Hi this is Laura Chappell and welcome to this video on Deep Space Networking Traffic Analysis. In this video I'm going to show you how I open up a trace file inside of Wireshark and begin the process of customizing Wireshark for Deep Space Networking Traffic Analysis and we'll go through and learn about the bundle protocol and we'll learn about the TCP convergence layer. So let's go over to Wireshark and get started. In Wireshark I've opened up the trace file depicting the Deep Space Networking Traffic. Now we're talking about delay and disruption tolerant networking here or DTN we simply refer to it as and in this scenario we have a host that is sending a bundle of data in bundle segments to another host to be relayed to a third host. So let's see what we have in this trace file. First of all we have some traffic that doesn't have anything to do with our DTN communications. It's this OSPF traffic. So I'm going to get that out of the way. Now what we're left with are some interesting packets here. First of all these top packets that you see they're UDP packets they're kind of sprinkled throughout the trace file. If you were to select one of those packets and look down in the packet bytes window what you would see is in the data portion it appears that we have a host that is announcing its DTN endpoint ID. So <IP_ADDRESS> is announcing that its endpoint ID is N1 kind of like its name. On the second packet there we have <IP_ADDRESS> announcing its endpoint ID as N2 and the next packet <IP_ADDRESS> announcing its endpoint ID as N3. All right that information right there is very interesting but it's not a data transfer process that is just an announcement. What we're interested in are the TCP CL packets. So frame number seven there is the first time we see a TCP convergence layer packet and this is just a keep alive. By looking at these keep lives we can get a general idea of which hosts can see which hosts. So in frame number seven we have <IP_ADDRESS> sending a keep alive to .4. In the next packet we have .1 sending a keep alive to 3. In packet 9 we have 3 sending a keep alive to 1 and then in a little bit further on we see in packet 19 we have .2 sending keep alive to 4. All right we can build a picture from this. This is what we know so far. It appears that we have four hosts and based on the keep alive we can see which hosts are talking to which hosts. So we can see based on the keep alive that .1 can see .02, .2 can see 4, 4 can see 3, 3 can see 1 but we don't see any keep lives going between .1 and .4. We don't see any keep lives between .2 and .3. So perhaps we have something in the way between those two hosts. It could be something like a planet. So here I have a picture one of the various pictures of Venus with a color filter on it. Now let's go back to the trace file. So let's take a look at when data actually begins to be sent here. We see an awful lot of those endpoint ID announcements and we see a bunch of keep alive and we see some acknowledgments for those keep alive. When we get down to frame number 57 that's the first time we see a larger length frame that probably has some data in it. So let's create a profile in Wireshark and we'll begin the customization process to help us analyze deep space networking traffic. So in the bottom right hand corner on the status bar in Wireshark it says profile default. I'm going to right mouse click on that column there on the bottom and say new and I'm going to name this profile DTN example and I'll say okay. Now Wireshark takes me back to the default settings for profiles and this font is way too small. There's no way I'll be able to see that. So I'm going to zoom in here using the enlarge button on the main toolbar until I get to a font size that I like. Now with all of my profiles I always add one special column. I like to have it up with every all the work that I do analyzing traffic and that column is one that will tell me the conversation numbers in the trace file. Wireshark numbers all the UDP conversations and it numbers all the TCP conversations. It starts numbering each of them at zero and they are exclusive of each other. I'm going to go into the very first packet in here and inside the UDP header there is a field called the stream index field and I want to add this as a column. To add a field as a column you simply right mouse click on that field and say apply as column. I'm going to right mouse click on that column heading and choose to align center for the contents of that column. Now we've started creating this great column but we're not quite finished yet because our TCP traffic is not showing up with a stream index number. We've only told Wireshark to add a stream index column for UDP. So I'm going to right mouse click on that column header and I'm going to choose to edit the column. And I can see right here that the field that it's based on is UDP dot stream and I'm going to add on to this to say or TCP dot stream. It doesn't matter if you do TCP stream or UDP stream or the or this direction doesn't matter either way that will work and I'm going to say okay. There we go. Now as conversations are intertwined it's easier for me to see which conversation I'm currently working in because I have the conversation number on the left hand side. So the next thing I want to do is I want to pull out the most active conversations in this trace file. I'm going to go up to statistics and conversations and I'm interested in the most active conversations based on the TCP layer. I'm going to sort based on the byte count from high to low and there I can see I have two conversations of interest here. These top two are what I want to look at. That's data flowing. So I'm going to right mouse click on the very first line here which is traffic between dot three and dot one. I'm going to right mouse click and prepare a filter based on the selected value going in both ways and then I'm going to go to the second line. I'm going to right mouse click, prepare a filter and this time I'm going to say or selected going in both directions. Wireshark builds the filter for me in the background and I can see exactly what this filter is based on, the IP addresses that it's based on and I'll go ahead and I'll just apply that filter now. So I have two conversations, the top two conversations showing in this trace file. I'm going to go to file and I'm going to save this as a separate trace file now, just these two conversations. I'll go to file, export specified packets and I'm going to call this DTN example. Now I'm going to open up that trace file called DTN example because by default Wireshark only saves the filtered packets. So here we go. Now we have some keepalive segments. Let's take a look down in the packet details window. Here we can see we have an ethernet header, an IP header, a TCP header and then after the TCP header we have the DTN TCP convergence layer protocol or TCP CL. It has its own header and for keepalives all we have is this single little field here saying that it is a keepalive. I want my keepalives to kind of melt into the background here. So I'm going to set up a coloring rule to change the way these show up in the packet list pane so they kind of melt away. I also don't need my capture filter anymore. So in the detail window I'm going to right mouse click on this keepalive packet and I'm going to colorize this with a filter and create a new coloring rule. There's the syntax put in there for this coloring rule and I'm just going to call it TCP CL keepalive. On the bottom of this coloring rules window I'm going to click the background button and I'm going to change the background color to sort of a kind of a washed out grayish color let's say. I'll click okay and okay. Now those keepalives have their own background color of this gray showing up. The next thing I want to do is I want to look for anywhere where it says bundle protocol and I should expand the protocol column so you can see what's marked as TCP CL. Frame number 14 shows up and in the info column it says bundle TCP CL segment. TCP segment of a reassembled protocol data unit. Wireshark's TCP reassembly function is on and we'll talk about how that affects the view in just a little bit. But if we look inside of frame 14 I'm going to move this up. What we can see is we can see that we have the TCP header and then right after that Wireshark is telling us that three different segments have been reassembled to create this bundle segment. Let's stop for a moment and let me show you how bundles relate to bundle segments which relates to TCP segments. The bundle protocol is essentially an application and you would have some other application that uses it. Let's say you wanted to do FTP across the galaxy. You could do that. You would type in your FTP commands but that command would go through the bundle protocol to transfer data. We would have a bundle here of data and bundles need to be transferred around. It could be scientific information. It could be video. It could be pictures of the surface of a planet or something like that. It doesn't matter. You have this big bundle of data and the bundle protocol is a store and forward protocol. Again it's covered in RFC 5050 if you want to go look up some information about this. But when a bundle of data