, while he's gotten rid of a few of his LA mansions, he has plenty of luxe properties to fall back on as you might have guessed. Hey guys, it's Cara the Vampire Slayer and today we're bringing you another house to our here and famous entertainment. We noticed about 95% of you aren't subscribed and were the original Celebrity House tour pros, so hit that subscribe button and ring that bell, we post a new video daily. Today we're looking at where the one and only Simon Cowell calls home, and since he owns multiple properties, we'll be looking at about three of them from Malibu to the UK. Don't forget to follow me on Instagram to chat, and now let's get into this video. In summer of 2020, it was reported that Simon has sold one of his Beverly Hills mega mansions for $25 million. The lavish property was located on the best block of a posh street in the Beverly Hills Flats neighborhood on a nearly one acre piece of land. Simon bought the home way back in 2004 for $8 million and spent five years and millions of dollars transforming the property into a high tech palace packed with custom amenities like a million dollar security system and a full wellness center. It was just off of sunset Boulevard and considering Simon's affinity for a luxury lifestyle, you would expect his home to offer only the best and it does. The traditional meets modern mansion boasted a black and white interior design with a ton of marble and glass. Cowell was involved in every aspect of designing his LA home right down to the doorknobs. Inside a 9,335 square foot estate offers six beds, six baths, and everything you could imagine. There's a sleek all-white entryway with a winding staircase and Florida ceiling glass, and amenities like a high tech home theater, professional tanning salon, state-of-the-art gym, a solarium, and not one but two kitchens. Like I mentioned, the exclusive wellness center includes a spa with side-by-side massage tables and a steam room. Even the staff can live at luxury and Simon's former mansion. For the hired help, there are separate live-in maid and butler's quarters and a 24-hour staff kitchen. The one million dollar world-class security system reportedly includes retina and fingerprint scan authorization, so safety isn't an issue at this palace. Outside the amenities continue, and you'll find a lap lane swimming pool, cabana with retractable roof, outdoor wet bar, and even a petting green. After selling this masterpiece, Simon went on to unload another home owned in Beverly Hills just months later for $14.5 million this time. This mid-century modern house was in the expensive Truesdale State's neighborhood, and despite the expensive price tag, Simon was actually at a loss on this sale. Besides these two homes, he also put a hidden Hills Ranch he owns on the market too. Simon currently owns a $24 million mansion Malibu, which may be the most famous of his properties. It's even made appearances on his TV shows over the years like the X-Factor Celebrity Auditions that was taped at this home. Simon purchased this contemporary single-level estate in 2017, and it's situated on a private bluff at the end of a gated tree-lined drive. There's over 1.5 acres of gorgeous garden surrounding the home, and sweeping ocean views from the entryway down the Grand Hallway. The mansion is just meters from the coast, and inside stands 10,000 square feet of space with seven beds and nine baths. Simon's Malibu house was built in 1988, and of course, updated since, offering stone and dark wood floors, Venetian plaster walls, and high ceilings with large skylights. In total, the home has six fireplaces, some inside and some outside that you can choose to cozy up aside. Rooms in this home include everything from formal dining spaces decked out in marble to a home gym. While all the bedrooms are luxurious, the one which Simon and his partner Lauren share is decidedly the best. His and hers master suite boasts a massage room spa, fireplace, and private patio. One of the other guest suites has its own entrance, and there are additional staff quarters too. Simon's home is ideal for indoor outdoor Malibu living with a ton of space for entertaining. Pocket doors throughout open up to the tropical backyard, where there's a plunge pool, hot tub, barbecue, and multiple terraces. There's a koi pond directly off the home too, which has Florida ceiling glass windows, and aerial shots show a lighted tennis court on the property, which the media mogul seems to use as a car park. Simon actually spent months recovering from a broken back at his Malibu home during 2020 after an electric bike incident. While the star doesn't usually share picks inside his private home, he did share a photo of himself with his son in the garden with their pet dogs. Reportedly, Simon is planning to spend more time at the property in order to enjoy a quieter life away from Hollywood with his family. Before we end this house tour, of course, Simon also owns homes in his native country of England. It's said that he owns more than one here, but there's less information readily available about these places. The main one we know about is in London, and he paid over 20 million dollars for the place in 2018. The X Factor judge sold another home in West London and moved into this beautiful 18th century house, as it was closer to some good private schools for his son Eric. Now, Simon has been given the go-ahead to transform the historic home into a house that meets the modern requirements of the family. Reportedly, Simon wants to add expensive upgrades like a 15-meter outdoor pool with its own pool house and bar. The home apparently has seven beds and features like a sunroom. Plans say he will extend this sunroom and add a glazed conservation lantern roof to allow for sunshine throughout the year, even in the privacy of the home. Security conscious Simon is adding state-of-the-art security and upgrading the lighting as well as the security cameras in the house. He'll add three new vehicle gates, but one thing he plans to leave as is is the Village Victorian mailbox attached to the outer wall, which is still in use. Some other touches Simon plans to add to his London home include underfloor heating and air conditioning and a timber pergola on the surrounding grounds of the property. Now we've taken a look at three properties X Factor judge Simon Cowell calls home, so I think I'll bring this house to an end right there. With such a massive fortune to his name, I'm not surprised his homes have all gone above and beyond. And I also learned how much the man values his privacy. I mean, one million dollars in security in one house? Crazy. So what did you guys think about Simon's mansion? Which one was your fave? I can't tell if I like the Beverly Hills spot he sold or his current Malibu home better. I would say Malibu if only because it's on the coast. Be sure to rate his homes down in the comments. I'd also love it if you guys like and subscribe to my personal channel if you haven't already. I love seeing you guys comment and getting to know you guys better over there. Link in my latest video. Queen Victoria's here. Couldn't stand Victoria. Queen Victoria. First thing I said was Rebecca Black, is that you? She literally looks like her so much. Thanks for tuning in. Don't forget to follow me on Instagram and I will see you all in the next video. Bye.


