He's a data visualizer and designer for NASA's Earth Observatory with 19 years of experience at NASA. He's an expert at creating clear and compelling imagery from satellite data. Many of you have probably seen his collared tutorials who are incredibly just really incredible. He's focusing on producing visualizations that are elegant and easily understandable while accurately representing the underlying data. His imagery has appeared in newspapers and magazines and websites and advertisements and we're actually featured on the logging screen of the original Apple iPhone. So please give a warm welcome to Robert Timmons. Thank you, Irene. So I'm the lead visualizer on NASA's Earth Observatory. And our mission is to share with the public the images, stories and discoveries about climate and the environment that emerge from NASA research. And as you can imagine, that involves quite a few remote sensing images and data sets of the Earth. So we deal with things like temperature, temperature anomaly, vegetation, aerosols, ozone, nitrogen dioxide, carbon dioxide, carbon monoxide, energy flux, clouds and dozens of other data sets. So it's really challenging to show all of these different data sets so that they're all distinct but that they all show the underlying data as accurately as possible. So I've just worked a lot with using color as a representation tool. So today I'll just go over some very basic color theory just enough so you'll understand the terms that I'm talking about. Simple versus perceptual color spaces. Color and visualization, so how to represent numbers with colors. And that's actually mostly a solved problem. So even though that poor use of color is really common, 15, 20 years ago researchers like Cynthia Brewer actually proposed a pretty comprehensive way of using color for representing numbers. And then I'll show some examples that really take advantage of what you can do with precise control of your colors and then just go over a couple tools useful for creating your own palettes. So just to get started, does anybody know what this is? And John Swabish, you can't answer. Good, anything else? It's actually Mars. It is not an artist's impression. It is the first image of the surface of another planet ever taken from space. So July 15, 1965, Mariner 4 flew past Mars. It was actually a replacement mission, the first one it failed. And the tape recorder was a backup tape recorder. So the engineers were concerned about the reliability of their hardware. So while they were waiting for the computers to crunch the numbers, they printed out long strips of papers with the values that had been recorded by the instrument and then transmitted back to Earth, and then colored in the numbers with pastels. So they were doing by hand something that the computer was actually taking longer to do. So they were literally painting by numbers. And so the purpose of data visualization, so pretty much any data visualization, is to illuminate the data and to show patterns and relationships that are otherwise hidden in an impenetrable mass of numbers. So if you go back here, if you would just have that and it was just the numbers, you really wouldn't be able to see the girl in the red dress from the matrix. You just see the numbers. But by color coding them, you can see the patterns that represent the surface of another planet. And so color for this type of spatial data, so where you have an X, a Y, and then a Z, so multi-dimensional data, it doesn't have to be a place on a surface, but that's the type of thing I work with most oftenly. It's one of the most effective and certainly the most common way of conveying that type of information. And then careful use of color can enhance clarity, aid storytelling, and draw a viewer into your data set. But poor use of color can obscure that data or even mislead. So to sort of explain how to do good color, I think you need to understand a little bit of basic color theory. And so this is not really my area of expertise. I know just enough to get myself into trouble. So if you do know a lot about color theory, don't boo too loudly. So just go into some of the ways that our retina detects color and how the cells in our eyes respond to different wavelengths, how that compares to the RGB channels on a monitor, and then describe color spaces based on lightness, saturation, and hue, which is more representative of how we think about color and how our brains process color. And also discuss how these color spaces are perceptually uniform. So no matter which direction you're moving, your changes are going to be equivalent. And that's really important if you want to accurately represent underlying data. So there is an objective reality to color. It's a representation of wavelength. But the colors that we see are tricks of the imagination, and there is no perfectly objective view of color. This is a quote from James Gleick on the Radio Lab podcast a couple years ago. They did one called Ripping the Rainbow, a new one, which is just a fascinating look at the psychology of color and color perception. And I highly recommend it.  It's processing that's going on from the moment that light hits the retina and then continues through the optic nerve and into the brain where we're processing vision. That's not a real reality. It's definitely a constructed reality so that we can interpret it and move through it and react to it appropriately. So we can see that lion that's hiding in the brush, or we can see that bright red piece of fruit that's hanging off of a tree. So this is sort of the basic spectral response of the cones, which are the color sensitive cells in our retinas. And so this is actually a relative contribution. So it's not the exact amount that each of these cells responds to different wavelengths. Or you can also do it sort of proportional to the number of cells because they're not evenly matched between short wavelength, medium wavelength, and long wavelength. But sort of it's assumed that the three types of cones contribute equally to our color perception. And you can see that these are all different shapes. They're all different widths and different heights. And that they're all much broader than the three very narrow frequency bands that you would get from a monitor. So the red, green, and blue primaries in an additive color space. And then these are what these three primaries look like. To a computer, these three color ramps of red, green, and blue are all equal. They all start at 0. They all go to 255. So they're black to full brightness. But because of that really strange and non-linear color response, green is very bright to us. Pure red is actually about 50% gray. And pure blue is even darker than that. So you can't just assume if you put up a pure red, three chips of color, one's red, one's green, one's blue, that we're going to think that those are all equal values because the brightness varies so much. Instead, what you need to be doing is thinking in a hue space or a color space that's built around lightness, hue, and saturation.  