is going to a target system it can be split up into bundle segments and that is what we see in this trace file. In this trace file the bundle that is being transferred from the first host to the relay has split the bundle up into four different segments. When we look at it on the cabling system it's really strange because we're so used to an application sending down a certain amount of data and then the TCP segments go all the way to the end of the data and stop. That's not the way it's done with the bundle protocol. A TCP segment may have some data from bundle segment A and it may have some data from bundle segment B inside of it. So you can see that if this first bundle segment is going across the network it's got a TCP segment another TCP segment and then part of the third TCP segment is part of segment A and then bundle segment B goes out but part of bundle segment B is sitting in the third TCP segment here and then we have a TCP segment devoted to bundle segment B another one and then we only needed part of another TCP segment but why stop the TCP segment and create a new one? Nope. We will have bundle segment C and it starts in the middle of a TCP segment. Literally you will see a header inside of this segment right here indicating that this new bundle is coming up. Same thing with the final bundle segment. There will be a header right at the beginning of this bundle segment saying that this is the start of a new set. So let's go take a look at these bundle segments as they show up delivered by TCP segments. So in the info column we see on frame number 14 it says bundle TCP CL segment and when we click on it we have an indication here of which packets were required to put together this first bundle segment. Frame number 11 frame 13 and frame 14 and the numbers in parentheses are how much data fits into this bundle segment. Now we can also see below this this is the TCP convergence header and unlike the TCP or the TCP CL keep alive there are more fields there are a total of four fields in this TCP convergence header. So we have the packet type field so this is data then we have this is indented so don't get stuck on this these are not new fields this is the summary line for these two below. There is one bit that would indicate if this is the start of the entire bundle and there's another bit that indicates if this would be the last packet of the entire bundle and then we have a line that tells us what that how I should say how large that TCP bundle segment is that is a bundle segment length. So let's do some customization of Wireshark based on these fields. Number one I want to take the packet type field and I'm going to right mouse click on this and apply this as a column there we go it's easy now to see the difference between keep lives there and where data packets are and then I also want to do the same thing for segment contains start of bundle I'm going to right mouse click here and this is the start of bundle packet so I'm going to apply this as a column and we can see that it's true I'm going to center align that and then this next field segment contains end of bundle I'm going to add that as column as well and I'll center align that now it's pretty easy to look through the trace file and see which packets are the start of the bundle and then farther on we'll be able to see which packets are the end of the bundle that's the end of the entire bundle not the bundle set or bundle segment but the bundle itself to make it even easier to detect these I'm going to set up two coloring rules I'm going to set up one coloring rule that turns all packets that are the start of the bundle a green color and then the end of the bundle red so in frame number 14 I'm going to right mouse click on this line that says segment contains start of bundle and I'm going to choose colorize with filter new coloring rule and I it's already set to a one because this is the start so I'm going to just name this start of bundle and then for the background I'm going to choose sort of a lime green ish now by setting that coloring up it's very easy for me to detect where the start of the bundles are in the trace file three of the packets should have matched that here's another thing we can do we can right mouse click on that field and just simply prepare a filter based on the selected value that just puts that filter to detect the start of bundle packets up in my display filter area and the far right hand side of the display filter area there's a little plus there I'm going to click that plus and I'm going to turn this into a button I'm going to give the button a label which is going to be s dash bundle that to me means start of bundle I'll say okay now I have a button when I click on it any packets that have the start of bundle bit set to a one will show up let me clear out the filter that the button just applied I want to do to do the same thing with the end of bundle I want to give it a color I'm going to give it a vibrant red and then I'll make a button so I can easily detect the end of bundle and just show those packets now this packet has the end of bundle bit set to a zero I'm going to change that to a one when I do my coloring rule I'm going to change that to a one when I do my display filter button so I'm going to right mouse click on this packet prepare colorize with a filter new coloring rule name it end of bundle and I'm going to give it a background color of this vibrant red I'll say okay okay so now it's very easy oh I forgot to change the number well it's good for you to see how to edit coloring rules to edit a coloring rule we go to view coloring rules and I wanted to change this to a one at the end there we go all right so now we can see there's the start of a bundle there's the end of a bundle there's a start of a bundle and there's the end of a bundle right here the first start of bundle we see going from <IP_ADDRESS> to <IP_ADDRESS> so the first thing we see in here is we see a bundle being transferred from .1 to .3 in bundle segments after that bundle is done right there then down on frame 37 we see another bundle starting and this bundle is from .3 to .4 this is the first bundle being forwarded on by .3 so it's being relayed on and then we see the end of that bundle after it's been relayed and the very last packet that we see here from .3 going back to .1 it's a little strange it's its own bundle we can see that the start of bundle is set at true and the end of bundle is set at true first of all I want to pop up a button up here for end of end of bundle well this I'll use this packet since it has the end of bundle bit set on I'm going to go inside the TCP convergence layer here right mouse click just going to prepare it as a filter based on the selected value there it is that's what I want to build the button on so I'm going to click plus and give it a name e bundle that means end of bundle to me so now if I want to see the end of bundle packets only I can click on the second button I want to see the start I can click on the first button I'm going to go ahead and clear this out now you'll notice that after this bundle has been relayed from three to four there's this packet the strange packet here where it has the start and end of bundle bits set on that means it's its own little bundle and this is actually a reporting packet so let's go inside of it and take a look let me bring up this let's see I'm going to bring this sort of up so we see it up on top and it is frame 49 we're interested in and when we look inside of frame number 49 we see the TCP convergence layer indicating it's the start of a bundle and the end of a bundle also so we're obviously not transferring a bunch of data here and then we'll see the primary bundle header and if we look a little bit farther in here we will see a payload header and beyond this we have the administrative record so this is indicating right here that this is a bundle status report that's the purpose of this packet I don't want this packet to be colored red just because it hit the end of bundle coloring I want this packet to have its own coloring so I'm going to right mouse click on the line that says administrative record type bundle status report and I'm going to choose to colorize this with a filter make a new coloring rule there's that line admin record type one and I'm just going to say that this is the admin report I'm going to turn the background yellow for this one say okay and now there we go this is the admin report and that admin report is being sent from dot three to dot one to say that I successfully delivered the bundle on for you so now let's go up and just take a look at some of the packets in here and how this how this works in frame number 14 wire shark says this is a bundle segment and sure enough if we look inside we can see the data packets that were required to make up this bundle segment packet 11 packet 13 a packet 14 and we can see the amount of data in each of those segments to make up in each of those TCP segments to make up this bundle segment and then when we look down at frame number 18 again on the right hand side it says bundle TCP CL segment and if we look inside of that frame we can see which TCP segments were required to make up this bundle segment and there's the weird one right