Hello and welcome to the session. In this session we are going to discuss how to apply the properties of operations and use them as strategies to add, subtract, factor and expand linear expressions with rational coefficients. Now consider a linear expression or we can say another bright linear expression that is 5x plus 2y plus 3z. This type of linear expression is divided into three parts that is variables, operations and coefficients variables such as x, y and z as used in this linear expression and the operations used in this linear expression are addition and multiplication numbers like 5, 2 and 3 that are used in this linear expression are called coefficients. The coefficients in a linear expression are rational that is integers, positive or negative fractions or decimals. Then these are called rational coefficients. Now let us learn how to apply properties of operations and work with rational coefficients and first we have associative property which says addition or multiplication in numbers or in algebra remains the same no matter how the numbers are grouped. For example we will verify 2 plus 3 plus 5 in brackets is equal to 2 plus 3 in brackets plus 5 here on the left hand side we have 2 plus 3 plus 5 in brackets. So we first simplify the terms in brackets and later add 2 to it so we get 2 plus which is equal to and similarly on the right hand side we have 2 plus 3 in brackets plus 5. So again we simplify the brackets first and then add 5 to it and therefore we get 5 plus 5 which is equal to 10 here both left hand side and right hand side are equal to 10 and same applies for multiplication that is 2 into 3 into 5 in brackets is equal to 2 into 3 in brackets into 5 and this is equal to 30 that is on the left hand side we have 2 into 3 into 5 in brackets. So here also we simplify the brackets first and we get 2 into 15 which is equal to 30 and similarly on the right hand side we have 2 into 3 in brackets into 5 so we get 6 into 5 that is simplify the brackets first which is equal to 30 as it rightly we can write associated property for addition as A plus B in brackets plus C is equal to A plus B plus C in brackets and for multiplication it is A into B in brackets into C is equal to A into B into C in brackets. Now we are going to discuss commutative property to fasten the buttons of your shirt it does not matter that you fasten the last button first or the first button last so the order in which you fasten your buttons does not matter that if you are eating something then you choose first and then swallow you cannot reverse this order that is you cannot swallow first and then chew later so in case of fastening the buttons of your shirt the order in which you fasten the buttons does not matter so it is commutative but the order in which you eat does matter so it is not commutative so we say that an operation commutative in the order of the numbers does not change the results that is for example 2 plus 3 is equal to 3 plus 2 that is equal to 5 that is if we add 3 2 2 or if we add 2 2 3 the results will always be 5 so we can say that it is commutative similarly we have 2 into 3 is equal to 3 into 2 that is 6 also in algebra we can write a plus b is equal to b plus a that is for addition and a into b is equal to b into a that is for multiplication now if the distributive property which involves both addition and multiplication suppose we have to solve 3 into n plus n in brackets where n and n are any numbers to multiply m and n by number 3 which is outside the brackets that is 3 into n plus n can be written as 3 into m plus 3 into n and using the same method we can solve 3 into 7 plus 2 and we get 3 into 7 plus 3 into 2 so solving any expression using the other rule is called distributive property and if we solve left hand side and right hand side separately we get the same value here on left hand side we have 3 into 7 plus 2 in brackets on simplifying the brackets first we get 3 into 7 plus 2 that is 9 which is equal to 3 into 9 that is 27 and on right hand side we have 3 into 7 plus 3 into 2 that is we get 3 into 7 is 21 plus 3 into 2 that is 6 21 plus 6 will be equal to 27 hence we see that both the results are equal to multiply a sum or difference by a number multiply each number in the sum or difference by the number outside the parenthesis that is if we have a into b plus c in brackets so we can write it as a into b that is a b plus a into c that is a c or if we have a into b minus c in brackets we can write it as a into b that is a b minus a into c that is a c now let us take a few examples and understand these strategies to add, subtract, multiply, divide, factor and expanding linear expressions now let us consider 5x plus 3x that is 5x can be written as x plus x plus x plus x that is adding x 5 times plus 3x can be written as x plus x plus x so here we get the total as apex for addition we add life times together where coefficients of x are 5 and 3 so we add 5 and 3 and we get so we say that 5x plus 3x is equal to apex similarly we have subtraction now consider the expression 2x minus 3x we can also write this expression as 2x plus or minus 3x by using the properties of integers that is we get 2x can be written as x plus x plus minus of 3x can be written as minus x minus x minus x and therefore we get x plus x plus of minus x is minus x again plus of minus x is minus x plus of minus x is minus x and therefore we are left with minus x now if we directly apply the properties of integers to the coefficients in the expression we get 2 minus 3 into x that is we subtract the two integers and the sign of the result would be the sign of the larger integer so 2 minus 3 will be equal to minus 1 into x and we can write it as minus x here the larger integer is 3 and it has a negative sign so our answer would be negative next we have multiplication consider 10x into 5x here we multiply the coefficients together and the variables together and therefore we get 10 into 5 into x into x which is equal to 10 into 5 is 50 into x into x that is x square so our answer is 50x square here we should note that we obtained a nonlinear expression as the power of x is 2 next we move on to the reason if you have the expression 10x upon 5x to simplify this expression we first divide the coefficients that is 10 is divided by 5 and we get 2 next we divide the variables that is x is divided by x and the answer is 1 and we get 2 into 1 that is 2 so we get 2 as the answer next we are going to discuss factors and expansion if we consider the expression that is 10 times x plus 2 on expansion we can write it as 10 into x 10 into 2 which is equal to 10x plus 20 also if we want to write 2x plus 10 in the factor form we can write 2x plus 10 as 2 into x plus 2 into 5 now taking to common from both the terms we get 2 into x plus 5 now to check whether 2x plus 10 is equal to 2 into x plus 5 in brackets now 2x plus 10 is equal to 2 into x plus 5 implies that 2x plus 10 upon 2 is equal to x plus 5 if we take the expression on the left hand side we have 2x plus 10 whole upon 2 and we can write it as 2x upon 2 plus 10 upon 2 which is equal to x plus 5 and it is equal to the expression on the right hand side is complete our session hope you enjoyed this session


I'm Neroop Ambushankar. I pretty much had most of my experience in med device health care related stuff like computational geometry, implants, implantable pacemakers and stuff. So here I'm currently at Intel as an end-to-end AI framework and solutions architect and a product manager for federated AI with OpenFL. Today we'll be talking about building better AI models with private data using openFL. I'm actually glad that the previous speaker talked about federated learning.  So today's agenda will be mostly divided into four sections. It's as federated learning as an overview of what federated learning is, what centralized versus federated. And this next section, I will introduce you to OpenFL. I'll try to keep it at a higher level, make it more interesting and productive as much as possible. But feel free to pin me any time after the talk to ask for specific questions in the architecture. But in this, we'll talk about the FL architecture, what the core values and how you can get started. Then I will talk you about the Intel's federated learning security, which is a security extension on top of OpenFL that we actually have for reducing the attacks and risks. Like Intel has something called SGX or Intel Software Guard extensions. And now we can prevent some of these risks that one of the actual audience asked in the previous talk to. And then we'll talk about all the real world usages for OpenFL. Let's dive into the overview. So let's talk. AI needs data. And usually the issue is in terms of health care, finances, and I think there's also an IoTG interest here, needs data. So getting these data have challenges a lot. So especially most of these data are distributed across organizations. It has its own rules for sharing. And most of it is across your boundaries, which means specifically like health care, you have FIPI compliance, OSHA compliance, GDPR, and all these things. So you have a lot of issues of taking this data. So data replication or data movement from countries or passing or organizations