Hue is what we colloquially think of when we think about color. So orange, green, blue, purple. Those are hues. And then saturation is just dull to rich. So a dull gray to a blood red. So each of these ramps is controlling the other two characteristics are held constant and just varying lightness, varying hue, and varying saturation. And then there is a color space called CIELCH, which breaks these components down mathematically and is used for translating between different color spaces to translate from film to a computer screen to print. And it's actually based on human perception. So they would measure how people saw little chips of color that filled either two degrees or 10 degrees of their field of vision. And so it's a color standard. And if we use this standard, we can actually build palettes and build color rainbows and stuff that are actually perceptually constant. So they are corresponding to how we see instead of corresponding to simple math and a computer. And so this is what LCH color space looks like. Each of these pinwheels is a constant lightness. And then saturation increases as you move outwards from the center. And hue changes as you move 360 degrees around in a circle. So hue is sort of unique because it wraps in on itself while lightness and saturation have a begin point and an end point. And one of the things you should notice is that color space is extremely lumpy. So there's no such thing as a dark yellow. It just doesn't exist. It's not something we can perceive. And it's something that you need to be aware of when you're trying to represent numbers with colors because you don't want to be changing your numbers that are in an invisible way because you're not conveying any information that way. Another thing that you should notice about this chart is that every step is equal. So if you move out from that center point to the next one, no matter what the lightness is, it's still the same perceived difference. And that's the way that we can build color palettes that change equally in accordance to the data that's underlying. And so one thing I'd like to point out that this is not the same thing as an HSB scale. So HSB is just a direct linear translation of RGB. And it just forms a cylinder. So this is that exact same thing where each of these is, in theory, equal lightness. But you'll notice that they're not actually equally light and that they're perfectly uniform. So there's actually colors that you can specify in HSB that mean nothing because there's no visible difference between them to us. So now that we have those basic definitions, I'd like to talk about using color in visualization. There's a long history of doing this in cartography. It's 100 or 200 years older of a practice than computer science or even before the term data visualization was coined. And the palettes that they've developed are actually based on how we see and similarly to LCH, as opposed to a lot of the modern palettes, which were more built for the convenience of the programmers. Using a color space is more accurate than using an RGB color space, but it's still far from perfect. And then I'll also talk a little bit about sort of the different types of data. So sequential data, which is just high to low, divergent days of data, which is positive and negative, and then qualitative maps, which are to show different categories. So as I mentioned, cartographers have been encoding data with color for a very long time, at least 190 years. This map is from 1823 and it's temperature zones across the whole world. And so even though if you know my work, you know that I'm not a big fan of the rainbow palette, in this particular case, I actually think it works because it's partially quantitative. So you're talking about hot regions and cool regions and that you can assign particular temperatures to that. But it's also partially qualitative because it's not a direct numerical relationship. And the rainbow palette is actually very good at segmenting regions. So you can pretty easily see that around the equator you have the torrid regions and in the poles you have the frigid regions and those zones in between. So I just really encourage you to look at the work that cartographers are doing in print because it is consistently at a very high level, has been done, I wouldn't say in a purely scientific way, but definitely in a research-based way, developed in the late 1800s, refined in the mid-20th century, and continue to be used today, including this map, a bathymetric map of Crater Lake and surrounding the surrounding National Park, where you both get a sense of the depth of the lake and the land cover classification and the topography, as well as some of the roads and the park boundaries and things like that. So it's just looking at maps is probably, you know, contemporary maps are some of the best uses of color and data-vis. So I've mentioned these palettes developed early on for data visualization and computers and the two most common tend to be, or are the grayscale palette and the rainbow palette. So grayscale is just a black-to-white ramp and a rainbow palette is just moving around the edge of that hue saturation and brightness space at full saturation. And they're both pretty flawed as far as accurately representing the patterns and the underlying values, but they're flawed in completely different ways. So with the grayscale palette, your main problem is that surrounding tones affect our perception of the lightness of like small areas of color. So adjacent colors and adjacent brightnesses will affect how we perceive things. And this is something that's physiologically based. You can't really defeat it just because you think you can and you're thinking about it. It's like it is so hardwired in your visual system that you have to understand that it's always happening. So each of these little gray rectangles in the color ramp is the exact same color. They're all 50% gray. But on the left-hand side, surrounded by light, they appear darker. On the right-hand side, surrounded by dark, it appears lighter. And so this is actually the smaller the area, the worse the effects. If you have a really fine detail in a data set, this is a much bigger problem than if you had like wide swashes of color. So with the rainbow palette, the problem is more that it's uneven. So if you have a change from one to two or 11 to 12 or 101 to 102, you want that perceptual change to be equal because it's all just one change. In a rainbow palette, if it's near cyan or yellow, that could appear to be a huge change. But if it's right in the middle of this green area, it would appear like nothing at all is going on. So if you're using the rainbow palette, which my colleagues at NASA do all the time and just are so wetted in that they won't believe me when I present, I'm like, look, it's there. And they're like, oh, but I see more detail. It's like it's not detail that you can see. I mean, it's not really detail in your data, it's just detail that you can see and it's misleading you and they just don't get it. So I try, hopefully you guys will take that out into the world and use better color. But there is a slow revolution happening with a lot of the younger scientists are taking this more seriously, they're taking design more seriously. So I do see at least a little bit of hope. So a few other problems with the rainbow palette is that it varies from dark to light to dark to light to dark again. And so it sort of breaks any natural ordering of the colors. And another problem with it is that it uses up the entire spectrum. So it makes it very, very hard to layer in additional data. So if you wanna show like coastlines or roads or anything like that, you really can use black and white and that's it. There's no other way to add in extra data. So it just dominates everything. So as you can tell, I really hate the rainbow palette. And fortunately I have some improvements that I