So, fine, we are connected visually, acoustically. Great to have you here, and thanks for having me here to introduce myself. I'm Peter Baumann from Jakobs University in the north of Hamburg. And I'm going to bring to your doors what's called data cubes, but as you will soon see in terms of programming, that simply means arrays, multi-dimensional arrays. We are talking about large ones. We are doing that research since a long time, and one effect has been that our concepts have been brought into standardization of ISO, and the SQL query language is being extended currently. Standard is in the make, that adds arrays to SQL. That actually is a core part of the PhD thesis of Dmitri Amizhev, and he is PhD steward of mine, I'm from Macedonia, and I want to tell you a little bit about what that is going to be, what that standard means, and what we are doing in terms of implementing it. But first, allow me five seconds of advertisement of our master program in data engineering at Jakobs University, which by the way is an all-English program, we are more than 100 nations on campus, so it's all English. So anybody wanting to join us, be very welcome. Somewhere I have an acoustic feedback, and over the talk I will find out where I can place myself so that I still can reach to the laptop while not generating feedback. Okay, I have a database guy. So whenever I see something, I like to think in terms of data structures behind it. If you look at what is to big data today, where the proponents are, I see data structures behind it actually are not so many of them. Interestingly, my hypothesis is, please correct me if you find me wrong, that we have a few core data structures that make up for at least large part of the big data. So if I abstract that away from those randomly picked domains, then I actually find very few data structures that can conflate into sets. You have large numbers of elements that just form sets, animated sets. For this, we have relational databases, we also have no SQL databases, works sort of. We find hierarchies. Okay, hierarchies can be modeled for example in XML, any other kind of things like JSON as well. So we are on the, graphs are something extremely interesting. So like for example, the Facebook graph with a billion nodes and maybe a couple of billion of edges. So doing graph algorithms on that graph, that's an exciting thing. Therefore graph databases are a very important and exciting research area. And then we have a couple of data that happen to be lined up on some grid, gridded data like raster images for example. But also weather forecasts or confocal microscopy images, you name it, there's a lot of them. These are the ones we are focusing on. And incidentally, if you see what that means in practice coming back from the abstraction, then you find that typically this is sensor data, that is image data, image time series, it's simulation output or statistics data cubes. There you go. Data cubes is a common term for it. What in the end is a race. So the standard in a nutshell is going to look like this. It will be part 15 of SQL and it adds capabilities that a column in a table can hold a race, can hold very large a race. We know that already from blobs that they are just byte strings. Now we talk about something that has structure inherent. So I can define the standard stuff like in green and I can add a definition of a scene that is a satellite image that happens to be an array of 5,000 by 5,000 extent and each pixel in this case happens to be seven bands. That is landside seven, that hosts seven bands. Okay, once we have that definition in the database, we can indeed do queries. Now the data dictionary knows about it and so we can do operations like band one minus band two over band one plus band two, let us say the typo in this query. This is something that researchers in remote sensing call the NDVI, the normalized difference vegetation index. That's a very common formula. Now what does it do? It takes the array and applies the operation that you see here on every pixel simultaneously. That is a concept that you find in many languages like MATLAB, like IDL, Python, you name it. That's a common technique and that is now available in a database query. You can use that also in the where clause. If you collapse everything you do on the array into a single Boolean scalar, then you get a true, false, a yes, no and that means you can utilize that for doing filtering on the data. So it's essentially what we do is marrying or injecting image or signal processing language or more generally linear algebra into standard SQL. And that means suddenly a lot of domains can benefit which traditionally have been outside of databases. Who has been maintaining satellite images, I mean the pixels inside a database, not many people, weather data and so on. So it's interesting indeed to do that. On the side, there is another effect we gain because suddenly we are able to have red queries and green queries united in one. What does that mean? Green queries are on metadata or the small, smart descriptive data. Whereas the red query parts are on the data, the big, huge, clumsy data that typically you could only download. So this age old subdivision to data and metadata we can overcome and we get a common information space for users. So this logical modeling already has its advantages. So this is what will become the standard. The status is that we are just entering the second committee draft in ISO and then it goes into draft international standard, blah blah and third quarter of 2017. We believe that the full adoption cycle will be finished and this will be fixed standard. So we have an idea of how to embed a race into databases. What we can do with it, we have seen one class of operations. We can do much more just by way of example, you can do matrix multiplication. Top right, you have the formula mathematically and if you translate that, you can see that the operations that we use here are relatively one to one. So in the red part, we define an array that has an extent from zero M in one dimension to zero to P in the second dimension. We bind iteration variables I and J to it and then we define for each of the elements of this newborn matrix what its contents should be. That contents is given in term of an expression where we in the innermost part find A, I, K multiplied by B, K, J. So we take individual numbers and multiply them like in the matrix formulation topic. Then we need to aggregate the big sigma that is materialized by the condensed plus, so user plus operator to collect everything. And by the way, we have another iteration, the K iteration, which happens to be at the bottom of our big sigma. And so the blue part is an aggregation operation that collects the result of expressions and the red part establishes a new array. Those two together allows to formulate a lot of things in image processing and statistics like for example, convolution operations, you can do rollups in OLAP and other fancy stuff. The second example shows a histogram which is again the same structure. You have the red establishing of a new array which in this case happens to be one dimensional, 256 buckets, and you have the green part which is the aggregation part. Actually, excuse me. If we step back a little bit, the other operations that I showed you before can be expressed via a combination of these two or even just one. In the end, all the operations of this language or this sub-language can be reduced to these two operators. That is not only conceptually beautiful, it also allows us to define optimizations and