is generally very hard. And you also need to do data de-identification. That is, you don't want to identify who is sending this data when you train it. And data sovereignty challenges, which was also touched upon in previous talk, where every country owns the data. So federated learning, we need data. So how can you expand it in robust and federated learning? So I just want to touch this thing where just like a teaser before I talk about in detail is we actually ran the world's largest health care federation with OpenFL. And it actually has 71 sites across six continents, 20 plus countries. And it's called the Federated Tumor Initiative, where you actually look at brain tumor segmentation. And I'll talk about these results, but it accuracy and everything is great. So it's just a teaser to get you excited about things. So that's what is one of the. And last year in 2022, it was one of the most 25 most downloaded nature communications, nature paper in health care. This is the paper that came from Intel Labs and UPEN. So anyway, so first going with what is traditionalized central learning. So you start with a training site. You always want, let's just for the purpose of this, call this aggregation site, because that's where the final model comes out. You will start with a global model, or any model before you train. And then you look for institutions. For the sake of purpose of this talk, let's just think of institutions as collaborators, because these are all terms for federated learning. Just trying to get you think in federated learning terms. And these institutions can be substituted like health care institutions, bank institutions, or anything that you can think of, compliance, governance, that is required, that has all these data that you wanted to do some training on. And they have these data. You have to send this data to a central site. This is where the problem is. The previous slide, I talked about the GDPR, HIPAA compliance, governance, all this stuff. And it's not easy. Even for the test that we did, we saw that it takes around almost one to three years. It's crazy how much of compliance and governance you have around all these data to send it. So but in centralized learning, you still have to pass it, pass through all these things, send the data in, you train a model, you get a trained model. Let's reverse the paradigm. In federated learning, you start with the institutions. You have a global model, the same model that you thought about earlier. You send it to this institution. So you don't move the data, you send the model out. And then you actually train the model locally. And then you get an updated model locally to those sites, to those hospitals, data is still private. And you send the weights alone back to a central aggregation server. That comes to that aggregation site that I talked about. And then now, in this aggregation server, you do several kinds of weighted average or fed average. There's a lot of different algorithms that you could actually do this aggregation on the weights alone. Again, I'm stressing on weights only because you sent only the weights, and it's important for most of the privacy things I'll talk about later. And then you've got this updated global model, and then you repeat the process. And now you send it back to other institutions. And so you just repeat the loop until the training happens. But there are a lot of other collaborative learning techniques. So just in this paper that is there, there's something called institution incremental learning. This federated learning is not the only way you could do this distributed data privacy learning, where the image speaks for itself where you actually go ahead, have one of these institutions train it, send it to the other institution, and keep getting a final model. You can either do it for the entire dataset, or you could actually do it for a few epochs and keep it cyclic. So that is the cyclic incremental learning. So what I'm talking about right now is basically one of the studies that we did in that Fed's initiative paper that first started out with Intel and UPEN, where we actually did this for brain cancer segmentation. And we compared all these kind of collaborative learning techniques. So first is you could see that CDS is the centralized data sharing. It's just another name where you send the data. So centralized learning. And federated learning at CDS, you can actually see this. The convergence and all this quality is actually 99%. So you achieve it, whatever you could achieve now with centralized learning. FL is superior to the other two techniques that I mentioned here, like IIL and CIIL in the previous slide. And you see these kinks, right, sharpness. And that's because it's kind of something called as catastrophic forgetting. So you take it to one institution, it trains well. And depending on the data when you send it to the other institution, you can end up messing with some of the pre-trained value or generalizing it. So that's the issue with what's what you see in CIIL and IIL, these sharp curves. And then the degree of independent and identical distribution of the data actually matters whenever these institutions are. And it matters a lot more in federated learning than centralized learning. We saw in that paper, so these are the outcomes. And federated learning did take a lot more epochs to actually converge to the same quality as that of centralized. But I will be explaining in the paper about the tumor segmentation. You see that it's actually more, still have all these site issues are still OK because you still have larger access to data and helps in generalizing and getting a good model overall. And this paper that we said is one of the first major paper for federated learning and health. And at this point, I also want to give you a little sense of how it all started. So federated learning was actually a published paper from Google in 2017. So how it all started was they actually wanted to train data from our Android phones. It's private to our data, so they can't central it moving to the servers there. So they said they coined this term federated learning actually, where they actually did the training in our phones itself and sent the weights alone back and did some weighted average. They called it federated average, but it's like any other weighted average. And there are several ways of algorithm like medium and different things. It actually, that's how it started. So with that, let me actually introduce you to OpenFL. And OpenFL is basically started out with this initiative that we kind of had there. Intel and Eupen started out, looked at it, see centralized learning. We wanted to extend it to the super duper large 71 sites thing. And so that's what federated learning is. And OpenFL came out of it, and we made it open source from Intel. So like I said, there is, again, you introduce these aggregators, these collaborators, which is the institutions, and you send the data across. And OpenFL is an open source software. It's completely federated learning system architecture that you want to do it. And it's in Linux foundation now, Linux data plus AI. We moved it from Intel to Linux already of last year. And it's released in Apache license, so please go ahead and use it. It's very easy to use, scalable and manageable for large federations. The whole purpose we were building it for, we wanted to go beyond 71 institutions and all the step federations, bigger and bigger federations is what the whole intention is. And it's completely open source, please contribute. And we also keep privacy preserving machine learning in mind. So because it involves data, both the data and the model is actually protected in transit use and storage. And there are a lot of privacy things that comes, and there are additional extensions that I'll talk about that Intel is doing and still researching and wanting to contribute. So OpenFL solves that data silo problem with software and accelerates time to market deployment of federated learning. It provides the greatest access to data through enabling secure privacy preserved data. And one other thing I want to mention is this local training, all these collaborators and aggregators, which is institutions and aggregators, the one kind of data that is getting shared is sent across the network is NumPy. And whoops, is NumPy. And the thing about NumPy arrays is it helps to protect all these data owners and IT securities that are very strict about sending data across over the network. And having just NumPy arrays helps with all these IT security passing and safe reviews and et cetera, et cetera. And because the aggregator is sending only NumPy arrays, all the training happens only in the collaborator and institutions. So which means just sending the weighted means you could have the aggregator being framework agnostic. So everything happens there. You get the weights alone and you do this weight and you don't think. So which means it supports Keras, TensorFlow, PyTorch, Onyx, and JAX, amongst other things today. Distribution happens through GitHub. And installation is pip install OpenFL. PyPy, it's that easy to install. And we also have a Docker. With that, I will talk a little bit more