can suggest. So Colin Ware, a color researcher at University of New Hampshire, I'm not a color researcher, a data visualization researcher at UNH, describes it this way. A kind of spiral in color space that cycles through a variety of hues while continuously increasing in lightness. So you're basically adding a lightness gradient to a change in hue and saturation. So you get the perceptual linearity of a grayscale palette and you get some sort of more discrimination because you're changing in these other color components. One other thing to keep in mind is that our visual system is really driven by lightness. The hue and saturation are more the secondary order effect. So if you have the lightness vary in a way that's perceptually accurate, you can cheat a little bit on the other two to make a palette that's more suited to your dataset or emphasizes or de-emphasizes certain things. So here's another example, showing a palette computed in LCH versus a palette computed in HSB. And you notice the LCH palette has a very nice smooth gradation from pale yellow through aquamarine to blue. HSB starts at the same pale yellow, goes through sort of a bright green that sort of smudges out some of that detail and then has a very quick transition to dark blue. So HSB is better than rainbow, but still you should be working in pure LCH space. And so all of these palettes are applied to what Cynthia Brewer terms as sequential data. So it's data that has a low value to a high value. In this case, this is a map of evaporation in the Idaho high desert. You can see the central pivot irrigation and the areas where there's rivers. You have a lot of vegetation. It's emitting a lot of water into the atmosphere. The more yellow areas are more barren land with very little evaporation going on. So it's just a pretty simple mapping of high values are the dark and highly saturated colors, the dry areas are a light, very pale color. So in addition to sequential, there's two more fundamental data types, divergent data and qualitative data. So divergent data has like a break point in the center. So signifying a difference. So anomaly data, difference of this year's temperature versus the average temperature or profit and loss or positive and negative charge are things that it's appropriate to use a divergent palette for. And then qualitative data is where your data is broken up into discrete classes or categories. So something like land cover or political parties in a European multi-party system where you have like six or seven parties, you would want to use what's called a qualitative palette. So here are some examples of divergent palettes. And they're basically two sequential palettes sliced together with the center point being a light neutral color. You want a neutral color because you don't want to associate the middle values with either of the extremes. And light usually works better because you are often wanting to emphasize your outliers. So it's easier to pick up, our visual systems are better picking up dark saturated colors than they are light pale colors. So that's why you usually construct with a light color in the middle and then move to the more extreme colors on either end. And so here are just two examples of the palettes. And then here's one in use where I actually break that rule I said about using a neutral color. So this is magnetism on the sun. And so the color is representing positive or negative magnetism and the neutral color is yellow because I wanted it to be evocative of the data set. If I used gray, it would just sort of be bland. And this really just helps with an instantaneous read of what you're looking at. Another short digression, red, green color blindness or color deficient vision. So about 8% of males, 5% of the general population are missing some of the cones in their eyes. And that means that they cannot distinguish colors as well as people with a full compliment of all three types of cones. Most common is red, green color blindness. So browns and reds look like green. This screen is actually doing a pretty good job of showing it. So each of these pairs is a full color version and then a version that's simulated by Photoshop to look as it would be to somebody with red, green color blindness. So I'm assuming since I have an audience of about 200 people that maybe five to 10 of you have some degree of color deficient vision. So of the top pair, do any of those look identical to you or can you clearly see the differences? Yeah, so Eric has an issue with, he can't see them. So unfortunately that top palette we had been using on the Earth Observatory for like five, maybe 10 years to show differences of vegetation. And even though one of my colleagues is like, I'm colorblind, I can see nothing. I stuck with it. The bottom palette is from Color Brewer. It is considered to be colorblind safe. And I hope that Eric can see the differences between the low values and the high values. All right, excellent. So you can use Photoshop or a couple other web-based tools to simulate different types of color deficient vision to make sure that your visualizations are readable by as large an audience as possible. So the last type of data is qualitative data. And there you have, you wanna make your colors to be as distinct as possible to segregate out these different classes. I'm showing two different 12 class palettes. These are actually a bit beyond what is reasonable. Like if I were to use this on a real dataset you really wouldn't be able to distinguish some of these colors from one another especially if the fields are small. Good rule of thumb is magical number seven plus or minus two. Even though the research was on a completely different subject it still seems to pretty much hold up. So five to nine categories works. If you have more than that one technique you can try to do is to group like classes together. So in this case, urban areas are all given chains of tones of red even though there's four different classes of urban. And forests are given three different shades of green. So this allows you to sort of have an overview where you can see urban and forest and then you can pick out more details when you look at the whole image more carefully. It was pointed out to me after I used this as an example that this is completely useless for somebody with color division of vision. So the urban and the forest look pretty similar I'm sure. It does, okay. So it can be problematic. So this is just, I left it in here because it sort of underscores the point that you need to be really careful with presentation and accessibility issues sometimes that things that you think are perfect can break down in some cases. So all of what I've gone through so far is what I would consider to be like the basically understood color theory and color and visualization. Cynthia Brewer wrote it down all in the mid 90s. It's in Colin Weir's books. Tufty talks about it. And it's all pretty well understood and it's based on what seems to be pretty decent research. Beyond this, I think some of the things that you can do sort of playing with these colors are to use intuitive or semantically associated colors. Think about the cultural associations of colors when you're doing a visualization. You can carefully match colors to display multiple data sets. Use it to show transition points that aren't diverging data. Use it to separate data from non-data and to separate figure from ground and use it to create a hierarchy for layering multiple