a few things that can internally make the system faster. Okay. Sorry. So we have a concept. How can we implement it? Our answer to that is called raster men. That fancy name stands for raster data manager. It traces back to a time when I did not dare to call it arrays because people in image processing think of raster images. But in the end, it is exactly the idea. SQL plus n dimensional arrays. Getting a query language that allows us any query, any time on any volume of data cubes. And therefore internally, we have established an architecture that relies on tail, on tail streaming so that we can evaluate queries on objects that are larger than our main memory and that typically are. If you say I have a terabyte main memory, then I say I have a petabyte data cube. So it's always bigger. Okay. That is actually in operational use. If you go to planetserver.eu, this is about Mars, Moon and other planetary objects that is hosting 20 terabytes of data in the raster men community, which is open source and which you can download in source code or as a RPM or as a virtual machine. This is something that is more and more recognized today. It took a long time, actually way more than a decade. But meantime, it's recognized, for example, by the European Space Agency. And actually, I'm sorry. Okay. Right after that, where's the next graveyard? This is something that has let us stumble into standardization into geo services because they were the first ones to offer us big data. Historically, medical data were not that big. Today they are, but not when we started into that. And so we are mainly busy today in the geo sector where we also received a couple of awards and recognitions, but let's go ahead. We wanted to talk about implementation. How can we implement such a language? First, we need to care about the data. Well, obviously a petabyte is too big, even for today's disk, so we need to partition. Typically, what systems of that kind do is they do some homogeneous partition. You cut it into pieces of the same size and via directory lookup, you then easily can spot the tiles or partitions that are affected and so you can load them from disk into main memory for evaluation. We do that in a more general way so that actually you can adjust the partitioning scheme to your application, to your query workload. That is a little bit more involved, but it gives you nice tuning parameters. These tuning parameters actually are offered to the administrator so the user doesn't see it. You saw nothing about partitioning in the previous queries. The administrator, however, gets an extra statement in the insert clause, the tiling clause, that says, I want to use tiling scheme area of interest, and by the way, these are my areas of interest. Dear system, this I want to have as fast as ever possible. For the other regions, do something meaningful, say tile size one million elements. And then we can do a couple of more things and in the end, the system knows how to organize data on disk so that we get it fast. Wait a moment, some people say, that's overkill. This regular partitioning is really okay and it's sufficient. Really? Let's look into OpenStreetMap. They deploy a tiling that looks like the one you see here. It's on vector data, but the issue is the same. If you want to go into New York City, you have a very fine grain access of people, very small boxes that they want to retrieve. Whereas in the Southern Pacific, you can have one big blue tile. So actually, it is worth adjusting to access behavior of users. On the right-hand side, you see a simulation of two neutron stars rotating around each other and you can see nicely the adaptive refinement of the grid. So this way, I would counter, yes, it makes sense to go into that work and implement arbitrary tiling. So much about the data. Now, hooray, once we have partitions, we can nicely parallelize them in that we send each partition to a different node or we put it on a different machine even. That's what we actually are doing and we have done that to the extent that a single query was split over more than 1,000 Amazon nodes by doing the following. We take a query, an incoming query and we look first, where do the data sit? In this made up example, we might have data set A right away. Okay, that's us anyway. Data set B is close by. It is sitting in the cloud on another node on another disk, so it's relatively easy to fetch. But still, we don't want to fetch the data. Rather, we ship a subquery. So this other node can do something for us. It does computation on its data and just sends back data, the results. In this made up example, actually it's just a single integer that comes back. So it really pays off. It pays even more off when we do not talk about a cloud but about a federation of data centers. And that is something that we indeed have working. ECMWF, close to London, wants to federate data with NCI Australia. So we cannot ship data cubes. So rather, we ship queries and get back the results. We can even drive it to the extreme that we have a moving target like a satellite up there. And actually, this is not vision. This is not science fiction. But you see, that's really fun for us. We have been accepted by ESA as an onboard experiment on that satellite, Opsat. And this summer, we will go on board and Rassamann will be an instance there which will be federated and then the satellite becomes a database server that we can trigger with queries. So, yes, there's a lot of fun in it. Okay, the architecture is like that. So we have a central workhorse which is the Rass server process, the Rassamann server process that can put its pixels into a database. This is how we historically started. So it can be PostgreSQL, for example, or any other relational database. At some time, people also wanted to have a version without the database underneath. Excuse me. And so, we developed our own storage manager which is roundabout a factor of two faster because it doesn't have some overhead that PostgreSQL with its functional richness has which we don't use. And so there are two options today to use that. This all is SQLMDA or it's our RassQL. It's syntactically still a little bit different because the ISO folks wanted to have adjustments. But it's the domain agnostic query language. On top of that, we have an application layer that adds geosemantics that implements the OTC standards of web coverage service, web coverage processing service, et cetera. In short, it knows about coordinates and it knows about regular and irregular grids and that kind of things. Okay, so that is the overall architecture. The Rass servers can be multiple processes and that can run on the same or on different machines without a single point of failure. It's not a centralized architecture but in a peer-to-peer style. Good, so far so good. Of course, when I talk about at big data conferences people say, big data, ah, Hadoop. And I say, no, not Hadoop. And they say, why not? Why do you not use Hadoop? It's so fast and scalable. First of all, Hadoop is scalable but not fast. But actually, Hadoop has an important missing property. Hadoop is very good for set-oriented data. Remember my intro, but it does not know about arrays. Now, if I have my red pixel that I want to access it's extremely likely that in the next millisecond I will want to have a blue one. As long as the array is small and fits into your RAM in Hadoop, it can deal with that as well. In the end is Java and Java knows arrays. But hey, we talk about big arrays. Yes, multi-terabyte objects. So you need to shuffle around and this is what Hadoop doesn't