about the OpenFL architecture. So it has an aggregator, like I mentioned before. And it has collaborators, which are the institutions or health care, bank, whatever you can think of. And in general, the main thing is it has a concept of this plan, which is the actual code. And it defines most of the code that runs in the federation, so the whole experiment that runs. So what all is in there, you have the model, the data loader specified, and other things that the federation needs to run, such as hyperparameters, network topology, information, et cetera, et cetera. So all this is defined in the plan prior, out of bound, before the experiment even starts. So then you send this workspace. You have this concept of workspace in the aggregator, which is basically all this FL plan, the code, everything that is in there. And you send it to the collaborators. And this happens before all the experiments start, so you need to send it. And it's easy for the collaborators to just start with, to import it with just one command, like FX import, and all the dependencies and everything that needs to run. So the aggregator, the model owner defines the model in the plan, tells what they want to do, what's the network connection, who are all the collaborators that need to collaborate, send it. One FX import imports that whole environment into your collaborators and institutions, the hospital institutions. And then the aggregator also serves as the certificate authority, which is the one that actually tells which collaborators can collaborate with me, that will help in securing this mutual TLS connection, a secure connection. And then in all this is set up, you can do one single command, FX start, and the experiment starts. So what happens? Let's go to the next view. So it is actually based, is built on top of GRPC. It's a client server model. So on startup, basically, after this, the collaborator asks the aggregator what that it wants to start, what kind of tasks it needs to perform. Mainly, there are two kinds of things in federated learning. I would broadly say federated AI, because there's federated learning and there is federated validation. And they can help care. Let's say the FDA wants to do something with the model. And they want to even validate certain things. So sometimes you just send it to the collaborators, not for training, even the data, just to validate that, hey, whatever we are doing with some of the data is this even correct? So you can get some data from, say, Europe, ask someone in India to validate it, just for the sake of things. So the collaborator basically asks what tasks. So you will get the model from the aggregator and telling the aggregator, say, do validation and, let's say, training. So it will first validate the model that it receives from the aggregator. That's like validating whatever you receive.  And the aggregator will receive it, do from all the other collaborators, will do the weighted average, and will create the global model. And at this point, you kind of finish one look. And yeah, one other point I want to make is, of course, the aggregator weights. There are several different algorithms, so all of this depends on the task you perform. So the model itself right now, so that's why I said, so in the initial FL plan, I'll talk more sooner after the talk maybe, just in the interest of time. But I will explain it clearly if that's OK. Thank you. Yeah, so open FL, so the progress and stuff. So again, it started after Google published in 2017. We kind of started with the UPAN initiative. And in 2018 to 2020, we did it. We released it in February 1st, 2021, for public release in GitHub. Then we moved it to Linux Foundation for everyone to use it in LF plus data AI. And we currently did six major releases, and 1.6 is coming soon in the next quarter, mostly. Very early quarter. It's about 600 stars in GitHub today, and it's still growing. Please, please contribute and bring larger federations and stuff. Let's create better models. The core values, it's always been all about scalability. Like I said, please grow. More larger federations are needed just because we are excited after seeing all this health care need. Again, I'm stressing on health care. That's only because we did it, but bank institutions, a lot of cases will add more generalizability for this. Security, we have a lot of security. We'll touch more in the forthcoming slides. And the developer experience also, we wanted to be easy. That's the single start. OpenFL is distributed with, like in GitHub, PyPy Docker Hub. And there's a lot of tutorials for you to start. Please take a snapshot. And the slides are also on uploaded. So please use them. And you can see the tutorials start for TensorFlow PyTorch and even verticals. We are trying to make the documentation easier too so that more and more people adopt it. All right. So with that, let's move into the next interesting thing about security. So there's a task at risk. So you have access to data. It has its own huge issues, and especially when you go across boundaries and stuff. So I've touched about four things here. And some of them that we've tried to eliminate or address at least the way we can. One is kind of model IP theft, which is you think of one of the collaborators joining and trying to stay there only to take the model off instead of really contributing to it. So that's an issue. We should try and help protect with that. And then model parameter kind of updates where instead of helping with adding this data, they maliciously actually change the model parameters in ways that the entire federation does not succeed if someone wants to have ill federation things. And the other risk attack is the model parameters. Data extraction, the previous speaker also touched about a little bit on this. So if the federation is limited, you do this data identification. But still, instead of training, you kind of guess what their data is and kind of tap it out of the model. So smaller the federation has this issue, but larger federation, it's harder to do it. But still, it's there. And the last one is like a malicious entity participation, which is like if anyone is coming in. So I will touch how Intel's FL Security helps with this, and especially this, because you need some kind of a person telling, hey, are you the participant that I know? Can I trust? Only then come in, which is the collaborator. So you need that attestation. So we provided that attestation services. So heritage learning framework need to have additional security to manage these risks. So with that, I want to introduce something called as Intel's SGX, which is Software Guard Extension. It is at the software level where the main thing is it brings about a creation of something called as a trusted execution environment, where the code that actually runs within the trusted execution environment, TEEs, in short, is encrypted within the memory and not even viewable by the root user of the system. So after the code is accepted, everyone is agreed within the participant. You actually, the model owner, if they encrypt the code and the entire plan, none of this is viewable by anyone outside of them. Even if they have the decryption key, they will be the only one who is viewing. They'll be the only one who can change the code, because they can't even view it. So there's nothing to change because it's running, right? So anyways, the trusted execution environment with other security features. This ITA is another Intel thing called Intel Trust Authority, which is the attestation service. It's almost like saying a new participant is coming in. It's like asking, hey, do I know you? Sorry, you're not a participant. That's one way of dealing with it. Intel Trust Authority is, I don't know you, but hey, can you verify that the code, everything that I have, is trusted so that I can join the federation and contribute? So that's where this ITA is like a thing where you could check and they will attest whether this participant is trustable and their software guard extension and dependencies are all valid so that you could do it. So this is like at a hardware level thing. And so what that provides is data confidentiality. So data never leaves the premise of the data orders. And model IP is protected end to end use. So after everything is done, it's still in there in memory until you grab it and only they can decrypt it. No one can access this TEE, trusted execution environment, except the ones that are already given access to. So integrating attestation, only verified and approved ML models are running, as long as the FL plan is agreed upon by the aggregator and the rest of the participants, which are model owners, collaborators, everyone, before the experiment starts. Once the experiment starts, the enclave is secure. You cannot tap into it. Participants cannot insert unapproved code at any time. So this way, Intel XGX provides a mechanism for prevent stealing and model for reverse engineering data distribution. This is just a little more insight. Maybe just in the interest of time, I won't touch too much into this. But it's just that the governance service is basically what that gives that additional security. It has its own ledger, admin, security