data sets together. So here's an example of what I would consider to be a reasonably intuitive palette. On the left is our famous rainbow scale. Gene Feldman has argued with me again incessantly that he's been doing it for 20 years. He knows what he's doing. And not only do you see these really sharp transitions between the equatorial waters and then just north and south of there and then another sharp transition in the North Pacific, then it washes out all the detail in between the Canadian coast and East Asia and then along the coastline you get detail again. So you have those inherent problems in the rainbow palette but also we're talking, this data set is something called ocean color. So it's measuring essentially the amount of vegetation in the ocean. And generally speaking, we don't associate day glow green, bright yellow and bright red with ocean colors. Using a more muted palette that goes from dark blue to pale yellow suits the data set more and I believe is more readable by audiences that don't already have PhDs in your subject matter. And there is a growing amount of research that supports that view. It's not just me hand waving. So there's a good paper that just came out and was just written up, I think the night or Harvard School of Business called Selecting Semantically Resonant Colors for Data Visualization, which is talking about like if you're doing a chart of say sales of different types of fruit, if you color the bars to represent what we, blueberries and tangerines, blue and orange that we can perceive that much more quickly than if we have to cross reference with a key. Another thing is that colors have emotional and socially constructed meanings. So John Nelson put together essentially Google Image Search Quoting where he would take these abstract terms, run a Google Image Search and then blend them all together to come up with sort of the archetypal color for each of these. Notice that this is in English. But so for nature we have green, which is no surprise. Abstract is sort of a blue, business is blue, frightening and dangerous and depressed are red and orange. So I put it out that these are all in English. You know, red is sort of a color of anxiety or danger in Western cultures. In China it's a symbol of good luck. So don't expect these to be universal. So if you still don't believe me on that, here is the famous Iraq's Bloody Telegraphic. Andy Kirk brought it up yesterday, talking about negative space. So Andy Kotgrieve, I think maybe six months ago, did a redesign, making it Iraq, deaths on the decline. We're merely by flipping the orientation and coloring the red blood-like drips into a nice neutral blue. He gave the graph an entirely different message. So obviously some of that is the shape of the data set and how we read it, but a huge component is also the changes in the color. So you can use color to evoke an emotional response in an audience. Another thing that I think is really cool is you can show two equivalent quantities at the same time. So in this case I'm showing net primary productivity, which they're measuring the exact same thing, which is the amount of carbon that's brought out of the atmosphere and into vegetation. So either that same oceanic vegetation we were looking at with ocean color or mostly trees on land. But they're measured in completely different ways by different instruments. So they're not the same data set. And so I could have just brought them together and applied one pallet to the whole data set, but then you wouldn't be able to tell instantaneously which is land and which is ocean. I'd have to put a coastline in or something like that. But what I've done instead is take two color ramps that start and end at the exact same lightness and they have the exact same change in saturation. They just simply have different hues. So you can instantaneously differentiate what's land and what's ocean, but also directly compare between the quantities in say the Amazon rainforest and the equatorial belt in the central specific. So another interesting thing is to do, to indicate a threshold. So this is a map of sea surface temperature. And one interesting thing in the oceans is that as soon as you hit 82 degrees Fahrenheit, you can form sustained hurricanes. So at 82 and above, you get hurricanes that are getting stronger. As soon as they move into cooler water, they start dying. So this color pallet, the lightness is changing uniformly in the entire range. And I simply changed from a blue hue to a pink hue at exactly 82 degrees Fahrenheit. So you can see again instantaneously where you have a risk of hurricane formation and then where you're not gonna get any hurricanes. And you can see in the western Pacific why that's such a hotbed of really intense cyclones. And if you don't believe me on the lightness being equal, that's the same map converted to grayscale and Photoshop. So it preserves the underlying structure of the data. Andy also talked about nothing and the importance of showing no data. You can use color to differentiate data from no data really easily. So on the left is a globe put together by my colleagues at NOAA, where all of the water is colored, it's a much lighter color than anything that's considered to be a valid part of the dataset. But it's on the same color spectrum essentially. So it's just a lighter yellow on a beige to green scale. So it seems like it's on the same continuum. So you actually have to work hard to differentiate what's land from what's water. If all you do is just switch that to a neutral gray, which is sort of off of that continuum, it becomes relatively easy to make that differentiation. And that's because our brains and eyes actually do something called pre-attentive processing with color. So before we're even consciously aware of it, the eye is segmenting these two categories into colored and non-colored. So we just can glance at the image and you can see where the valid data is and where it's irrelevant because we're measuring vegetation, which isn't growing in the middle of the ocean. There's also some sort of surprising things that I found using color. In this case, I was trying to map the world's hottest places. So this is a map that's the maximum land surface temperature that occurred over the course of seven years in any given area. So every single pixel you see, except for Antarctica, which I should have colored to no data because it wasn't measured, but is on the scale. Every pixel you see is relatively warm for that area. So I didn't wanna do the traditional blue, yellow, red palette. I wanted everything to look warm. So I chose to use a desaturated yellow to rich red because the very brightest areas and darkest areas of the hottest regions sort of stand out a little bit, even though they're small regions. So you can see parts of Australia, parts of Iran, parts of Northern China are where these really hot areas are. And it worked pretty well as a global map. But when I zoomed in to show Northern China in particular, I got what I consider to be this very strange optical effect that is very strong to me personally, where in this image, what do you think the hottest areas are? Just glancing at it. Yeah, so it looks like those bright areas are the hottest ones. They're actually about 30 or 40 degrees C cooler than the sun-facing areas of those dunes on the right. So even though I'm using that exact same color palette that I used here, because of the structure, because they're these smaller areas with