know. Consequently, this is shown by benchmarks. So people at George Mason University, University of Madison, and so on, they have done benchmarks where indeed the green one, Hive, was faster than Rustam and when it gets about accessing a single pixel. Hooray, good. But whenever you want to have more than that, actually performance times skyrocket with those systems, whereas our architecture is specialized and is faster. I'm not saying, just to make that very clear, I'm not saying we are better than Hadoop in general. We are just specialized on a very particular niche, on a very particular data structure and therefore we can do better. In other domains like indexing web pages we would fail in a big disaster. We cannot do that. This is where Hadoop can excel. Okay, this whole thing is embedded in, or one of our projects, our flagship project actually, is the Earth Server Initiative where we do exactly that. We take three-dimensional and four-dimensional data cubes and make them accessible for agile analytics. Agile means you can send any query anytime via the query language paradigm. Utilize three-dimensional time series, X, Y, and time. And we have four-dimensional weather data. So we have a step where we find, for example, European Space Agency, Sentinel, Satellite Data at Mio. This is currently exceeding 250 terabyte. We have Plymouth Marine Laboratory. They do ocean color analysis. We have national computational infrastructure in Australia and European Center for Medium Range Weather Forecast in Reading close to London. And now if you think about federating NCI and ECMWF, you have exactly that distribution situation. Okay, so that is what we are doing in this alliance across three continents and now in year five. So yet another year to go and then we see what we find next. Actually, I forgot one. There is Planet Server up there, Mars. I mentioned that earlier. So that is another one. And okay, there we don't want to federate with Earth climate. Okay, admittedly. So this is the geodomain. Actually, we have done experiments in many other domains out of curiosity. So we have looked into the Earth sciences from 1D through 4D. Life sciences is very interesting. Gene expression analysis, for example, looking at the fruit fly embryo, human brain imaging. We are just teaming up again to continue that research and furthers like computational fluid dynamics, et cetera. You see, I have a fable for technical scientific applications and not so much into monetary applications because my own bank account is not big data, so it doesn't pay off. Okay, this was for the technical part. Now let me go to MetaLil and report on a few other. Hey, wait, we have so much time left. I thought I cannot do it, but I can. Fortunately, I can show you a little bit just in case you're interested. Rustamend.org, if you direct your browser to that page, you get that one where you see documentation and you get the source code, as I said. So please feel invited to download. If you want to see the downloads, we count them since some time. You see that there are a few, this is strange, so somebody seems to have many fails in download, so don't count that peak. That spike is for sure not realistic, so realistic, I believe, is this one, having on average about 10 downloads per day, something like that. If you want to play with it, play with it, yes, go to standards.rustamend.com. This actually is a strange URL, you may think, but the main purpose actually is we want to demonstrate the use of GU standards so that we tell GU folks like satellite data centers, hey, it makes sense to offer services based on the standards because they are easy to use and they are scalable and so on. So we have lots of demonstrations for different situations in life, but as we are talking here about the SQL extension, I would like to draw your attention to this one, Rust SQL console. We have some sample data online and you can type a query in here and then you get a result. So I take something that gives us some colors, so blah, blah, blah, select and code and then some query and we retrieve it and we get back the image. So if you want to try out how it feels, be our guest to look at that. In another talk that I've given this morning, I have presented on the Python interface. There are quite a few Jupyter notebooks available now that also allow you to immerse into that, but there's still to come. We have many items on our plate where we have to continue. Let me mention one thing, it's very important to me. I may seem to appear now as evangelizing SQL, as the world speak, intergalactic, definitely not. We database people have learned our lesson. So initially we thought we invent the natural language called structured English query language and maybe not many in this room would find SQL, natural language. So we have dropped that idea, but SQL still is an excellent client server programming paradigm because it allows you just by string manipulation to compose some code and then you ship code to the data. You tell the system what to do. You don't care about the optimization and about distribution because the high level language just ignores that and for this purpose, I believe languages like SQL really have their purpose. But then actually you would want to embed this into your application that ideally has some visuals, some graphical interface and that's what you really want to offer to your users. Okay, so disclaimer, no, this is not the new intergalactic world speak. This actually, if you go into this one, you will find one link at the end, OSTO. So we found it naturally since we started in 2008 to fork the open source version from the pre-existing fully commercial one to join the open source community. And so we found our way into OSTO and I would like to make a few comments on that now on meta level. So we had the opportunity to gain some experience and we found a couple of fantastic folks there but we also found some room for improvement and so what I'm unfolding now is based on number one, I'm OSTO charter member since three years or so. We have done the incubation procedure and as I said, we are engaged in open source software since about eight, nine years now. So the one thing is the maturity. I believe also for open source, you need some coherent management of it. So for example, that was not my complaint but of somebody else last year, there's no inventory of decisions taken. How do you know what has been resolved earlier and that your decision today is not countering another decision earlier and maybe you can base your decision on past wisdom? Also management, incubation management, we took six and a half years. Pi WPS actually took eight years. If you take into account does OSTO is only 10 years old? So that is taking up quite some time. At some time I suggested, hey, OSTO folks, make it simple. Just apply your incubation criteria to yourself first but there was not much response to that. No, that doesn't fit, we don't need that and something else I observe unfortunately is that typically insiders recommend each other when it comes to elections. For example, into the OSTO charter membership. The next thing is that actually it's good to have a broad focus but sometimes it can be too broad. We want to brand good projects in the sense it's good software that we can do use but we should not conquer the project and we should not impose particular models. So I don't like very much designed by committee. If somebody has a great idea and is willing to invest into that and implement it, let this guy do it and let's see what comes out. Do not take away a project and lose maybe this expert. The next thing is and this is my most serious thing which worries me most.  