there so some of the data that's in packet number 14 is part of bundle segment a whereas some of the data in packet 14 is part of bundle segment b the second bundle segment that's very interesting it's a bit of a different protocol and if we keep looking down here and frame number 26 frame number 26 is listed here as bundle TCP CL segment and if we look inside of there we can see oh let's see 14 I think I did that one before let's clear that out and let's go beyond that to the next bundle segment oh maybe I didn't I don't remember so I'm clicking on frame number 26 and in frame 26 oh it just hadn't click I didn't click on frame 26 before it was still selecting frame number 18 so now I'm on frame number 26 that says some of its data is in frame number 18 some of it is in 23 24 and 26 and then the last bundle segment is actually the one that finishes up the whole bundle so frame number 32 we can see its data and if we look down here we can see exactly which frames were required which TCP segments were required to get this bundle segment across it is pretty it can be pretty confusing to look at this because we have bundle segments and then we have TCP segments and they don't necessarily align along the way here we had some acts showing up so here we have the first bundle segment up here and then we have an act down here on frame number 20 this frame number 20 act that we see it is acknowledging receipt of this bundle segment this act down here on frame 27 is going to be acknowledging this bundle segment the act counts up so if I look at frame 20 I see an acknowledgement saying act length 4096 that was how much data was in the first bundle segment I go to the next act and it says now I'm acting up to 8192 bytes because it's now acknowledging the second bundle segment we'll see the next act coming up right here this act is acknowledging the third bundle segment so this guy is acknowledging this bundle segment which of course includes those packets and we can see that it's counted up to 12,288 and then we have the last act here which is counting up also the very last bundle segment it can be sort of confusing to see what's happening when especially when we have wire sharks reassembly feature built into it but what I did is I made a little tcp spreadsheet that goes through what's happening here we have bundle segment one or a bundle segment B bundle segment C bundle segment D there and these are the frames required and the amount of data in each of those frames and we add the data up in the bundle segment we subtract three for the tcp convergence layer overhead and that leaves us with the actual segment of data the amount of data in there same thing here on the second one we it required four packets to get this across subtract the tcp cl overhead and we're at 4096 here another 4096 bundle segment and then the last one isn't quite as long so I'll bring that back in in a moment when we talk about the bundle header itself so all of that was the bundle being transferred from <IP_ADDRESS> to <IP_ADDRESS> and then all of a sudden we see <IP_ADDRESS> transferring this transferring this data over to <IP_ADDRESS> and the same thing we have data data data data interestingly enough we have some acts that show up later but for something else so when we look at this let's let's reassemble what is being transferred across this network as this bundle is being relayed we can go into any one of the stream one packets and just right mouse click on the frame in the packet list pane and select follow tcp stream this is the data that is being transferred by this bundle at the top of this bundle you have these at signs and those are keepalives so when you click on those you can see you can click and that wire shark will take you to the keepalive packets in the background what is following that immediately the next three bytes is actually the tcp convergence layer for the very first bundle after that we have a bundle header and after that this is all the bundle header and then after that we have the payload header so when we look at our segments going across we have a bundle header that is applied only on the first bundle segment every single one of these bundle segments though starts with a tcp convergence layer header that's how we know it's the beginning of a bundle segment the very first bundle will be followed by a bundle header and then it will be followed by a payload header so these are the header structures that you would see in something like this we need to have that tcp cl header in there because that provides us with the interface between tcp and the bundle protocol it's kind of the shim between the two of them in this slide i'm showing you the amount of data that is carried in each one of these tcp segments and when we add that those up these are the payload values and when you look at the x we just count up from 4096 then we count up from there until we get to our final value let me just finish up by taking you into the bundle header for a moment we're going to go all the way down here to let's pull frame number 32 this is the last frame of the bundle after it has been forwarded to oh wait no that's not the one i want i want to go up to the first hang on i've got a filter on there no wonder so let's go let's go to it doesn't matter which one of the red packets we go to they'll basically have this very similar information in them i'm going to go to this frame 46 let's say that is the final packet of the bundle after it's been forwarded to <IP_ADDRESS> and let's go into the bundle header and let me bring the bundle header up in that top window there we go there are some strange things about the bundle header here like for example one of the big things that catches people is that this bundle header length field does not include the whole entire bundle header it includes only the fields that follow the bundle header length field so the bundle header is actually 49 bytes and then the payload header is another four bytes so this packet is telling me the last packet of each of the bundle segments and then i can see down here this is the amount of payload now this is different than the final act so if i come up here and let me just show you what i'm talking about here this act right here that we saw this act says that the amount of payload is 15,272 bytes sure enough that's what it is if we add up all of the payload from each of the bundle segments but when we look inside of a bundle header to see how much payload it thinks has crossed the network it's giving us a different value it's giving us 15,219 and the way it gets there is we have to subtract out the 49 bytes from the bundle of the bundle header plus the four bytes of the payload header we got to get rid of 53 bytes from the extra payload headers and bundle header at the front so that's how we get to that particular number so i've shown you how we can add columns i've shown you how we can add buttons into wireshark and i've shown you how we can add coloring rules into wireshark to make analyzing the bundle protocol and the tcp convergence layer protocol a little bit easier


Welcome to the session on Assembler Directives of 8086 microprocessor. At the end of the session, students will be able to develop programming skills in assembly language. Let us see first what is assembly language. Assembly language is a low level programming language and is written for programming for computers, microprocessors, microcontrollers and other ICs. Now if you are looking for assembler directive, it is a statement to give direction to the assembler to perform some task of the assembly process and it controls the organization of the program. So it provides necessary information to the assembler to understand assembly language programming and generate necessary machine code indicates how an operand or a section of the program is to be processed by the assembler. And it consists of two types of statements that is assembly programming consists of two types of statements instructions and directives. Directives are translated to the machine code by the assembler but directives are not translated to the machine codes. Here some of the important assembler directives are given. So out of that data declaration directives under that some simple directives are there like DB, DW, DD, DQ, DT, then assume, N directive, EQ directive, PROC, segment, group, include, external public name, label, etc. We have some directives. So let us check briefly one by one. So in data declaration the very first one DB, the DB directive is used to declare a byte. A byte is made up of 8 bits. For example, if there is a variable or a declaration price which is of data type DB and the value for that price is 49 and 98 and 29. So here we declare array of 3 bytes named price and initialize them with specified value. The next DW, the DW directive is used to declare a word type variable. So a word occupies of 16 bits or 2 bytes. In the example, if you are looking words is a type of data is DW and the value for this is 1, 2, 3, 4, 3, 4, 5, 6. So here we declared an array of 2 words and initialize them with the specified value. The next is DD. The DD directive is used to declare a double word. So double word is made up with 32 bits or 2 words or 4 bytes. And if you are looking