well-defined boundaries, they pop out, they pop into the foreground, and they look warmer. So I couldn't use that palette and had to switch back to the more traditional one, where you can clearly see that not only are they cooler, but you can see that that's a completely different structure. So that's all irrigated areas with vegetation, as opposed to the un-vegetated surfaces that are surrounding it. And I could probably even tweak this palette a little bit more, just to make some of the, even those sort of medium blues are super warm. Like even the hottest areas in this image are about 70 degrees C, which is literal cook and egg on it. And so the final thing that I'm gonna talk about that I think you can do with color is use it to create a hierarchy. So Ben Schneiderman has his famous mantra of overview first, zoom and filter, then details on demand. And he was referring specifically to interactive data visualization. But I think it works for static visualization too, where you give an overall picture, and then as you look at an image and study it more carefully, you can get more and more information out of it. And the way you do that is by layering. And color is an important part of that. So this is a mid-century USGS map of Chattanooga, Tennessee. And the underlying terrain is sort of these mellow earth tones, light, desaturated, and that shows all the topography. And then they've laid the street grid and buildings on top of that. So it segregates itself into two different layers. And you can start to see how the two are related. So how does the topography govern how the street grid is built? How does the river interact with the topography? So again, this is an example from cartography where they've just been doing this for so long and have been thinking about it so seriously that they've really come up with some of those local optimums that Mike was talking about yesterday. So I'll end with just a handful of tools that you can use. Color Brewer, the NASA color tool, and chroma.js. Color Brewer is the easiest. It's the most widespread. So she just gives you a tool which has sequential palettes, divergent palettes, and qualitative palettes. And then you can pick the number of categories you need. It'll show an example on the sort of sample map. And then it'll give you a list of RGB values that you can use to create a palette. These are really common. They're very well-designed. And it's pretty easy because you're just picking from a menu. If you want some more control, the NASA color tool gives you basically direct access to that CIE LCH space. So the pinwheels I showed are actually pulled directly from the site.  You have to go through sequentially and move the hue, pick your hue, pick your color, move to the next hue, pick your next color, and then import that into Photoshop sort of by hand. And then finally, Gregor Eisch, or Eisch, I'm not sure on the correct pronunciation, built a series of tools called chroma.js, which do interpolation in CIE LCH space. There's a bunch of different front-ends for this. This one is on viz4net slash lab slash multi-hue. You can actually specify colors by name. And there's a few other flavors. If you just search on chroma.js, you'll find a variety of just different interfaces that expose his underlying code. So one last thing I'd like to say, and this is a quote from Donald Norman, aesthetics matter, attractive things work better. So everything that I've said right now is based at least a little bit on research and on human vision. When I talk to my colleagues at NASA, they're often calling me an artist. And that's something I actually am somewhat uncomfortable with because I feel like they think that then I'm using my opinion to construct graphics and I'm really trying to do things that are as accurate as possible. But on the other hand, I do think that creating something that's attractive is a tool to make our visualizations more effective. So in addition to color, I think it's important to use all the other standard design tools to make coherent visualizations. So careful use of line, grouping things together, typography, and things like that. So just to sum up, color has long been used to denote numerical values, especially in cartography. When you're choosing colors, I think it's best to do that in a perceptual color space. For data visualization, use a palette that is based on lightness but also varies in hue and saturation. Remember that certain types of data should have a divergent or qualitative palette applied. Try to use intuitive colors when possible, build a hierarchy and combine it with other design principles. Here's a list of selected resources and thank you very much.


By way of introduction, allow me to take a moment to present my credentials. Timothy Matlak, merchant, brewer, clerk of the Second Continental Congress, storekeeper of military supplies, member of the Philadelphia Committee of Inspection, secretary of the Committee of Officers of the City's Militia Battalions, secretary to Congress's Marine Committee, commissary and clerk-in-chief of the Committee of Claims, Colonel of the Fifth Battalion of Rifle Rangers, delegate to the Pennsylvania Constitutional Convention, secretary to the Supreme Executive Council of Pennsylvania, trustee of the New University of the State of Pennsylvania, secretary of the American Philosophical Society, co-founder of the Religious Society of Free Quakers, architect of the Free Quaker Meeting House, and the Anthony Morris Mansion, the Highlands. Surveyor of the Headwaters of the Susquehanna River, scribe of the Olive Branch Petition, George Washington's Commission as Commander-in-Chief of the Continental Army, and the Declaration of Independence. Now, if you've heard my name at all, it's probably in connection with this document here. Now, of course, Thomas Jefferson wrote all the words to the Declaration of Independence. In fact, he wrote a lot more words than actually made it into the Declaration. It seems that some of his ideas were considered too radical by Congress in those times, and they decided that it was bad enough they were demanding their independence from Great Britain, but they didn't want to say anything that was going to make the King really mad at them. So, by the time they got done nitpicking Tom's Declaration, it looked like this. Now, you can't hold up something like this as the birth certificate of a new nation. Official documents should look really official. Now, every government office had a clerk or two whose sole job was to take the rough copies of all the declarations and proclamations and new laws and make them bigger, more impressive, and official. Why, any young fella who wanted to get a nice job out of the hot sun could take a few lessons in penmanship and find a nice career as a scribe or an engrosser or a scrivener or an engraver or whatever you wanted to call him, and you didn't have to have a university education. I never had one. I was born in Haddonfield, New Jersey. My parents were good friends. That is to say, they were members of the Religious Society of Friends, also known as the Quakers. When I was eight years old, we moved to Philadelphia, the Quaker City. We rented a nice old house right next door to a very clever fellow named Benjamin Franklin. My father started the first of several unsuccessful businesses, and I went to the Quaker School where I excelled at, can you guess, penmanship. When I was thirteen, I was apprenticed to a prosperous Quaker merchant to learn the mercantile business. Now, if your family wasn't rich, then they couldn't afford to send you to England to go to a university. You had to learn a trade to support yourself. Now, you had plenty to choose from.  