So they say all software should be free. Well, I'm very much in favor of free software. We are doing that ourselves with considerable effort. Why should all software be free? So we have the extreme commercial model. I put it like S3, so the market leader in GIS and then you have OSTO on the other extreme and only this is what they want to look at. What about the ones in between? We are losing so many. We could join up forces going up to here and then we have a much larger basis to counter the purely commercial parts if you want that. And by the way, these ones in the middle are very often the small and medium sized enterprises. So whom do you really hit if you hit everything right of OSTO? You mostly hit the small companies, not the large ones. They don't care so much about the small ones. Suffer real disadvantage. And this is where I say, let us have a more inclusive approach. I believe it's very important to have this open source idea prosper and we try to contribute our humble part to it. Let us think about all possible models that we can accommodate and not be artificially exclusive. Okay, so that was my kind of provocation of the day. Let me wrap up. So, a race is something coming back to technical level. That I believe is important. It's important for many, many applications that in the past have been ignored. I can show you papers that write. We take a pixel matrix and put it into an unstructured blob in the database. And consequently, you can do nothing with it. You can just deliver the image. And this is not what we want in the days of big data. So actually, it's good to have that support in some way and in the clear modernization. Not have another silo, but have a clear interface. And that interface is going to be SQLMDA. So remains the question, what's above that? Let's do spicy clients. This is not so much our game. We are on the back end. So at the lower end, how can we have efficient implementations that are scalable, that have high performance and that are easy to handle for the application programmers, not for the end users once again? With Rustam and Community, we try to make a humble contribution to that driven by a database perspective, driven by the query language perspective that has led us stumble into the standardization, as I said. And today I'm the editor of the OTC DataCube Standards. So web coverage service, web coverage processing service. And in ISO, likewise, we have more activities on this running. And as we have a little bit time left, normally I always take too much time. So I said to myself, Peter, be careful, not too many slides. And now I'm in the comfortable situation that I can detail a little bit. Actually, there is quite a few activities. I talked to you about ISO SQLMDA, but ISO also is interested in adopting the OTC standard of coverages, which is DataCubes in the end, DataCubes plus more. And in Inspire, we find the same thing. For those of you who don't know Inspire, this is the European legal framework for a common spatial data infrastructure. This sounds impressive and actually it's a huge work because we want no less than all the institutions in the European community that offer geodata should do it in a compatible way. That is a real huge project. It's not just mapping agencies at the top level, it's down to the level of municipalities. And they all should adjust to open standards. And for the coverage part, so author images, elevation data, and that kind of things, coverages are to be used. The good thing is now that all these converge. So we don't have competing standards on the same thing, but we have a notion of coverage that carries over the different organizations. So you know that just will be interoperable if you follow these standards. Okay, coming back, help, now I'm lost. This was it, okay. So we have interfaces that can be used. We look for scalable implementations. I mentioned on the fly, on political level, so to say my observation is I believe OSG specifically, I generalized it here, forgive me, needs to be more inclusive in terms of business models.  And as opposed to the single images that we have the satellite images, one cube I would contend says more than just a million images. It's just more tractable. And this way we hope to contribute to the big data tillage. And this way I would like to finalize for now. And thank you very much for bearing with me. Can you hear? I think any questions please. And if you do, we'll come running to you with the microphones so we can be heard on the recording. That's great, thank you very much. Is there a timeline for this to make it into post-GIS? So the support for arrays, is it going to be adopted in post-GIS? Or is it already there? I cannot speak for the post-GIS people. Actually, at the time they started with post-GIS roster, I told them, hey, let us team up, that would be a nice endeavor. But they said, no, we want to do our own thing. We had public discussion on that. And now they have developed their own way of doing that. And they would need to adjust. That of course would mean some effort and so I cannot speak for them. I know that they are, excuse me. I know they are aware of it. We had some exchange on it. But is there somebody here from the post-GIS community? Could you maybe respond better than I can? Okay. It seems more logical to do it this way. I would agree. So I don't know. Be great if it makes its way. Of course, I would love to see that because that allows us not only to be interchangeable in the sense of vendor login, but it also allows to federate different systems so we can establish the system of systems idea. I would love that. I'm open to collaborate. Okay, great, thank you. Thank you. You mentioned about querying combining metadata and data. Can you tell us a little bit more about that? Yes, definitely so. It's actually, we can just pull up the example again and see the color schemes here. So the green part is metadata symbolically here. Of course, you have many more metadata normally, but it's metadata which have the characteristic that it's small attributes. It's the classical attributes of numbers, strings, and so on and dates in this case. So this is what you define in the table and you define also that you have the data attribute hosting the pixels. So conceptually, it's in the same table. Physically, of course not. You would not want to do that. It would devastate your performance. So the system internally actually must be clever enough to split that. This is actually what we are doing with mediator technologies that we split such a query as we have them below and we find components that go into the classical relational part and are solved there and others that go into the array database part and they're solved there and then we reunite. That goes well unless we have a mixed join. If we have a join that involves both data and metadata, then it gets spicy and that is a current research area for us how to optimize that. That's a difficult part. By the way, we are doing that not only for relational stuff in collaboration with Athena research, we do the same thing on X query and arrays. And in collaboration with Uppsala University, Tore Rish, we do the same thing with RDF data, with Sparkle, what's called polydors today. We want to inject arrays into other data models and see how well this can work. Does that sort of answer your question? Yeah. Okay. Excellent. Thank you. I guess we're done then. Good. Thank you again. Thank you very much. And enjoy Boston.