the example array DD and the value is 2, 5, 6, 2, 9, 2, 6, 1. So this will define a double word named array and initialize a double word with the specific value when the program is loaded into memory to be run. So next one is DQ that is defining the cod word. The directive is used to tell the assembler to declare a variable 4 words in length or to reserve 4 words storage in memory. So here we have an example big number DQ which is a very big number that is nothing but 4 words storage. The next is DT that is defining 10 bytes. So the DD directive is used to tell the assembler to declare a variable which is of 10 bytes in length or to reserve 10 bytes of storage in memory. Now we will move for the next directive assume. The assume directive is used to tell the assembler that the name of the logical segment should be used for a specified segment and the 8086 was directly with only 4 visible segments and they are like code segment, data segment, stack segment and extra segment. And in example if you are looking assume cs colon code. So this tells the assembler that the logical segment named code contains the instruction statements for the program and should be treated as a code segment. So here if you are looking the next directive EQU, this EQU directive is used to give a name to some value or to a symbol. Each time the assembler finds the name in the program it will replace the name with the value or symbol you given to that name. For example, if you are looking factor EQU 03H. So here the advantage of using EQU is used many number of times in a program and you want to change the value all you had to do so to change the EQU statement at beginning and it will changes the rest of all. So the end directive, end is end program, end p is end procedure and end s is a end of segment. So end is nothing but it signifies the end of the program model and the assembler will ignore any statement after an end directive. But about the end p, end p is specifically used for ending the procedure indicates the end of a procedure and end s it indicates the end of logical segment. Now we are moving what is PROC or the procedure directive. The PROC directive is used to identify the start of the procedure and the term near or far is used to specify the type of the procedure. For example, if you are declaring smart PROC far this identifies that the start of procedure named as smart and instruct the assembler that the procedure is far. So you have a question please think and write the answer. Question is the directive that marks the end of an assembly language program is you have the options end s and end s and end none of the mention. So please take a pause of your video and write your answer. So your answer is B and the answer is end. So the directive end is used to denote the completion of the program. Now the next our directives are group include. Group is nothing but it can be used to tell the assembler to group the logical segments named after the directive into one logical group. And the examples are like a small system group code data stack segment. And the next one is include it is used to tell the assembler to insert a block of source code from the named file into the current source module. And this shortens the source module and an alternative is used of editor block command to cop the file into the current source module. So the next directive extom it is used to tell the assembler that name or label following the directives are in some other assembly module. For example if you call a procedure which is a program module assembled at a different times from that which contains the call instruction. And you must tell the assembler that the procedure is external for the assembler. So which will put information in the object code file so that the linker can connect the two module together. The same way there is a one more directive public the public directive is used to instruct the assembler that a specified name or label will be accessed from other module. So the next our name is used to give a specific name to each assembly module when program consists of several modules are written. And label it is an assembler assemblies a section of a data declaration or a instruction statements. So, directives are also called a pseudo operation that controls the assembly process and they indicate how an operand or section of a program to be processed by the assembler. So these are the my references thank you.


Thank you very much for inviting me to this conference. So I'm going to talk about the emergent collective dynamics in large-agent systems. It's so large, so it's not fitting a screen. So my main field is artificial life. And I was studied in 1987 in Santa Fe. I was in Santa Fe, too, in 1989. So then there's tons of interesting concepts and ideas. And then still, after 30 years, we are still working in this field. And then the aim of this artificial life field is to answer the question, what is life? By exploring emergent dynamics and open-ended evolution. So this talk, I'm going to update what's the recent development in artificial life on how the artificial life is committing to biological life, I hope. So this is my drawing when I gave a keynote at the artificial life conference in 2008. So artificial life is all about the theory of evolution on the brain. And back in 1950 to 1960, there is a cybernetics. It's about an autonomous robot and then a philosophy and Alan Turing's chemical reactions, and then also von Neumann's cell automata. These are the classic artificial life studies. And then from that, we have been developing all these interesting ideas. And artificial life started back in, as I said, it's 1990. There's a bunch of interesting ideas that came out. And then using that one, we try to understand what is life and then what the evolution is. So this year, 2018, I'm organizing this artificial life conference in Tokyo. And then, so this is our home page. And then theme is beyond AI. So there are a bunch of people interested in how to use AI. But I'm sure that the era of AI popularity is going to end within five years. And after AI paradigm, we have to think about new epistemology of artificial life and complex systems. So my principle is life must come first. And artificial intelligence is just a side effect of artificial life. So artificial life is going to be the main theme within five years, I hope. Then as you know, one of the big challenges for artificial life is how to use new technologies, which we call exponential technologies. So the computer is getting faster and faster. That's the main engine why we can use deep learning and we can use a bunch of data flows. So using exponential technology, the old ideas of artificial life models back in 1970 and 1980 can be updated. For example, like the Game of Life, random VR network, and also the Red Cray Reynolds Boyd model, it also can be updated with the exponential technologies. So for the first, my talk is about Boyd model, but Boyd model not in my computer, but with a K-computer and then also GPGPU. At the Boyd model, you may know that everybody been talking about Boyd model, which is separation, alignment, and cohesion. There are a bunch of ways to write down into the equations, but I didn't check carefully, but this one is more or less like a generic forms of Reynolds model. So simply using this one that I can... So this is the famous Boyd model with 256 individuals, and you can see there's a sort of self-organization of structures and patterns is evolving from random conditions. Even though this is quite a simple toy model, but it took a lot of our computer power when back in 1990. But now we have a strong computer, so I was hoping that we can compute more complex ones. So in order to assimilate a large-scale Boyd model, we need GPGPU and then also a K-computer. The K-computer is a supercomputer in Japan, but still it's compatible with the GPGPU because the K-computer is even fast that many people want to use it at the same time, so it becomes slower and slower. So this is the periodic boundary condition in a three-dimensional space. Then the numbers of the individual is 2048, and so there's a big flock is going from here to here. So going from the left to right, that's what you can see here. Because then the next scale is 16482 to the 11th. Then this different kind of structure emerges, like this one, which is connected by this small snake-like structure. The next scale is 130,000. By the way, the density is the same. The space is getting bigger and bigger to increase the numbers of the individuals, but the density plus space is always kept constant. And then you can see some interesting structures in there. Then you can even scale up to the next level. So this is a half million numbers of the birds. It's not birds, but it's individuals of making flocking behavior. So this structure is something interesting. When I gave this talk, the biologist studying bird flocking behavior raised his hand and said, he said this is not birds, but I knew that.  