So, there were lots of opportunities for you. Did you ever wonder why there are so many people named Smith? Well, there was the black Smith, the white Smith, the gold Smith, the silver Smith, the copper Smith, the Cooper Smith, the tin Smith, the lock Smith, the gun Smith, who made the gun lock, stock and barrel, and a horn Smith who made very clever things out of cow's horns. Don't want to be a Smith? You have rights. The real right made the wheels that went on the cart made by the cart right. The cart took your goods down to the boat made by the boat right. The boat rode out to the ship made by the ship right, and then you sailed off down the river waving goodbye to your wife in the house made by the house right. Don't care for that? That's all right. There's a whole laundry list of other opportunities and I have it right here somewhere. How about Tinker, Taylor, Cooper, Sawyer, Joiner, Miller, Tanner, Courier, Fuller, Weaver, Chandler. They make candles. Barber, Baker, Hatter, Cutler. They make knives. Shoemaker or Cobbler. Cobbler repaired the shoes. Or maybe Potter or a printer. Well, choose wisely because you're going to spend the next seven years learning that trade. I spent my seven years learning how to run a business, mostly writing letters and orders and keeping the store records, which was very good practice for my penmanship. When my father's brewery business failed, so did his health. He died when I was only 16. And then we all had to pitch in to help support our mother. I was hired by Benjamin Franklin to copy a very massive petition to the king all about the treaties that the Penn family had made with the native tribes. It would not be the last message I would write to the king. In time, I married and started my own business, or I guess I should say businesses. I had no more luck at business than my father had. But I did get very involved in politics. Things were heating up on the political front. Everybody was dissatisfied with the way that England was treating the colonies. So I began to go to town meetings and serve on committees and take part in other duties. And until finally, in 1774, I was called upon to engross what Congress is calling the Olive Branch Petition, a sort of peace offering to the king. They wanted it done in a fine dignified manner. And Charles Thompson, secretary of Congress, remembered the work I had done for Ben Franklin, so I got the job. The Olive Branch Petition looks like this. Very impressive, huh? I'm sure the king would have been impressed too if he'd bothered to read it, but he didn't bother to read it. And things just got worse. I was given the job of writing a commission naming George Washington the commander-in-chief of the new continental army. By that time, I was the official clerk of Congress. Now, George Washington is a very impressive fellow. But no matter how impressive you are, you can't just walk into the continental army and announce that Congress has sent you to take over. You've got to have a very impressive paper to prove it. So I wrote this. Very impressive, huh? Signed by John Hancock himself. Everybody knew who he was. Well, while George Washington was keeping the British army busy up into England, the Continental Congress was keeping itself busy arguing with itself about what to do about England. What is our situation? Should we stay or should we go? My good friend Thomas Paine wrote a little pamphlet he called Common Sense to encourage them. It was about 40 pages long, and he had it printed, so I didn't have to write it up. Pretty soon, everybody was talking about it, and Congress listened. And so, on the 4th of July of 1776, the Continental Congress approved of the Declaration of Independence. And Tom Jefferson wrote a nice, clean copy to take to the print shop so they could print up nice print copies. Perfectly serviceable. They were sent out all over the countryside so that people could read them or have them read to them and approve or disapprove. We were committed now. What we had to do was officially sign the Declaration of Independence. There was only one small problem. The clean copy that Thomas Jefferson had provided for the printer never came back. It's never been heard from since. I suspect the apprentice took it home for a souvenir, perhaps. Anyway, Congress needed something impressive looking to sign when Congress came back into session. So, on the 19th of July 1776, it was decreed that a fine copy be engrossed on parchment paper. So I had a big job to do. Fortunately, we had these printed copies to go by. When you are using typeface to write a document, you can get a lot more words on the paper than if you were writing it out by hand. This paper is 19 by 14 inches or so, but a handwritten copy is going to be much bigger. This official copy is 29 and three-quarter inches by 24 and one-quarter inches. Well, there was going to be a lot of calculating to do. First of all, you needed to understand exactly how you wanted the text to appear. Figure out how to put the sentences all even on one side and keep the paragraphs from looking all choppy on the other side. And you needed to count all the words. There are 1,337 words. You had to scribe very fine pencil lines on the paper all the way down so that the sentences would be even and level. You need to figure out how big the text was going to be on the paper so there would be room at the bottom for the signatures and room at the top for the title. You might want to write up a couple of practice copies just to see how it's going to look that everything will look neat and balanced on either side. Very impressive, you see. Well, when you're satisfied that you had everything ready, you went to work slowly, carefully, concentrate. A quill pen will hold enough ink to write four or five, maybe six words and dip it back in the ink again so there's no hurry. You might make a mistake now and then, but that's all right, just let the ink dry and then you can shave it right off with a very sharp blade. That's possible on parchment paper. Well, then once you're satisfied with the finished product, all I had to do was inform Charles Thompson, Secretary of Congress, who informed John Hancock, President of Congress, who chose August the 2nd, 1776, as the date to sign the official copy of the Declaration of Independence. Job done. Well, of course as they say, the rest is history. The Declaration of Independence has been admired and displayed all over the world. I might be so bold as to say that it is actually a work of art as such. It has been examined and questioned and queried by experts and students alike. I'm constantly getting questions about it. Some more came in today and I'm glad to share them with you. Question. Why are the words of the 13 United so small compared to the rest of the title? Well, if you look at the July 4th print copy, you'll see that it doesn't say the unanimous declaration of the 13 United States of America. That's because New York's delegation didn't vote until July 9th. So they couldn't say unanimous. But once they voted, well, you had to change the title. You wanted to reflect the fact that it was a unanimous declaration and highlight the idea that we were now states instead of colonies. Also, using a smaller set of words there allowed me to