that Minneapolis is now a Muslim city. It's a little strange to think, given Minnesota's traditional demographic makeup as ethnically whiter than the driven snow and religiously polite. Again, classical Western brainwashing. Don't you see Minnesota's wider snow? Genius, we're not talking about race. We're talking about religion. And Islam is a religion for everyone. You're welcome as well to accept Islam, believe it or not. Islam came for the world. You don't have to be brown to be a Muslim.


Income tax 2021-2022. Payments. Get ready to get refunds to the max diving into Income Tax 2021-2022. Most of this information can be found in the Form 1040 Instructions Tax Year 2021 on the IRS website at irs.gov, irs.gov, Income Tax Formula. We're focused down here on the payments area. Quick recap of the formula. Everything down to this taxable income. You can imagine basically kind of like a modified income statement where we have income minus different types of deductions. Then we get to the taxable income. Then we apply the tax calculation, which is confusing because it's not a flat tax, but we finally get to the tax and you would think then that you would be comparing the tax then to what we paid because we have to pay during the year, which would be the withholdings typically for a W-2 employee to get to the amount that you would still owe or the amount that that you would get refunded to you at that point. But we have a little bit more complexity down here, including possibly other taxes that we have to include other than the federal income tax, one of them being, for example, the self-employment tax. And then we have the credits as well, which we'll talk about later, noting that the credits are beneficial. So people often group them up with deductions because they're kind of good things for taxes. But if you had a dollar credit versus a dollar deduction, the credit would be better because you're getting that full dollar worth of benefit, whereas the deduction would be reducing the taxable income, which would lower the amount of tax in that way, giving less of a benefit. So from the credits then act in a similar way as the actual payments because you're getting the same amount of benefit as if you actually paid the government or if they gave you that dollar of credit, a dollar payment and a dollar credit is in essence the same amount. But then the credits get a little bit confusing because we have the refundable credits and the non-refundable credits, and that means that if it's non-refundable, then we're not going to get that dollar credit if it goes or takes the tax liability below zero. The government's not going to pay us, in other words, for that amount. But if it is refundable, then they will pay us that amount. It would be just like we kind of overpaid the government in essence. So that's why we have to group those a little bit separately. So when we get down to the payments down below, it gets a little bit complex because there's multiple ways that we can make the payments. And because the payments and the credits kind of get lumped together because they have a similar function within the income tax formula, realize that if in a perfect world, if the income tax code was set up the way it's kind of designed to be set up, and if it wasn't so complex, then it would be kind of similar to the payroll taxes, meaning the filing of the form 1040 should just be an information return, not triggering either more tax or a refund if everything went smoothly. In other words, during the year, we have to pay the government either through the withholdings that we have or through actual payments that we make, for example. And then at the end of the year or by April 15th or April 18th following the year, in this case, April 15th or 18th of the 2022, we would file the form 1040 and this would just be a recap recalculating what we owe to the government and what we had already paid in a similar way as like a 941 does for payroll taxes.  The reason we cannot do that with the taxes is because the tax code is way too complicated because we have a progressive tax system, because we have multiple things that even deviate from the progressive tax system to calculate the tax, because we have multiple income kind of areas because we got these different types of deductions. Therefore, we have to shoot basically for an overpayment when we calculate our payments or withholdings or our estimated payments so that we can avoid the penalties and interest. That's why we shoot for a refund. So down here, we're looking at the payments that we already made, which we had to estimate in some way because we have no idea actually exactly how much we owe. So we had to use some kind of estimate. Hopefully we did an estimate so that we can get a refund because we want a refund per se, because I would rather have my money during the year personally, but because I want to avoid penalties and interest. Now, this is the actual form 1040. This is page two. We're focused here starting on line 25. Now, again, note that the second half you would think would be a lot shorter than the first half. You would think that calculating the taxable income would be the complex part of the return, and then the second half would just be the payments that you made. But again, there's different forms of payment that you can make, and the credits get involved down here that keep kind of commingled, which makes it a bit complicated. So on line 25, we've got the federal income tax withheld during our working years. For most people, that's going to be represented by the W-2 income, and the employer is responsible for making those withholdings based on our W-4 and taking them from us, paying them to the government. They report them to us and the government on the W-2. And then we've got the form 1099, which is more common that we might have withholdings on the 1099s if we're in retirement. For example, with a 1099R from pension plans in IRS, for example, and retirement plans. And so that might be the way we have withholdings. We could have other withholdings. Then we would be adding up the withholdings. Line 26, 2021 estimated tax payments and amount applied to 2020 return. These are the estimated tax payments that we might make, which is common if, for example, we have a Schedule C business where we don't have withholdings from the W-2 and we have to actually make payments. So that would be in a Schedule C or possibly in a retirement situation. If you're not having the capacity to withhold from the 1099Rs, you might just have to actually write them a check. And then it gets a little complex because now we've got the credits that kind of are down here co-mingled up with these amounts of the payments, which we'll talk about the credits later. But you've got the earned