So one of the big questions with this system is how to identify each flocking behavior. So usually the people assume there's one simple flocking behavior, but actually what happens is there are many, many of them. So I use K-means, it's one of the simplest clustering methods, a self-organizing map, or a DB scan, or even deep neural network to classify what is flock and how many flocking behavior, flocking patterns that you can find in this space. That's what I was working with. Then one of the methods that I was using is not three-dimensional real space, but we have to use four-dimensional metric space. So XYZ plus the amplitude of... Sorry, it's my Gmail. Amplitude of the speed is also the important dimension. So this is the one that you see. So when you see this, for human observers it's apparent that there is a flocking behavior which is connected by the snake-like patterns, but if you want to use machine learning to classify them, usually it's difficult.  I will show you that within this flocking behavior there's a brownian or a singular levy-flight-like behavior you can find in here, but outside of this one there's a more coherent dynamics that you can observe. Then... So by using some techniques that we can temporarily follow how the flocking behavior changes over time. So you can see some of those flocking behaviors, like blue ones and all these kind of things, it's changing its patterns, but these are very slow dynamics. It's slowly changing its structures, but for these snake-like patterns, they have less lifetime so that they can vanish, but they are very interesting roles to connect from one big one to the other big ones. So for one of the techniques that we can use is non-negative matrix factorization. So taking... Usually the people use it as a document in terms, but for this void model I use... This one is the time axis, and this is the ID number of the individuals. So we can factorize into some sort of mode. Maybe these are corresponding to the flock, and then this is the IDs of the void. So this is one way to classify whether there exists flocking behavior in terms of NMF. And you can see... This is time from 1,500 time steps to 250 time steps. There's some waves coming up, and these waves are corresponding to these patterns, and this one is a kind of mode, so there's one. So you can see some of the mode.  Then we look into the speed. So this is the velocity, and this is the fluctuation of the velocity, and this is the density, local density, and then also this is the local density fluctuations. So if you look at the size, then the bigger, the larger size swarms have a slower time scale, slower speed, and then the density goes up. So... And then also the fluctuation has the same thing. Small to middle-sized flocks have larger fluctuations, but for the larger flocks have larger local density fluctuations. So the big one has a slow speed, and then fluctuation goes up with the density. But the middle-sized and then also small ones has much faster than the big ones, but the local density fluctuation is very suppressed. And as I said, there's an enormous diffusion that's going on in the large flocks. So in sum, what happens is there's two different kinds of fluctuations that exist. This is the floc size, and this is the susceptibility. So when you see... So the floc size is going from the scale of 10 to the second to the 10 to the fifth, and the bigger ones have fluctuations over density fluctuations, but the smaller ones, less than 10,000, or 1,000, is about velocity fluctuations. So the different susceptibility to the different external perturbation can be expected from here. So that's what I thought was quite interesting, because when you are scaling up up to larger than 10 to the fourth, then you can find a different kind of structure which is characterized by these different kinds of fluctuations. That's what I thought was interesting with these large systems. And also, people might say, well, because this one is very abstract model, but I have been working with this oil droplet, which is oreic anhydride with water. Then this is my very first experiment I did in 2007. So you can see a bunch of droplet is generated and then coupling with each other and moving around. So this is more like chemical gliders. The game of life is very fragile, but this one is much more robust, because this one is happening in the real space. So the one that I saw in the Boyd model can be applicable to this kind of artificial chemical droplet. Then I was thinking that maybe the one that we have found can be applicable to the honeybee stuff. So fortunately, I've been working with Jean Robinson in the University of Illinois. Then we have analyzed how the honeybee hive is going to show any kinds of corrective behavior or not. So we have five different honeybee hives. Then each bee has a QR code on its back. So we can trace each individual for a longer period of time. Then we can see what sort of individuality can commit to the corrective behaviors. Then I can tell you one of those discoveries that I found. First of all, this is a video that we can generate from the data. So this is where the heading of each bee, and the data is like this one. So this is time, and this is the positions in two-dimensional space, and this is the heading direction. And this is the bee ID number. So when the line is connecting to one individual to the others, it means that the bees are interacting with each other face to face. I don't know whether you can notice.  There's some collective bursting behavior that can be generated within the system. So now it's bursting, right? But now it's going to suppress. Again, it's going to be happening again, right? So in order to analyze this one, we look into this time series, and this is kinetic energy. So we can define kinetic energy for each individual bee, like for each unit of time, how much distance each bee can move around. So this is the definition of kinetic energy. So we can define total kinetic energy of the nest. So this global kinetic energy is bursting over time, and here this door was open.  So the question is how this bursting behavior changes when we open the door, and then before and after the door opening event. Then we noticed that there is an interesting two different characteristics of bursting behavior. One is an endogenous burst, which might be caused by the interaction between bees, and there's an exogenous burst, which is caused by externally. There's kind of stimulus is coming from outside. That's why the bee becomes activated, and then there's big burst is coming out. So I look into this. So this is time, and this is how the global kinetic burst is going up. And you can see this is for the endogenous, and this is for the exogenous. So endogenous starts very slowly, but exogenous happens rapidly. So we try to understand what causes by this kind of thing. So because we have a QR code for every bee, so as you see here, right, so this is the global kinetic energy. So before this global bursting happens, first of all this B574 is activated, and then another one is also activated. But this one is a queen bee, so it's always activating, but this B574 or B31 is only happens at a certain period of time. So in this case, B574 and B31 started to burst, then the other one is also start to burst. So we try to, again, we are using like NMF, regenerative matrix factorization. See what sort of a mode we can see here. So for this exogenous burst, there is only one or two, I mean, it's maximum, almost all the bees are contributing to this bursting behavior, but for the endogenous ones, there is a sequential activation of bursting behaviors. First of all, some bees become activated, and then those bees are going to activate some other bees, then other bees are going to activate. So there is a cascading processes going on here, but for the external exogenous burst, there is no such cascading bursting behaviors. It's more like everyone is going to activate at the same time. So we are using, this is a multi-dimensional scale. So which one causes the burst? You can see, so this is the different nest. This is 1201 and 1301 is a different conditions with the different numbers of the bees, but still the red one happens before the door opens, then the blue one happens after the door opens. So before the door opens, the bees are only in the closed room, but after the door has been opened, the bees can go out and come in. So there is an information cascade that is expected after the door has been opened. And then also interestingly, the individual bees that cause the burst is different before and after the door opening. So maybe some of the bees can carry the information and then causing an information cascade when the burst has been observed. So we try to do whether the foragers can be a pioneer bees. So when I say pioneer bees, that can cause bursting behaviors. And as you see here, once you open the door, there are a bunch of bees which go out to forager, to become forager, is also committing to the burst. So maybe two, the foragers can be the pioneer bees. That pioneer bees can cause bursting behaviors. So there should be some sort of information cascade is going on when you see these corrective behaviors. And then finally, one of the things that I want to discuss is the web services. So the first was a void model that we can see some collective behaviors of the big flocks and then was connected through this coherent behaviors. But there's two different fluctuations that we cannot observe. And then honey