put the title on in only two sentences instead of five like the printed copy. And you remember I had to leave room at the bottom for the signatures, especially John Hancock's. Huh. Why did you put in all those dashes? Yes, the dashes throughout the text indicate where a new paragraph would be in the old paragraph left off. If you look at a normal document, paragraphs generally start on the left-hand side of the text and are indented. Now, as I said, that would make the declaration look all choppy. So I only did that twice. The very first line is indented, and the six lines from the bottom, it's indented at the beginning of the concluding paragraph. Why do so many words have Fs in them? It sounds funny when you read them. Well, those letters are not Fs at all. They're actually Ss, the old-fashioned long S that dates all the way back to ancient Roman times. If you examine the declaration, you'll notice that those Fs only appear where there are two Ss in a word and never at the end of a word. They stick out like sore thumbs all over the printed copy. Eventually, printers got tired of getting those long Ss mixed up with the actual Fs in their type trays, so they simply stopped using them. Ah, question. Were there any mistakes on the copy that you missed? Yes, as a matter of fact, there were three mistakes that I missed. When the document was examined by Congress on the 2nd of August, they noticed that there were three mistakes. 16 lines down, the word representative had the E N left out of it, and we had to put that back in. 11 lines from the bottom, the word only was left out of one of the sentences, and that had to be put back in to make sense. Directly under that, the word British was spelled with two T's. But in those days, spelling was a matter of opinion, so we just left it there. You've got to give me credit. Only three mistakes out of 1,337 words isn't so bad. Question. Is there a secret map on the back of the Declaration of Independence? In a word, no. That is just an adventure story. Totally fiction. People like adventure stories, and the more improbable they are, the more they seem to like them. But no. The Declaration of Independence is a national treasure all by itself. I'm glad you like it. Well, for the Second Continental Congress, I'm Timothy Matlak.


Hey, oh Nice to have you here. Thank you for coming. So in case this is the first time you're seeing the same communicator name then I'll start with a short presentation. It is a communications application I'll allow you to do lots of interesting stuff on the internet starting with instant messaging We support most of the instant messaging Networks out there like MSN and Yahoo messenger, but above all we concentrate on the open and standardized ones like XMP PNC So a very important feature centric feature of the SIP communicator is its ability to do audio video calls That are by the way completely secure and encrypted using different mechanisms starting from SRDP for example an internet Specification and I believe we actually have the author of the specification in the room So thanks for working on that and we are also using ZRDP for negotiating The the encryption keys ZRDP is an encryption key negotiation system defined by Phil Zimmerman Specified by Phil Zimmerman. He's also the author of PGP. So I've also thrown in here a couple of screenshots displaying our file transfer feature we have an image preview here showing and Another feature that I'm particularly glad to talk about today is our desktop streaming Implementation that we've just finished this week. You can check it out over there In our stand in the A.W. building an important part of SIP communicator is an important aspect is the fact that it is completely very multi-platform you can use it on Linux Windows we have pre-built packages for macOS X Ubuntu RPN based systems. So we are using it on all these operating systems and We are using it daily for our work on SIP communicator itself and After we keep using it and we keep adding features that we need to see in there And now one feature that we particularly wanted is to have conference calls and That is not necessarily a difficult thing because most of the providers out there Give you the opportunity to give you the option of calling a certain number that acts like a rendezvous service that allows you to To meet with other people and then whatever you say everyone is hearing, but we wanted more than that we wanted to to see who else is on the call we wanted to have audio levels and We basically wanted it to act and feel a little bit like Skype our conference calls Not only because they look good, but because they're also useful It is it is nice to be able to know who's currently speaking how lovely Who's it who else is participating on the call and The one one big advantage about using open standards in your work is that whenever you think about a new feature That you would like to add to your application Well chances are that someone else has already thought of it and has specified it and has described a way that it should work And that's a good. That's a good thing not only because you know That the specification that you're going to be implemented is going to be working in different kinds of internet topologies and in different scenarios But also because if anyone else has implemented already or if anyone else is going to implement it in the near future Well, then your solution is going to work with theirs With just like that. Well and in theory at least because in practice You probably have to resolve a few bugs before it does but it's still a great advantage, right and In the SIP universe, which is the IDF standards and specifications are called RFC's and the RFC that deals with basic SIP conferencing is 4353 and For those of you who don't really know SIP, don't worry. This is going to be it's actually in principle a very simple protocol So right here we have Basic call one one call between Alice and Bob. It's always between Alice and Bob It's it's just it's just like no one else is calling on this earth so a call starts with sending an invitation Alice would send an invite message then Bob would reply by Sending status responses indicating that his phone is ringing and then a no care response to actually accept the call and after Alice acknowledges the call and they would start exchanging media whether that is audio video or both That doesn't really matter. So if at one point Bob would like to add someone else to the conference Well, he would need to do two things according to that specification. First of all, he would Have to re-initialize his goal his existing call with Alice so that she would know that this is no longer one-to-one call and this is important because Maybe she would like to know that whatever she's saying is potentially being heard by someone else and The second thing that Bob would do Is start a simple call just as the one like he's having with Alice with Carol Again, and he's going to indicate that this is going to be a conference call another one-to-one communication and after that They're all going to communicate now What is important here is that both Alice and Carol would be communicating with Bob only It is up to Bob to mix packets that he's getting from Carol with his own audio and then send it to Alice And it is again up to him to set to mix packets that come from Alice with his own audio and send it to Carol This is why Asians like Bob are often referred to as mixers. That's how I'm going