income tax credits. You've got the non-taxable compact pay election, the prior year credit. We've got the refundable child tax credit. So now we've got the refundable portion of the credit, American Opportunity Credit. These are credits related to the education, which we'll talk about later. Recovery rebate credit with the stimulus payment kind of credit amounts from Schedule 3. And then we're going to add those up and notice here it says total other payments and refundable credits. So again, the reason I'm going into the credits here is because they kind of co-mingled those things together because they have a similar impact as the payments do with regard to just the form or calculation. But we're focused here on the payments at this time. So line 25, federal income tax withheld. Line 25A forms W2. Add the amounts shown on federal income tax withheld on your forms W2. Enter the total on line 25A. The amount withheld should be shown in box 2 form W2. So that's the most straightforward one. The W2 will typically have, of course, your income line item on line 1. And then the withholdings you will have on line 2. The withholdings then second page of the form 1040 line 25A.  Line 25B forms 1099 include on line 25B any federal income tax withheld on your forms 1099R. So 1099R typically in retirement likely to have 1099Rs for IRAs. For example, retirement plans, 401Ks and whatnot, pensions. And so those would likely have withholdings reported on them in a similar way as the W2s, most likely for people in retirement. The amount withheld should be shown on in box 4 there. Attach your form 1099R to the front of your return if federal income tax was withheld. If you're doing it electronically, then of course you would be electronically filing and not, you know, stapling. If you received a 2021 form 1099 showing federal income tax withheld on dividends, taxable or tax-exempt interest income, unemployment compensation, social security benefits, railroad retirement benefits or other income, you received include the amount withheld in the total on line 25B. And then we have this should be shown in box 4 of form 1099. Box 6 of form SSA for your social security 1099 if you had withholdings there or box 10 of form RRB 1099. So then we have line 25C. Other forms include on line 25C any federal income tax withheld on your forms W2G. I believe that was the gambling winnings because if you had large gambling winnings from a casino, for example, and they go over a certain threshold, the casino might be required to make withholdings or possibly you want them to make withholdings possibly because that would be, you know, make your tax situation beneficial. So the amount withheld should be shown in box 4. Attach forms W2G to the front of your return if the federal income tax was withheld. Notice that you can imagine situations where people make winnings on like like like they won a car or something like that. And you can imagine if you won $100,000 car and you don't have any any actual wages that year, you don't have much money, then you wouldn't even be able to pay tax on the car, right? So when you get because you would have income on the winnings and possibly not even have the money to pay the tax on the winnings, right? So sometimes the government might require then if you make over a certain amount of money on the winnings that they withhold it for it, or it might be beneficial to withhold it because then of course you don't get this big jump on your winnings with regards to your income tax if you had some large winning that you had. If you had additional Medicare, Medicare tax withheld include the amount shown on form 8959, line 24, and the total on line 25C attached form 8959 include on line 25C any federal income tax withheld that is shown on schedule K1. So a schedule K1 is like a flow through that you might have from an S corporation, a flow through entity or a partnership. And if you had withholdings on it, then you might report that then or you would report it on 25C. So also include on line 25C any any tax withheld that is shown on form 1042S form 8805 or form 8288A. You should attach the form to your return to claim a credit for the withholding line 26 2021 estimated tax payment. So now we're on the estimates. Enter any estimated federal income tax payment you made for 2021. So if you're not able to cover your payments with the withholdings in some way, then you might actually just have to write the IRS a check or give them some kind of electronic transfer. So include any overpayment that you applied to your 2021 estimated tax from your 2020 return. So in 2020 or in the prior year, if you had a refund coming to you, you might then have applied it as part of your estimated payments to the following year. That of course needs to be included as a form of payment as well. So we have that. And then if you and your spouse paid joint estimated tax but are not filing a separate income tax returns, you can divide the amount paid in any way you choose as long as you both agree. If you can't agree, you must divide the payments in the proportion each spouse's individual tax has shown on your separate returns for 2021. For more information there, you can see publication 505. Be sure to show both social security numbers in the space provided on the separate returns. So clearly if we have married filing separate, then it gets a look. You could have some complications with the report in there if you want to dive into that more detail publication 505. If you or your spouse paid separate estimate attacks, but you are now filing a joint return at the amounts you each paid together. So follow these instructions, even if your spouse died in 2021 or in 2022 before filing a 2021 return. So then we have line 26 divorced taxpayers. If you got divorced in 2021 and you made joint estimated tax payments with your former spouse, enter your former spouse or social security number in the space provided on the front to form 1040 or 1040 SR. If you were divorced and remarried in 2021, enter your present spouse's social security number in the space provided on the front of form 1040 or 1040 SR. Also on the dotted line next to line 26, enter your former spouse's social security number followed by DIV. Name change. If you changed your name and you made estimated tax payments using your former name, attach a statement to the front of form 1040 or 1040 SR. That explains all the payments you and your spouse made in 2021 and the names, social security numbers under which you made them. So if you change your name, then obviously the IRS is going to get confused. So you got to, you got to, you know, make, you know, going to have to work with the IRS to make sure that they've got all the payments that are applied to the proper name and social security number.