beehive, there's also different kind of clusters which maybe information is carried out from the foragers to cause bursting behavior. And those bursting behaviors is a characteristic of information cascade within a honey beehive. And web services is interesting because it's one of the most complex artificial systems that the human being has been created. And then there's one of the services called RoomClip that we are using RoomClip data to analyze what kind of evolutionary processes is going on within web services. So this web service is a tagging data. So each user can post a photo with some tags associated with the photo. So we have each user's ID and also what kind of photo or what kind of tag has been submitted to the system. And then the system size is about 10 to the... So it's almost 10,000 to 100,000 users has been involved in this web services. So what is interesting here is that... So we are using, like, Hawke's processes. Hawke's processes is like Poisson processes. So this one is Poisson processes without the second term. But with the second term it's more like a feedback from the other users. So once some user posts the photo then other user posts other contribution to the web services. If there are some interactions, if someone posts then the other one also wants to post again and again. So there's a feedback from the other users. That is represented by the second term. If you take into account this term then the interesting point is that... So the integral kernel is an exponential term. And once exponential term goes to 1 then it's going to be critical. So what I'm saying here is that if you can take an integral kernel as A times exponential to the minus B then this A over B is a nice index that you can see how the system is evolving towards what. So what you can see here is that the exponent in A over B is going to close to 1 as time goes by. So this is the time and this is the exponent. So exponent is starting from here and then going towards 1. But not exactly 1, right? So what we can see is that the exponent is somehow distributed around 1 means that the system is going to the critical state. You can say this one is like a self-organized criticality but it's not on the critical point. It's a little bit before the critical point that the web services are diverging into this point concentrated on this point. And more interestingly, we are looking at what kind of users is committing to these web services. So we are looking at the active users and then using a clustering algorithm to simulate, to characterize what kind of users that you can see here. And then distance between users is measured by the Jensen Shannon divergence, which is if the two users are using the same keywords, same tags, then we say these users are close to each other, right? So at one threshold, we see three different communities. Three different communities is spontaneously emerging from these web services. And these three different clusters are corresponding to they are the same users. This red one and blue one and yellow one, they consist of rather similar users. They have similar profiles. They have been using similar keywords. That's what I thought was interesting. And then more interestingly, when we are looking into who is generating the novel keywords, right? So RoomCrip users can generate, sometimes put new keywords, but sometimes just reusing the already existing keywords. So this one, the novelty is coming from same users. That's what I thought was interesting, that we're not going into the details, but if the same user is collecting to each other, then those clusters with the same profile users tend to generate more novel keywords rather than heterogeneous communities. So I can say that novelty or creativity is coming from, not from a heterogeneous community, but it's a homogeneous community has more potential to create new keywords. So my discussion is that... So starting this large system, also the evolutionary system that you can discuss that... Well, you can develop superorganisms, but you really have to increase the numbers of the size up to, say, 10 to the fourth. So 10,000 is probably the first critical size of the system that can create superorganisms. In our cases, different kind of fluctuation emerges after 10 to the fourth. So what we can expect is after this one, it's probably one billion numbers of those sizes. There's a new kind of superorganism that can emerge. So like in our physical systems, probably there are orders of the scale orders that where the superorganisms can emerge is determined by this number. But this is my hypothesis, but I don't know whether that's true or not. The second thing is, nobility is coming from homogeneous groups and also isolated groups, and they can make nobilities. So usually the people think that community must have heterogeneity to have interesting diverse cultures and new culture to come up. But actually what happens is that homogeneous groups can create more nobility comparing with heterogeneous groups. And the third thing, I didn't have time to introduce here, but robustness is also possible when you have a huge number of individuals. So what I did was, one is a Peter Gatchis model. So this cellular automata has two to the 223 states per site. So it's more like a cellular automata with each site has a big computer. Big computer is connected to each other. So if you have this system, it's self-simulating what happens in your own system. So using self-simulation that you can be much robust to the noise. This is also the von Neumann's idea that, von Neumann's dream, how you can make a self-reproduction which is robust against noise. And then what is required here is not just two states or three states, but two to the 223 states is necessary per site to have a robust cellular automata. That's what we can expect. And we, 70% have done with the simulations of the Peter Gatchis model. But maybe next time I can show you. And finally, my final quote is that this is a paper from Rodney Brooks, which I also invited him to the artificial life conference this year. And he, in 2001 Nature paper, that he says why his robot or why artificial life never becomes life. That's a huge problem for artificial life people. Why the Roomba is still robot and not the living systems. And then he discusses within his paper that maybe the first one is we might be just getting a few parameters wrong. Or maybe we might be building models that are below some complexity threshold. Or maybe perhaps it is still a lack of computing power. And then I thought maybe we have to go to number two and three. That's why I'm always making complex models. And then also because we are now having a big computing power, so two and three might be solved. And I hope my selection of parameters is right. But always people want to understand what's missing. I can be missing some fundamental currently unimagined laws that may exist in the world. And that's what I still dream sometimes. Maybe not probabilistic or not deterministic, but should be in deterministic model. Should be applied to understand what is life, to answer what is life. And that's the future of artificial life studies. Thank you very much.


Monday, the judge from the sister Burnett trial gave preliminary approval of the settlements that Remax and Anywhere proposed before the trial started. So before the trial started, Remax and Anywhere, they made an offer. They said, Remax said, we'll pay 55 million in a settlement before this case even begins to minimize our liability and our risk and our future uncertainty of what's going to happen with this case. And Anywhere did the same thing for 83 and a half million. Now, this was both companies, 50% of both companies cash that they had on hand. So they're like, here's half of the cash that we have right now to try to make this thing go away. So I'm going to dive into a little bit of that. Also, we have a very clear timeline on the judge ruling, okay? I want to explain that timeline, exactly how this is going to go to where we get to a point where the judge is going to give an injunction of what he believes should happen with the real estate industry as a whole, okay? So that's going to be interesting, this timeline here. And also, I want to just kind of briefly mention the lawyers that the defendants on the appeal have actually hired to go through with this appeal all the way to the Supreme Court. So all this is incredibly interesting. Let's dive into this preliminary judge settlement. Remax in Anywhere can breathe a sign of relief. Now, when you say breathe a sign of relief, you have to think about that because they offer 55 million before the trial started, and it's like, well, if the defendants would have won, then they just paid 55 million. They didn't have to pay, okay? They didn't know, but now the defendants did lose the case, but we still have uncertainty on what the appeal process looks like and if the defendants can win in the Supreme Court. And so it's still a risk. You still don't know how this thing is going to end through the whole thing. So it's not a complete sigh of relief. The only sigh of relief is certainty. This is what we're going to have to pay to make this go away. What about future lawsuits, though? What about all these