registry, making sure that the participant is in there. And if you see, it has all these small blue boxes. That is the Intel software guard extension. That's the thing that is saying that, hey, I won't do it. The governor is there just to say that, hey, is your Intel XGX valid? And is everything in the plan valid? And are you a participant that I know? Yes, you're valid. Check, check, check, check, check. Only then I will let you run the experiment. After the experiment starts, no one can mess with the experiment yet. That's the apart. So what FL security does is, apart from collaborators and aggregators, it has a governor security entity. All right. How does FL security mitigate this? Of course, model IP. So the trusted execution environment in collaborators alone will prevent any kind of model to be accessed so you cannot take anything. You cannot change the model parameters. Again, same in the collaborators, you have these TEs. And data extraction attacks cannot be done because TEs are there both in the aggregator and collaborator. And why I'm separating it out is sometimes, depending on the federation, people might see that, hey, I want an XTX only in the collaborators. I don't want an XTX in the aggregator, but having it everywhere will make it more and more secure. The level of security and privacy depends upon the federation and how they set up. The last thing is the Intel Test Authority verifies the TE enclaves. And the governor actually verifies that the plan is in there. So with that, I will go with some real-world usages. I gave a teaser for predator learning for rare cancer boundary detection. So like I said, we ran the world's largest health care federation. So what you see here in the graph is basically, let me, all the blue ones are what is publicly available models, data across the world. And all the orange ones are all the vast additional private data that we got access to with this initiative. And the green ones are the out-of-sample validation for all the ones who are machine learning savvy. We need to make sure that we validate outside of the current data set so that it's generalizable and not just specific to the data that we trained it on. And then there are out-of-sample validation with clinical trial data. And the aggregation server is a single server that is in the east coast of the United States where all this happened. So the results is where. So what we did is this brain tumor segmentation where there is something called enhancing tumor, tumor core, and whole tumor. So the blue color is the tumor core. And the enhancing tumor is what the doctors would want to see to treat the regions around it. And then if you actually go for radiotherapy, they would want to treat everything in the red regions. And you want to accurately try and segment this as much as you can so that you can treat the patient correctly. So in short, what we saw was the accuracy of identifying these regions was saw a 27% gain. This is the local validation. So this is the local validation from the training set. Saw a 27% gain for ET and TC, which are the tough ones to segment. And for TC, we saw 33% gain. And the even more encouraging one is the out-of-sample saw a 15% gain and 27% gain for each of these regions. So the key results of the FETS initiative and Intel and UPAN initiative is we saw that increased data can improve performance. However, the data science alone cannot. So of course, the quality of data matters and how they label it. Large sites can also benefit from this kind of collaboration with increased data. FL is robust enough for data quality issues. The reason I have this point is we did find some of the sites mislabeled their input data. And we found that the accredited learning did not throw these things off too much. So we still did converge. And we got a model out with the accuracy that I mentioned before. So it improves the accuracy mentioned. And then singlet and triplet model is basically, conceptually, you can use one model for all the three segments, or you can use three different models to detect these three. For this initiative, we found that both the techniques actually did give the same very close results. So we didn't find much difference. Although, I think intuitively, you can argue like saying, having three different models, each of these can produce better results. Yeah, that's just one. See, other things that we are working on. So of course, we started with UPEN initiative.  We've done something with Frontier Development Lab, where it's about now, oh, one other industry. So now it's going to go space astral NASA. So NASA wanted us to, and Mayo Clinic wanted to actually see the effects of cosmic radiation. And they already have data from all these space station folks. And think of that data. Genetic data is actually huge. Somehow they transmitted. I don't know how. And the data privacy involved in these things are huge. So they are using OpenFL. And they want to study this effects of cosmic radiation so that they could send further of human beings up there. Plus they also want to study how some of the cosmic radiations is affecting us today over a period of time. So that study is going on. Montefiore is using OpenFL, particularly after COVID. Two years ago, everything was COVID, but especially for acute respiratory syndrome studies and based on that, and death based on that. And VMware is using OpenFL. And luckily, they also contributed something called EDEN, which is a new compression pipeline designated for OpenFL. I will leave this with a slide. Please take a picture. So OpenFL has moved to a new home in the next foundation. It's actually joined by VMware, Lido's, U-Pen, Flower Labs, and Driving Future of the project. Use OpenFL, create high quality data, high quality models, and please contribute back and looking for any contributions at any skill level. And even regular ones, we'll push it to the maintaining positions. It's in GitHub. So the QR code is there. Send a message in Slack, and here's all the links for you. So Patrick is the chair for the OpenFL. And Prashant actually is one of the TSC members, and I'm TSC members too. And I'm also doing the product side of this. With that, I would end my talk today. Well, thank you. Yes, please. Yeah. I think there is a mic that goes on. It's not my role, yeah. So when you're sending the weights to aggregate or global model, what are the techniques that you guys may have used to eliminate the bias? Because there's a lot of local context, including environmental conditions. Like example, it could be the pollution, traffic, or whatever, right? So depending on the business case, use case. So how do you ensure when you are again pushing back to the institutions, the bias that has caused with the weights coming across all the local models is not compromising the accuracy? Thank you. So one thing I have to say is that federated learning is kind of a method to do this learning and actually give an access to increased data. General problems, I see that, yes, you're saying there's increased access to data, has increased issues, such as more bias coming in. And the characteristics of the RCA? Correct. So such kind can be addressed if the model owner, which is the aggregator in this case, but you could think of someone decides and creates the FL plan such that they look for specifically these biases to address it. It is interesting. I'll take it back to the team and see that if we can address it specifically in the aggregator. And another question is, how much do we want to address it ourselves? Because what it is? Yeah, sure, pleasure. So final model IP and while it is training, security, privacy issues, and then these are the main things we are kind of, and then data privacy, right? That's what we are trying to address. And usually the model owner, even for model IP, there are questions that will arise. Because let's say Europe is training or NASA in this case, right? They don't want Intel or any open FL folks to have access to the final model. And the data privacy people who don't want to say, hey, I'm using this data and I know that everyone in, I'm scared to mention China. China has more COVID or something like that. But they don't want to do some kind of attacks there, right? So you should be very careful. And let's say data owners gets their own data IP privacy. And model owners gets their own model IP security. And we are just enabling those things to happen such that people use this framework. And biases would need to be handled by model IP, but I'll take it. Yes, I had a couple questions. Am I allowed to continue here? Or should I get? OK, thank you. I'll make it quick. Oh, yeah. The first is I noted that you're building on top of Grameen. Is there any roadmap plan to integrate with more proprietary offerings like GCP, Confidential Spaces, or AWS or Azure offerings? Yes. So there is a lot of things that happen with privacy-preserving machine learning. It's its own thing altogether. And so we are built it on top of Grameen because SGX is there and it uses Grameen. So we are working with the team with an Intel XGX team and looking at their road maps and see how we can integrate it and make whatever we have maybe available generally public, satisfying