here from Layton Sports Cards and I am ripping here from the national 2021 Panini Flawless Football Hobby 2 box full case break. Oh wait, no, this is not flawless. I apologize. You're the wrong one. It's been a long day. 2021 Panini National Treasures Football Hobby 4 box full case break number 123 random teams. Again, here's the list of the random results. You can always find these at LaytonSportsGards.com underneath the results tab and the link is also in chat. Would you pull AC? Yeah, Mike, they were just talking about that train. All right. Here we go. Good luck, everybody. Let's see another big quarterback true RPA. That was sick. He just killed it last break. Wow. One of one from a silver pack. That's crazy. That's a huge hit. You got to send that to get graded, AC. Box one. I love the autos on those cards on the silver pack too. I just look so nice. Redemption on the back, everybody. Here we go. We got to 25 Jill Mixon for the Bengals. I bet. AC said he hit the one-on-one Franco out of series two at the silver pack. Who did? AC. Damn. What? Yeah, to 55. James Harrison on the Super Bowl Immortality. I'd say that's a pretty big card. Yeah. Colossal patch to 99 T. Higgins, Bengals, to 99 Tremendous Treasures. Hunter Renfro for the Raiders. Cowboys to 99. Dak Prescott for the Boys. 97 and 99. We've got Dual Rookie Patch. Rhonda Elmort to 25. Cardinals. Rookie Otto out of 49. John Bates for the Commanders. So in the series two Jumbos, there's like two packs that have silver cards. They look kind of like mojos, you know, if you know what a mojo looks like. They're just like a little bonus pack. Yeah, they're like jumbo exclusives. Yeah. We've got Crossover, RPA out of 30, little Chubba Hubba here for the Panthers. Nice Chubba Hubba. Panthers on that one. Bernie S with the Panthers. Bernie. We've got to 25 Chad Johnson, Bengals, little Ocho Cinco. And we've got Rookie Patch autographs. Purple card number 159. Justin Fields. Bones. There you go. Bears. Kyle S. Purples to 49. I believe. Nice. Big field hit there. Wow. Oh, Mag, that redemption for ya. Ann, Ann, T is hot. Carry that. She really did. Again, all props go to 14. That means 14 gets a raise, I think. Yeah, right. All right, box two. Honestly, after this week. Yeah, everything you all have dealt with in the past 40 hours have been here. Box two. We've got out of five. Aaron Rodgers on the Emerald. Here you go, Packers. Or sorry, that's four out of five, not two. Four out of five. Treasured moments out of 99. DK Metcalf. Seahawks. Fame fabric out of 99. T Higgins for the Bengals. Air Looms. Three-color patch to 10. Clinton Portis for the Redskins, aka Washington Football Team, aka Commanders. To 10. Wow, look at these patches. Two of five. Jewel Rookie Patch. Kenneth Gainwell. Those are nasty. Four-colored. Four-colored on the very cool part of the eagle there, I believe on the bottom one. Wow. From a gas station. That's crazy fire. Out of 25, Tray Lance Jumbo Patch. Niners still staying hot. Bernie S with the Niners. Let's go around. 425. Got RPA to 49. Chargers. Josh Palmer. Little big poem. Little big poem. Chiefs auto to 99. The Cheetah himself. Tyreek Hill with apparently the most accurate quarterback in the NFL thrown to him now. See about that. Nice one here. Six-time first team all-pro to 49. The Lion King. Barry Sanders. Boom. Lions with that one. Orin W. 18 of 49. Nice, Barry Sanders. And last one of this box. We've got Crossover RPA to 38. Ramon Dre Stevenson. Nice patch there. Four-colored patch.  You can see. Nice. Empty. All right, box. Street. Who did the Royals get? Who did the Royals get? I don't know. I just saw Rookie straight up for Ben and Sandy. I don't know if it's been announced yet. Gotcha. Oh, that one's pretty thick. Let's see. We got a thickie here. Of course. See there. Bye. Have a good night. Got out of 25. Chase Young. Washington. Otis Anderson to 55 on the Super Bowl Immortality. Colossal patch. Ravens. JK Dobbins. Limited to rookie. Wow. He's got some good luck. Out of 25. Class of 2014. Hall of Fame. Michael Strahan for the Giants. Giants there. We've got another Treasures of the Hall. This time. Class of 2012 to 25. Curtis Martin for the Jets. Filled Gear Quad Materials. Look at this one. Shrey Lance. Steve Young. Drew Bledsoe and Mack Jones. Old school and new school. Niners is Bernie S. and Patriots is Austin B. We'll random this at the end. Good luck to both of you, fellas.  We've got 2-11. Kellyn Mond RPA. It's what's these low number RPAs? That's the Jersey number parallel. Oh, that's right. That is right. Nice when they're Vikings 2-11. We'll mag that. And we've got another RPA here to 15. Ron Del Moore. Zona. Look at this next one. Nice. What a case. These NT cases have been crazy. This one is sick. It's a 99. It's going to go to the Vikings. Brock Lesnar Otto. Boom. Minnesota. James A. That is nasty. We've pulled a few of these now, I believe. 53 out of 99. Congrats on that one. Vikings will mag that. And we've got Rookie Jumbo Prime Signatures booklet to 99. Not sure. Harris. There you go. Steelers. Nice patches on that, too. Steelers. Who's got my Steelers? Peter M. With the nausea. Very nice. Getting 34 out of 99 on that. All right. Last box. Come on. Big RPA quarterback ink. Let's see something. I guess RPA would have ink on it, huh? Maybe. Here we go. Here we go. Here we go. Here we go. Now, redemption on the bottom. Starting with the redemption. Ending with the redemptions. 99. Matt Stafford for the Rams. We've got 9 of 10. DJ Sharp Jr. Jax. Oh, look at this patch. This is cool. So 49. Jarvis Landry for the Browns. That's got to be game used. Yeah. Game worn. He did grass stains on that one. Cool hit, Browns. We've got Jumbo Tremendous Treasures patch for the Broncos. 7 of 10. Melvin Gordon, the third. Nice three-colored patch there. Century patch to 99. Dolphins. Two attack by Loa. Dolphins. Dual Relic. Rookie. Patches to 49. Eli Mitchell. All the above. Brigitte Otto to 49. Saints. Pete Werner. Saints. Got true RPA to 99. Niners. Trace Sermon. There you go. Niners with another RPA here. Bernie S with the Niners. Nice Sermon RPA. Patch Otto to 25. Chiefs. How about a Tony Gonzalez? That's nasty. Chiefs on that one. Bernie S. Back to back hits. There you go, Bernie. Arguably, I'd probably say the best. Top three at least, if not the best. Top two, I think at worst. Yeah. Oh, yeah. We won there. 