copycat lawsuits that are coming out? And also, they made it a national case with a bunch of different defendants, of course, except for NAR, but how is Remax in Anywhere going to get tied into some of these copycat lawsuits? And are they going to settle those before all the, there's so much uncertainty. Well, what's happening here, right? But for the moment, they have a sigh of relief. The two corporate brokerages have received preliminary approval of their settlement agreements in the Citroen Burnett, the Morrill, and the Nosellic Commission lawsuit. So all three of these cases have been settled simultaneously. The judge who oversaw the Citroen Burnett trial last month in Missouri's Western District granted the settlements preliminary approval Monday morning. The courts find that the proposed settlements with Anywhere in Remax as set forth in the settlement agreements are fair, reasonable, and adequate. The judge wrote in his motion granting preliminary approval, the terms of the settlement agreements require Remax and Anywhere to pay $55 million and $83.5 million respectively, as well as make policy and practice changes. Some of these provisions include no longer requiring agents to be members of the National Association of Realtors or following the National Association of Realtors Code of Ethics or the MLS Handbook. These changes outlined in the settlement agreements include what the firms will require or encourage agents to make it clear to clients that commissions are negotiable, that agents will have the freedom to set or negotiate commissions as they see fit, and that agents will not be required to make offers of compensation or accept offers of compensation from cooperating brokers. So they're basically saying, hey, we're going to make it clear, we're going to encourage agents to make it clear to clients that we can negotiate commissions, that the agents have freedom to set or negotiate commissions however they want, and that we will not require our agents to make offers of compensation for the buyer brokers on listings or accept offers from compensation. We're going to basically step out of that and let that lie in the hands of the agents to make those decisions. We're pleased with the course decision to grant preliminary approval of the settlement. This is Nick Bailey, the president and CEO of Remax. This development signifies progress in our ongoing efforts and commitment to a resolution. It's a positive step forward in bringing these cases closer to the finish line. In an email, a Remax spokesperson also noted that the firms anticipates the final approval of the settlement sometime next year. So they got preliminary approval, but now they have to actually set up a final approval hearing. This is just preliminary approval. At Anywhere, the CEO and president, Ryan Snyder, was equally pleased. Our efforts to resolve these claims remove future uncertainty and legal expense for Anywhere. Our franchisees and affiliate agents, as we, as together, we focus on serving home buyers and sellers as they move to what's next. According to the order, the parties must contact the court prior to December 22, which is about a month, to schedule a final approval hearing. So that hearing for final approval is going to be next year. While the judge's motion answers one of the questions left by the jury verdict in the Citzer Burnett trial, much remains unknown. It's yet to be seen if the Department of Justice will try to get involved in Citzer Burnett as it is already done with the Nosellic lawsuit. Many analysts and observers expect the DOJ to get involved at some point. Let's talk about the judge ruling on the Citzer Burnett trial and when that could happen and what the actual timeline is. The Injunction and Citzer Burnett Commission lawsuit not expected until spring 2024. So we won't know until next spring, April or May, what the final outcome is going to be of the Citzer Burnett trial. We have new court filings outlining timeline for post trial motions and when a final judgment might come down. Let's go through this timeline because this tells you exactly when all this is going to come to be. As the dust settles from the Citzer Burnett trial and the jury verdict, many unanswered questions remain and based on the judge's last filing, it doesn't appear the real estate industry will have definitive answers anytime soon. According to a court order file Tuesday, any post trial motions the defendants wish to file need to be submitted by January 8th. So post trial motions from the defendants need to be filed by January 8th. From there, the plaintiffs have until February 26th to file oppositions to the defendant's motions and then the defendants have until March 18th to file their responses. So the defendants who lost have until January 8th to post their post trial motions. Then the plaintiffs from January 8th have until February 26th to file their oppositions to that. And then once that happens, the defendants have just three weeks, it looks like, until March 18th to file their responses to the plaintiff's oppositions. After all the post trial motions are resolved, there will be a 30-day waiting period until a judgment is executed. In other words, the real estate industry may be waiting until April or even May of 2024 to find out what the injunctive relief of the commission lawsuit will be. While some experts, industry experts have speculated the injunction will simply call for an end of the National Association Realtor's clear cooperation policy. Others believe it may demand the end of any cooperation compensation practices by barring listing agents from paying or offering to pay buyer's agents. Despite a lengthy timeline, defendant Kelle Williams remains optimistic. We have strong grounds for appeal and are focused on next steps, a spokesman from Kelle Williams said. In the meantime, the industry is facing growing pile of copycat lawsuits and continued uncertainty surrounding the Department of Justice's investigation into NAR. All right, so we've got a long way to go here before we get some answers of what the final judgment is going to be on this case. And like everybody said from day one, this thing is going to get appealed out for an incredibly long time. Well, that judge comes down with the injunction. If that injunction in fact creates a situation where they bar listing agents from even offering buyer-agent commissions, NMLS, then that could completely change what we do. Not really what we do, but how we do it. Let's just say it like that. And we'll go from sellers paying the buyer's agents directly. I said it, sellers paying the buyer agents directly. Everybody thinks the buyer's going to pay the buyer-agent commission, but that's not going to be the case. You're going to have the sellers paying it still because the buyers are going to ask for it in the contracts and there will be agents involved. Otherwise, there's going to be tons of lawsuits. Now the the defendants are beefing up for this appeal. NAR and KW gear up for legal uh, gear up their legal ranks for the Cicero Burnett appeal. All right, listen to who they got here. The defendants in the landmark Cicero Burnett trial who vowed a long legal fight after the guilty verdict are delivering adding top legal brass to their benches. Kelly Williams tapped Paul Clement to be part of the franchisor's appeal team. Clement served as a U.S. Solicitor General, the fourth highest post in the Department of Justice. So they got somebody who used to work directly on the Department of Justice from 2005 to 2008. He's an appeal attorney and in private practice who has argued more than a hundred cases before the Supreme Court. Okay, so this is this is a big, big guy. He used to work for the Department of Justice and he's also as a private uh, as attorney in private practice, he's argued more than a hundred cases before the Supreme Court, which is where this is going. The National Association Realtors also brought in a former U.S. Solicitor General with experience working under Clement. So they brought in the guy that used to work under Clement at Department of Justice and that's Gregory Garry. Garry succeeded Clement as Solicitor General and has argued dozens of cases before the Supreme Court. So this is actually someone who worked under Clement at the Department of Justice. He succeeded Clement at the Department of Justice as a Solicitor General and he's argued dozens of cases before the Supreme Court. He's actually defending Purdue Pharma's bankruptcy settlement, you know, I guess right now. Other defendants are also adding appeal lawyers to the ranks, including some with proposed settlements that have yet to be approved. So we're already getting into the settlement game here. But anyway, this was, this is just a quick update for you on, you know, the cases, where we are on the approval, the timeline of when we're going to possibly get to an answer of an injunction. It's going to be, you know, April or May. So I'm going to continue to cover this for you. Let me know if you have any questions whatsoever. I read every single comment. I'm doing my 2024 business planning session next Friday at 4 p.m. Eastern. I'll put a link in the description for that. And let me know if you guys are absolutely getting after it. Let's go.