those needs also. But yes, we do have some of those plans. And then just real quick, TEs are very memory constrained. What are the challenges and how much data could be trained over and did you have like batching or streaming capabilities to handle that? So this is built on top of SGX. There is also something called a TDX that Intel is coming up with, which has additional memory just for the things which is increasing the security. Like saying, it's not just the code that we want to run. We want to run the entire thing in the enclave. And then they've increased the memory as far as I know. Several TVs, I don't quote me on that. But I know that TDX has more memory just to address that concern. And integration of OpenFL with TDX is planned in the road map sometime next year. Thank you. So when the weights are transferred back, the improved weights are transferred back to the aggregator. I mean, the assumption is that that's private. And I guess for the medical application, people may not want the fact they have a condition to be known. But they probably don't care if the picture of their tumor is somehow leaks out. And with financial information, that might not be the case. Either way, at least OpenFL is not having the security portion. So we are currently doing pilot tests with some of our customers, the FL security. So I'm going to talk about the FL security, which will become later available. So in there, so if you decide your FL plan before and after it's encrypted, when you start this whole thing, it goes across the secure connection. Only the already agreed upon participants can even access it. But what I'm saying is that none of the collaborators also can access the model at weights at any point. They're only the model owner, which is aggregator in this case. But I'm purposefully separating it out because we are thinking of, we've heard situations where the model owner is different from the aggregator. But I'm saying during training, no one has access to the weights until the experiment ends, or they can decrypt it. Who can decrypt it? Are there only the aggregator? Let's say that they are the model owner. They can decrypt it. And if your concern is, the aggregator themselves, I don't want them to see the model. That is why we are dealing with the aspect of a model owner, who is separate from the aggregator. Because that way, you could have any kind of, how do I put it, hardware entity to be the aggregator, and the model owner to be this conceptually institutions, NASA, to say that no one can do anything. Finish the model, send it back to me, or I will grab it, and I'm the only one who can access to the model. And then after that point, we don't deal with anything. It's their issue of dealing with modeling. So there are two assumptions. One is that the transmission is secure. It's secure. And then the second one is that you trust the aggregator. That's why I said the enclave, correct. You trust the aggregator. And if the aggregator is the model owner, they will have the encryption key and decryption key. But if the model owner is separate, the aggregator also cannot see anything. Because it's inside the encrypted enclave. So you start the FL plan. You encrypt it. It goes into the enclave. No one can open it until you have the decryption key for that. It's almost like saying, everything is planned. I sent everyone inside this room. I locked it. And it's all in its own de-identification things. And things are running. Hope that helps. So I'm not sure I understood it all. OK. It's all we talked about. Yes. So you're talking about re... Oh, I see. So you're OK. You're talking about whether they could go back and look at what's happening from a specific institution. Yeah. In a smaller federation, when you get the final model out.  No matter what. First is V3, you can be competitors. So that's why I said participants comes in. Participants agrees upon what code runs and participants but still cannot see what is finally being presented unless the model owner gives the right to do so. Yeah. Hi. Thank you so much. I was wondering, because I come from privacy, I do personally not see federated learning as a means to be outside the scope of GDPR. So only differential privacy is a state of the art, like anonymizing data in a sense that you cannot say it's personal data anymore. I would even argue that those weights, even though they're sent encrypted to the aggregator, there are still, they could be re-identification attacks theoretically possible, that it's not like complete data privacy. It's just improved security, I think, for private data. So my question is, first of all, what do you think? But do you also offer differential privacy in your setup, in your architecture? Local differential privacy to randomize the data before it's? I'm going to take this question back to my security team. Differential privacy. I will make a clear note of it because I'm not really an expert in security-based things in extension. So the team did it. Thank you, thank you. Yeah, yeah, but differential privacy concern within the weights go back to the aggregator, correct? Yeah, because often you hear that it's only with differential privacy federated learning, it's really like making the data really private or really confidential. Sure, OK, thank you. Yeah, sorry, I have to wrap up. I will wait outside. Please ask me questions. I'm really happy to answer.


Remember the basic concept of an inverse function. If f of a equals b, then f inverse of b is equal to a. And very important to remember, the minus 1 in f minus 1 is not an exponent and does not mean 1 divided by f. We can apply this concept of an inverse to the trigonometric functions. So if sine of x equals y, then sine inverse of y is equal to x. And likewise, cosine x equals y gives us cosine inverse y equals x, and tangent x equals y gives us tangent inverse y equal to x. Because the trigonometric functions are periodic, we have a problem. For example, sine of pi 6 is equal to 1 half, so inverse sine of 1 half should be pi 6. But sine of 5 pi 6 is also 1 half. So could we have inverse sine of 1 half equals 5 pi 6? I don't know. Could we? Well, no, we really don't want that to happen. Since we don't want the inverse sine of a to have different values, because then it wouldn't be a function, we have to restrict the range. And so this leads us to the following definition. We define the inverse sine function, written sine inverse or arc sine, satisfies inverse sine of x equals y, where sine of y equals x, and y is between minus pi over 2 and pi over 2. So for example, let's find the inverse sine of negative square root 3 over 2. So definitions are the whole of mathematics. All else is commentary. If y is the inverse sine of negative square root 3 over 2, then the sine of y is negative square root 3 over 2. Since y has to be an angle between minus pi over 2 and pi over 2, it helps if we know the sine of the angles between minus pi over 2 and pi over 2. We see that the angle whose sine is negative square root of 3 over 2 is minus pi thirds. And so the inverse sine of negative square root 3 divided by 2 is negative pi thirds. We can define the other inverse functions in a similar fashion. So the inverse cosine function, written cosine inverse or arc cos, satisfies the relationship inverse cosine of x equals y, where cosine y equals x, and this time we restrict the output to between 0 and pi. And the inverse tangent function, inverse tangent x equals y, where tangent y equals x and y is between negative pi over 2 and pi over 2. The inverse secant function will have a range restriction between 0 and pi. And there's also an inverse cosecant and inverse cotangent function that nobody uses. So let's find arc tangent of 1, arc cosine of negative 1 half, and inverse secant of 2. So definitions are the whole of mathematics. All else is commentary. Arc tan is another way of writing the inverse tangent function. So to find arc tan of 1, well, if y is arc tan 1, then tangent of y is equal to 1. So since y has to be between minus pi over 2 and pi over 2, we need to know the values of tangent between minus pi over 2 and pi over 2. And so we see tangent is 1 at pi over 4. Arc cosine definitions are the whole of mathematics. All else is commentary. Arc cosine is another way of writing inverse cosine. And so to find arc cosine of negative 1 half, y equals arc cosine negative 1 half means the cosine of y is negative 1 half. Since y is between 0 and pi, we want the cosine values between 0 and pi. We find the angle whose cosine is negative 1 half is 2 pi thirds. Inverse secant. So again, definitions are the whole of mathematics. All else is commentary. To find the inverse secant of 2, we know that y is the inverse secant of 2 if secant of y is equal to 2. And that means we need to recall our secant values. Well, that's OK. We've memorized the values of secant. Well, nobody does that. But definitions are the whole of mathematics. All else is commentary. Secant is 1 over cosine. And we're arranging that tells us the cosine of y is equal to 1 half. And we pull in our values for cosine, and we see that cosine of y is 1 half when y is equal to pi thirds. And so we know the inverse secant of 2 is pi thirds.