18 to 25. Blue patch on that too. Player one. And we've got, last but not least, Rookie Jumbo Prime Signature booklet. Card number seven, Jamar Chase. Boom. Spangles on the redemption. Jeffrey S. Nice Chase booklet. What's up, Caitlin? Nice one there. We'll mag that. Give me just a second. We do have one random to do. Again, old school versus new school here. Niners and Pats. Very cool. Give me one second. Have a good night, Matt. Quad, Relic. We had 49ers. Again, whatever team is on top after the random, we'll take home this quad card. Again, Sri Lansen Young for the Niners, Bledsoe and Jones for the Patriots. All right. Here we go. Two snake eyes. That is a reroll. Two snake eyes. That is a reroll. All right. Nine times. Nine times. Good luck to the Niners and Pats. One, two, three, four, five, six, seven, eight, nine. I'm going to go to the Patriots. Patriots getting that quad. What do you think about it, Caitlin? I went and saw it on Thursday with my fiance. I gave it like a six out of 10. I really didn't think it was all that. You know, there was a lot of hype behind that movie, but again, this is going to the Patriots on the quad. Eli Mitchell to Melvin Gordon, Jarvis Landry, Curtis Martin, J.K. Dobbins, Michael Strahan, Trilance, T. Higgins, Clinton Portis, Dak Prescott. We've got Hunter Renfrow, T. Higgins, Rondell Moore, Tony Gonzalez, Pete Werner, Rondell Moore, Ramon J. Stevenson, Bernie Sanders, John Bates, Tyree Hill, Chuba Hubbard, Chad Johnson. Armand cards. We had the Najee booklet to 99. Josh Palmer to 49. Kellan Maughan out of 11. Trey Sermon. Bangles redemption. Jamar Chase on the booklet. We had Brock Lesnar to 99. Justin Fields, RPA out of 49 for the Bears. And then the 6-2-5 Kenneth Gainwell dual patch. All right, everyone, that'll do it for NT. And me for the night. Forrest is going to hop in and rip some flawless. Thank you, everybody.


Honorable ministers, deputy ministers, here present, Mr. Sorin Dendenau, President of Ancom and Chairman of GSR 18, CEOs and Chairman of Regulatory Authorities, Representative of ITU, the sector members, Mr. Ulin Dendenau, Secretary General of ITU, Mr. Malcolm Johnson, Deputy Secretary General of ITU, Francois Rancy, Director of Radio Communication Bureau, Distinguished participants, ladies and gentlemen, good afternoon to all of you, good afternoon. It is indeed my great pleasure to welcome you to this very exciting global symposium for regulators. It is good to remember that it is within the auspices of the ITU that the liberalization of the telecommunications sector was launched in the years me 90s, around 92, 96. And I still remember the green book for Africa, the blue book for the Americas and the white book for the Arab states that relaunched the liberalization of the telecommunications sector at that time. Elaboralization, of course, brought competition and established regulatory bodies. The global symposium for regulators was established in the year 2000 as a forum for experience sharing with the aim of strengthening and stabilizing regulatory frameworks across the world. 18 years later, things have changed. And a lot of change is to come. We are now in the era of digital transformation that is fundamentally changing our societies and our economies and also improving service delivery to many other sectors we are not doing before. And as we are back in using emerging technologies such as Internet of Things, artificial intelligence for accelerating the attainment of the sustainable development goals, GSR is as relevant as it was when it was created. The new challenges together with a huge, I say huge opportunities linked with them require coordination and sharing best practices. Innovative policy and regulatory measures are needed to respond to the changing landscape and to address the need for affordable and secure access and use of digital services. As a regulator, you need to keep pace with the changes in technology and address the new regulatory frontiers. The World Telecommunication Development Conference held in Buenos Aires in October 2017 reaffirmed the importance of joining forces to build a new ecosystem of opportunities for all based on access, affordability, collaboration, value creation, partnership building and innovation. As you know, ICT affecting all sectors and there is a need for collaborative discussion, collaborative regulatory approaches and collaborative or joint project implementation. In ITU, we are already working with our other sister agencies, UN agencies. We are working with UNESCO on education. We are working with WTO on health. We are working with FAO on agriculture. We are working with ILO on the decent job for young people. We are working on innovation with UNEDO and we are also working on digital financial inclusion with the World Bank. The theme of this GSR is very meaningful to all of us. The theme is new regulatory frontiers. We need to go out from our comfort zone and look at today the environment, look at the new ecosystem and play a key role we played in the year 2000 to make sure that the ICTs are used for development to make sure the ICTs are used to cater for all the 17 smart sustainable development goals and the 169 targets. We are here today explaining these new frontiers to discuss what we can do together and insist on that what we can do together to ensure that emerging technologies impact positively on our daily lives as consumers, as business or just as the citizens of the world to do so. We will work during this GSR 18 under the chairmanship of Mr. Sourin Grindanu. Thank you for accepting to share this GSR. The president of a national authority for management and regulation in communication in Romania. The mayor has seen his CV before being the president of the regulatory body in Romania. Mr. Grindanu, the first time I met him he was a minister. After becoming the prime minister of Romania now he is the president of the regulatory body. So in his career, he has the opportunity to embrace several areas of the ICT ecosystem. I think that he is the best chairman we could have to lead us during these three days as we are multi-stake holder here. He can understand when a minister is talking. He can understand when a regulator is talking. He can understand if a prime minister coming by is speaking. So then thank you very much for accepting that. I would like again to thank you just very much for joining us at GSR 18. Thank you so much.