Shakira turned heads at this year's Super Bowl with her performance to say the least, and the mega mansions she calls home are definitely headturners too. Unlike most celebrities who reside in Hollywood, Shakira and her family spend most of their time in Spain. At the moment Shakira lives in a beautiful mansion in Barcelona and it's reported she actually has two there. In this video we'll take a look at those impressive properties, her fancy car collection and more. Apparently the start even owns a whole island in the Bahamas. Shakira, which you should know is her real first name, is a singer, songwriter, dancer and more who hails from Colombia. Although she made her recording debut at the young age of 13, she didn't find success in the US until her album Laundry Service was released in 2001. It sold over 13 million copies and had two number one singles. From there Shakira had success with both English and Spanish albums. Now she sold more than 80 million records worldwide and is one of the world's best selling music artists. Shakira received a ton of awards over her career including multiple Grammys, Latin Grammy awards and more. She also was a star on the Hollywood Walk of Fame and has been ranked the most streamed Latin artist on Spotify. At the time of this recording Shakira is 43 years old and let's not forget among her many accomplishments she's also the mother of two. As you probably know she's married to the professional soccer player Gerard Pique who plays for Barcelona. Makes sense why the couple would live there. It also makes sense that Shakira and her man can afford more than one mega mansion. Her estimated net worth alone is a massive 300 million dollars. Hey guys it's Kara and today we're doing another house tour here on Famous Entertainment. Today we're going to take a look at where Shakira calls home including her properties in Barcelona and much more. If you like these videos make sure you're subscribed and ring that bell to be notified when we post so you can always be up to date. We've also done house tours on the likes of Jennifer Lopez and Zendaya and we'll have links to those at the end of this. Follow me on Instagram because I love connecting with you guys. I've also been reading all your comments and I'm going to be responding to some at the end of this. I need you to let me know who to do next in the comments down below and whose home you'd like to see. Now let's get into this video. Before I show you where Shakira lives in Barcelona let's take a look at the beach mansion she had in Miami. Way back in 2001 Shakira bought this mansion for 3.38 million. In 2013 it was available for a rent and then a couple years ago it went up on the market for sale. This time for over 11.6 mil. Shakira's mansion was located in the prestigious North Bay Road neighborhood of Miami Beach. Of course this multi-million dollar estate was hidden in privacy thanks to the stainless steel gates surrounding it. Once you get past security you're greeted by a wide driveway lined with hedges and a two car garage. The motor court was certainly big enough to house Shakira's fancy cars and more. Since the original purchase Shakira's added plenty of improvements to the home including new hardwood from Spain, custom designed furniture, landscaping and new lighting systems all over. The Miami mansion spans almost 9,000 square feet and has six beds and seven baths. The design inside is very airy and fresh suitable for Miami and the year round summer weather. Lots of whites and natural light thanks to many windows and doors. Double front doors open up to the main floor where there's a large open concept living and dining area. Moving through Shakira's house you'll find it's pretty minimalistic. There's a sleek kitchen that has a large island with a built-in snack counter and a built-in dining table. Behind the kitchen there's an entertainment wing that extends off of the back of the house where you'll find a media area with sound equipment and an additional billiards area. Other features include an office suite with built-in desk area where Shakira used to display her many awards as well as a spacious home fitness center.  One of them sits on top of the entertainment wing and has pink accents in the room, a bathroom with Venetian mirrors and a private roof cover terrace. Moving to the backyard there's a large deck big enough for parties that almost resembles a personal Vegas beach club which of course all surrounds a sparkling pool. There are also sunbeds and loungers all over and a poolside dining cabana with a wet bar. Not to mention hidden behind some palm trees there's a private dog. Shakira stayed in her Miami Beach mansion for quite some time but given that her man's soccer career brought the family to Barcelona she would spend less and less time in Florida. Let's move on to Shakira's main real estate. Her mansion or mansions in Barcelona Spain she shares with her husband and her children. In 2012 it was reported that her and Gerard bought two homes in an upscale neighborhood and turned them into one huge contemporary mansion. Because of this renovation the home also needed more work done inside so it took a while before they could move in. This house was located on a high hill in a quiet spot but still close enough to the city center. Considering the couple's fame they have solid security gates at this mansion and it's quite hidden from the street. Despite that there was a break in back in 2018 where Shakira had a bunch of her watches and jewelry stolen. However the cop said it was done by professional criminals. I'm sure since then Shakira's taken the security up a notch or two. So not only does she have that Barcelona mansion but in 2015 Shakira and her man allegedly added on to their real estate portfolio with a second and even bigger home. The second house costs them around 5 million euros which is about 5.4 million US I think. Anyways it's located in the posh neighborhood of Pedrabes Barcelona which hosts elegant villas and beautiful gardens. Not to mention it has the iconic home stadium of the FC Barcelona team. Well it all makes sense now. Shakira's current mansion spans over 15,000 square feet in us, seven bedrooms, two kitchens and even more bathrooms. The home covers three floors of living space with acres of private gardens surrounding it and although we haven't seen too many photos of the inside you can guess the place is pretty lavish. There's even a home gym Shakira likes to work out in. Safety and privacy was definitely a priority for the couple this time around and they got it. Shakira's family home in Pedrabes is hidden behind a high wall as well as towering trees and landscaping. So much so that the house isn't even visible from the road. Although Shakira's mansion is very secluded you can still see beautiful panoramic views of Barcelona's city from the upper floors completely unobstructed. There's a backyard with a cabana and swimming pool with a waterfall which we can see from aerial photos. Aside from the luxury homes we also have reason to believe Shakira likes to spend her $300 million net worth on cars too. I told you guys I would show you what's in her garage and here it is. It seems like the standout name in Shakira's car collection is Mercedes-Benz. She has a Mercedes-Benz SLK we know the latest versions cost anywhere from 50K to 80K. Sometimes even more. It's reported that hers in particular was bought on her birthday and has a present from her hubby and he spent over 60K on it. Shakira has other Mercedes models too like the SL600 which she was driving before the SLK. An SL550 with a 5.5 liter V8 engine and more. When she wants to feel tall, Shakira drives her G-Class wagon around. Yet another member of the Mercedes-Benz family that the singer owns. Aside from all of the Benzes in their garage she also has a Tesla, a BMW X6, an Audi A7 and an Audi Q7. Or at least these are some of the ones we've seen Shakira cruising around in. Her husband P.K. also likes to share these cars you can assume they have the same taste. I'm sure the couples drive away in garage if their Barcelona mansion is impressive to say the least. Not to mention Shakira has been caught pulling off some pretty questionable parking in the past and needed to ask her man for help. It's all good though. I know I'm a great driver but my parking, not so sure. Now when it comes to her travel, Shakira goes all out too. Also included in the stars real estate holdings is a private island in the Bahamas called Vons K. Back in 2011 she split the investment with Pink Floyd frontman Roger Waters and Spanish singer Alejandro Sands and they all share this piece of paradise. However, according to Wikipedia only Shakira and Mr. Waters own it. Maybe Alejandro was voted off. Shakira's island Vons K is in the Barry Island district about 37 miles from the capital of Nassau. Apparently the stars paid a total of 16 mil to call this 700 acre island theirs. Sources say they planned on building a resort there but I have yet to hear more about that. We do know Shakira and the fam love hanging out in the Bahamas for their family getaways even visiting the sharks. So now we've seen what we can from Shakira's impressive real estate from her Barcelona mega mansion to her previous Miami home and more. Where would you guys like to live if you were her? We also saw Shakira's car collection and it's pretty obvious the woman loves Mercedes Benz. I think if it were up to me I'm more of a Jeep girl but she definitely looks good driving them. Let me know what you guys liked best about Shakira's mansion and cars or if I missed any of her properties. And I think we can all agree that owning a private island in the Bahamas like she does is definitely a fun investment to say the least. Okay guys now I'll read out some comments from previous videos. On our Paris Hilton house I'm surprised to hear that Paris Hilton is only worth an estimated 300 million. I know she has many many businesses investments and she's literally her own brand. I would have thought she had way more money. Simply shocking. I think Paris is quite bright and truly a natural beauty and will continue to thrive and surprise. I totally agree Lisa. I think Paris is a really genius business woman and gorgeous, I'll always love her. Also on our Paris vid Samuel Giles said about her homes my gosh that Manhattan penthouse is easily my favorite sophisticated opulent with the added bonus of a private rooftop luxury personified. Perfect way to describe it Samuel. It is truly a gorgeous penthouse. Nevermind in the heart of the big city. I also got a few comments requesting a post Malone house tour and I'll hopefully have that one for you guys soon. I'd love to see where he lives. All right guys that's all I could find on Shakira's mansions in Barcelona and her mega car collection. What did you guys think about her homes? Did you like them or no? Let me know in the comments as well as what other celebrity houses you'd like to see. Follow me on Instagram if you wanna chat more and I'll see you all in the next video. Bye.