Who else going to be some changes at the White House? Yes Well ladies and gentlemen, I wanted to share with you this morning some of the more Significant results of this trip and to take a few of your questions before we leave for home The journey to Europe has involved many highs and yes some anguishing moments It took us to one of Europe's youngest capitals and two of its oldest Into a city which symbolizes the continuing quest for European unity and at every stop I Emphasize that our European friends can count on the United States to be their partner To help them grow to support their democratic aspirations and to stand with them to protect the peace We're leaving today With our Atlantic ties strengthened and we're returning home Mission accomplished Let me summarize what I believe to be our lasting achievements First our visit to the Federal Republic has strengthened u.s. German relations and the prospects for continuing peace in Europe The German leadership characterized our visitors opening a new page in German history. I believe that our partnership and friendship have never been greater or Stronger at the bond economic summit. We agreed to a common strategy to ensure continued economic prosperity and job creation We also move closer to our goal of launching a new multilateral trade round to eliminate barriers to free trade All the summit countries have agreed to the need for a new round all, but one agreed that it should begin early next year We were pleased that our partners endorsed u.s. Efforts in Geneva to achieve significant reductions in nuclear arms We also reached agreement for intensified cooperation against international drug trafficking Next at the European Parliament in Strasburg We set forth a sensible framework for improved u.s. Soviet relations based on strength realism peaceful competition and Negotiations I Conveyed the Soviet Union once again America's heartfelt desire for peace The constructive common-sense initiatives we propose to reduce tensions between us deserve a serious Soviet response In Spain and Portugal we further enhanced our ties with two close friends and valued partners It was heartening to see firsthand the strides these two courageous democracies have made both politically and economically It's been a long historic and thoroughly worthwhile trip Issues of major significance were dealt with openly vigorously and in-depth From our meetings came a strongly shared commitment to freedom democracy growth and European unity and now I don't think I've left anything unanswered, but you probably want to ask some questions anyway All right, Andrea On defense yes now you've accepted that and you've also accepted no increase of freeze on all codes including Social Security Can you explain about your campaign promise and why you've changed your mind? Well, let's take the defense matter first the zero growth is for One year the first year and then the growth rate that we'd asked for for the next two years is included in this but at the same time and Just a little while ago and somewhere around four o'clock in the morning in Washington I had the assurance of the senators That this is done with the proviso that if at any time it the zero growth Reveals in the coming year that it is going to in any way Reduce our national security or harm it in any way. I will be back asking for a supplemental to overcome that now the second thing you asked about was Social Security Well, first of all I never I never felt during the when I was answering the Accusations that were made in a somewhat demagogic way in the campaign that I was going to cut the the Benefits reduce them for the recipients and I was denying that I had any such idea or would ever have as any such idea I didn't have in my mind that we were talking about any potential or possible increases But it was taken that way interpreted that way so okay. I live with that the thing that has been agreed to Actually will amount to about the same Benefits as the two two and two which I proposed now we have found that the two two and two That I had proposed That most people aren't aware of all of the terms of that And this is particularly true of the social security recipients Most of them were not aware or did not recall that if inflation drops below three percent there is no cola and Most of them were not aware that our two two and two was two Plus any percentage of increase in inflation above four percent and When this was pointed out to them in surveys Roughly around seventy percent of the people preferred that and said that they would They would take that now as I say we have all right So we have held for one year a freeze in this and then Return to the normal cola process So and as I say for three years that comes out about even to the two two two so the other thing that I did say was that unless I was faced with a mandate and I would suggest that I Was say faced with a mandate when seventy nine percent of the senators which means Pretty much half-and-half Democrat and Republican Demanded That we have some Some curbing of the colas In the fall with possibility of meeting Gorbachev and why is it that you can preach? Reconciliation to the Germans who who committed so many horrors and not say the same thing to the Soviet Union on this trip When I thought that I had said some things I told about the changes that we felt in this unifying of Europe should take place But I also emphasized that it must take place peacefully, but I was not suggesting any any hostile action The with regard to going to the UN No, we have no confirmation yet Mr. Gorbachev is coming the word probable was Is about the best way to describe it, but it did not that statement did not come from him I then extended an invitation that if he was going to be here the door was open for a meeting between us and That still goes so the the ball is in his court first to decide whether he's coming here and then second As to time and place for such a meeting if he is willing President in the past you've drawn a distinction between Dictatorships on the right and Marxist dictatorship saying those on the right can evolve into democracies, but communist dictatorships never do Yet here in Europe you have talked about the changes you want to see in Eastern Europe where communist dictatorships are most deeply entrenched How do you see those changes taking place and what is your role in those changes? Well, we've said that we would be most helpful to anyone who Wants to make this modification. We have seen enough examples in the Americas alone of military dictatorships or just outright dictatorships and Pressure from the people in the democratic process Changing those to the point that today south of our border Roughly 90% of the people in what we call Latin America Are now living in democracies or in countries that are moving toward democracy and the only two totalitarian powers in our Hemisphere and Nicaragua and Cuba the So it is true that there is evidence that Right-wing governments or dictatorships well, we're standing in one that has gone from dictatorship to To democracy the same was true in In Spain when we were there But it is true that the what has been called the Brezhnev doctrine has Been predominant that once they get their grip in a country. It doesn't change. There are evidences That that isn't true. What is a matter of fact that too happened here because in addition to dictatorial tradition there was a time when communism seemed to be Moving in here and again the people of Portugal have made that change bill and I'll take you Mr. President a few days ago an official of your government Richard Pearl and the Defense Department Said that it was his opinion that it was time for the United States to start violating or stop observing the salt agreements First of all, sir, what do you think of him offering that opinion and second? What do you think about it? Is it time to stop observing the salt agreement? Well, first of all, you know in that country of ours everyone's got a right to express their opinion and He was doing no more than that Something that I know is very precious to all of you But I would I'm trying to think of how I how I want to answer this This question maybe you better reframe that last part again so I can get my mind switched Let me put it this way sir. What do you think is it time for the United States to stop observing the salt treaty? Which all right, we've never ratified We have tried on what seemed to be a verbal agreement between ourselves and the Soviet Union for some time that even though We have not ratified that treaty it had been signed by the negotiators that we That we would both seek to abide by the the term There's considerable evidence now that That has been rather one-sided and If it has been then there's no need for us to continue But whether we do or not that's a decision to be made down the road Actually, we have not come to a point in which we in any way in our own build-up our Violating or going beyond the terms of that treaty it is possible with regard to one system of weapons That we might come to such a point and we'll make that decision then and if we do we'll do it openly and we will do it with full knowledge of the Soviet Union Everywhere that you went in Europe the foreign leader is opposed the Nicaraguan trade embargo and we now hear that Costa Rica has opposed it Why is it sir that some of your closest allies don't back you on this and don't seem to feel that or take on the Sandinistas of the threat that you think he is I Don't think there's any question Chris that they don't agree with us about the threat they do They know what Nicaragua is the other hand. We're running into a kind of a philosophical difference here I think with regard to sanctions. We did a lot of soul searching about it ourselves There are number of people and certainly a number of governments who just don't believe in that as a legitimate weapon on the other hand when we were trying to get aid for the people of Nicaragua in their struggle for democracy and against totalitarianism of Many of our own people in the Congress brought up the fact of how could we be doing this at the same time that we continued to Maintain relations Well, we had continued to maintain relations and even including trade relations with them as a refutation of their charge that we were seeking their overthrow All we have ever sought is that they as one faction when I say they I mean the Sandinista government that Sandinista government is never been legitimized by the people it is one faction of a revolution that overthrew a dictator and they Stole that revolution away from the other factions which we now call the Contras and the leaders of the Contras were leaders in that revolution also and in doing that We have felt that what we what we are seeking and been trying to pressure them to do is to Come together again in discussion and negotiations to restore The promises they themselves had made as to what the goals of the revolution were and in doing that And as I say to refute their charges that we were somehow threatening them with aggression And if you will remember there was a time when mr. Ortega had us every other week Landing the Marines in Nicaragua and we never had any intention to doing such a thing. So we maintained our embassy there We continued our trade to show that what we really wanted to do and Then in this recent Vote in the Congress we found many congressmen justifying their position on the grounds that how could we still be doing business and and Yet wanting to to aid these other factions the revolution and we have decided that Pressure is needed to bring them to the realization that they should Restore the original goals of their revolution In recent days Mr. Gorbachev has had some rather harsh things to say about the United States and about and about you If there is a summit meeting, what would you have to talk about and what do you think that such a meeting could? reasonably produce in the current climate Well, I think there'd be a lot to talk about and I just happened to believe that it's time We started talking to each other instead of about each other and With regard to the harsh things that he said to say about me wasn't new about that That And I think has been consistent not only with me, but with every other American president It's just their way of doing things I'd like to go back to the defense to budget a few days ago You told us it would be an irresponsible act to freeze it this morning. You seem to say it's okay to freeze it But if you discover in the future that it is irresponsible, you'll go back to Congress Doesn't that suggest sir that you don't really have a firm view of what figure is needed and doesn't it open you up in the House of Representatives to the house taking more out of the defense budget Not one penny more should be taken out of that budget That has been given now and as I've said We're talking about the year of 1986 and I have the agreement of the Senate that if This represents and I in my own Mind field that it does represent a cut in spending beyond which we should go that They recognize that I will be returning for a supplemental Appropriation On the other hand I have to point out to you with that in this We have gotten More than 90% of what we have asked for in the budget It will amount to some 56 billion dollars this year almost 300 billion dollars, which was our goal over the first three years and there's no questioning the importance of Sending a signal to not only the world but to our own business and financial communities that we are determined to Deal with a deficit problem That has been a democratic heritage for the last 50 years of deficit spending continued deficit spending And once and for all we're going to try to get hold of it Yes, Leslie As you probably well know you've been called the Teflon president now for almost five years You in this Senate vote on the budget Asked for a six percent increase in defense and no cuts in Social Security Why shouldn't this be interpreted as a cave-in on your part? And do you think that the Teflon has begun to peel? No, I have always believed from all my past experiences a negotiator that You recognize that the other fellow is probably going to offer less than whatever you ask and I've always kind of believed in leaving a cushion there for dealing but This time Things have changed. This was a deal. I don't like the word deal. This was the working out of a budget that was acceptable to the to the Senate as well as to ourselves and I think that the first one we presented was a very sound budget But I recognize that in the give-and-take That must take place in a system such as ours To attain more than 90 percent of what we asked for means that all right we can We can do some giving along the line also Oh I have a president waiting for me here. Can I take one more? All right one more and then I'm sorry Since you've shown a a willingness to compromise on the budget on defense and some of the spending programs Would you also be willing to compromise on your tax reform program as a price of getting that? We're 90 percent of it to accept perhaps a temporary tax increase or a surcharge No, you've now come down to what was part of our success in getting what we have they all know that I absolutely will not accept a proposal for a tax increase I Think it is the worst kind of economic practice to do that. I think it would endanger our recovery and They know that I will veto Any proposal that comes to me for a tax increase? They also know that I have a signed letter signed by a hundred and forty six representatives which is enough to sustain a veto that I have that in my pocket also So they tell me I have to go that your president is waiting Yes, I'm going to yeah Stefan camera of Portuguese television. Yes One question Mr. Pershing Would you compare to reception you have here in Portugal? With those in other countries in Europe Would you compare your reception here in Portugal?  I have been impressed by the warmth of the people by their Open hospitality and and welcome to me and That has held true here as much as in any other country and I have been greatly heartened by The reception of the people now if in your mind you are thinking in terms of certain demonstrations Well, I'd have that in my own country. There is a faction wherever you go that's on the other side and It happens to be a faction that kind of goes out of its way to be rude and nasty in expressing its opinion, but I've just come to accept that as part of the way of life and as Harry Truman said if you can't stand the heat Stay out of the kitchen. So I just have to tell you I'm most gratified I think I leave with the sound friendships with the people of your government personal friendships as well as Alliances between us or agreements between us and I'm very pleased Let me just say one thing and then I have to have to go back here Since there has been a lot of discussion about Some members of my administration and one in particular and this being Mike Dever's last day. I just want to say to you That I Consider Mike's leaving in the nature of an amputation and It is me that is suffering the amputation He has been with us a number of years. I have never found fault with anything that he's doing with his loyalty with his friendship and With the common sense that he has always used and that extends to the arrangements for this trip And the part that he has played in the arranging of the trip and while it was very difficult I know that most of you were totally exhausted Some of us to manage to survive a little better If so, it's because we had Mike working in our behalf Particularly and he's going to be greatly missed. All right. Thank you all