to call him So we started by implementing this RFC and state communicator and this gives us basic mixing functionality But we wanted to go farther than that because we didn't have We didn't have the possibility to see who else is in the room. We didn't have the advanced user interface and interactivity That we were looking for so we moved on to implement in another RFC 50 45 75 Which allows us to do just that? So again, it's a simple principle Oh once a conference has been established between a number of participants They could all subscribe With the mixer with the centric entity so that they would get notifications. Whatever something changes in that conference And whatever the mixer is ready He would be able to send sip messages containing XML bodies Indicating if a who is on the call whether they've just joined or left or whether they've changed their status or name or some other information and At one point everyone in the code is going to have a complete view of the conference and be able to show it to their users now That was our second implementation step and that gave us The the notion of rooms people were able to see the list of participants in the conference go but we still didn't have that last part about audio activity and about the audio levels in a call and That was a tricky part because As you can see here all the agents in the call are only Communicating with the mixer and the only party here that is able to recognize who is actually talking at any given point Is the mixer? He's the only one that can analyze in the individual audio streams of all the participants so We were thinking that we decided that maybe we would need to allow the mixer give the mixer the opportunity to actually encode that data into the into the streams and Convey it to the other participants. So the protocol that is used in the better part of all VoIP applications RDP Is also an internet an IDF protocol. Here's it. Here's how a pocket looks. Here's how an RDP pocket looks and Obviously the most important part of that packet is the product conveys the audio that contains the audio But in addition to that we have a bunch of other fields that contain useful information like for example the sequence number Which would allow a user to determine whether or not Their packets are arriving in this order or the timestamp fail Which would allow you to synchronize your audio flows with your video flow. So do other useful stuff with it and In our case the fields that were interesting that were most interesting were these two the first one is called SSRC and It's an identifier every entity that sends RDP packets Generates a supposedly unique identifier to kind of sign their packets and So if I'm getting packets from Alice for example, I would see it All of them are signed with her identity with her ID So if I'm a mixer, I Would I could include all these in the list of CSRC identifiers? That's what it's meant for so that The participants in the co-conference code that I'm mixing that I'm responsible for would know the audio that I'm getting in this packet was generated by the entities with these identifiers and This is what would allow us to To show what your activity to show who's speaking in a particular call So we are almost there. We almost have the you know the Skype like conferencing user interface Except for the last bit showing the audio levels and why is that important to us because sometimes showing audio activity isn't enough Sometimes you have someone speaking and someone someone has the microphone And then at the same time someone else is generating some kind of weird Pastoring noise like, you know the Darth Vader people that would tend to put their microphones right underneath their nose and they start So you try to listen to the conference going understand whatever is being said but that doesn't happen because someone's so if When it when having all your levels it could very easily resolve that because the thing is that Normally people that generate that kind of noise don't don't know that they're doing it So when you once you determine who that is well, you could send them an instant message and say well Well, could you please maybe stop doing that and use your mute button because we're trying to have a conference call here You're not really helping so In order to what it's actually quite obvious that at this point It's the the most sensible thing to do would be to simply add a list of audio levels right after the list of the Identifiers and that's what we did we added an enumeration of audio levels here that Coming the same order as the CSR C identifiers and that was the last piece necessary necessary to implement the user interface up We were that I'm talking about We've described and published this solution in an internet draft you have the name over here and It's hopefully going to be integrated as an official working group document and one of the IDF working groups hopefully soon and That this was it. This is what gave us the possibility to give that to have that interface a Very important thing that I would like to say I would like to express our gratitude to the Internet Foundation From Netherlands who have been funding this work from the start. So thank you guys I don't know if anyone is here, but thanks anyway Oh, and another important thing is that again the fact that all communication is here is encrypted Transport through sRDP through the ZRDP key negotiation mechanism And oh, yeah, this currently only works with SIP for the time being but again as I said we are going to have an Implementation for XNPP jingle. This is the the job or way of doing telephony for one-to-one calls maybe at the end of the month or in the beginning of next month and We are going to port To to integrate the possibility of doing conference calls and all with all the interactivity with all the advanced audio level display stuff by Maybe the end of April So that was it. Thank you for coming and thank you for listening We have a couple more minutes. So if anyone has any questions, I'd be I'd be glad to answer. Yes Um, there is Yeah, I didn't say that indeed there is a field defined by the 4575 RFC that allows you to map a SIP URI to a to an SSRC number Anyone else? Yes No, you're not supposed to use the RDP to actually encrypt SIP signaling you do that normally using TLS and SIP communicator supports that so you can have a completely secure Co-establishment and in coal scenario with SIP communicator Simone state in your channel as in a man in the middle attack or Well, that's what the RDP is built for it has That is a special way of a very fine identity by involving a vocal check So you're displayed for characters that you have to read to the opposite party that have to compare them with with what? they're seeing and verify that this is indeed you so hijacking a connection would only happen if someone is able to imitate your voice or something which is Kind of difficult No, we don't do any of that right now Maybe yeah Well, that's the thing Well, we haven't really tried to find the maximum number of participants up that would depend very much on the On the computer configuration, but this is not meant to be something that you would use in large conference calls. This is Utility that it could use for Conference calls with four or five more participants And if you would need to have something more advanced than that then then you would have to You would have to use a server hosted solution somewhere and by the way servers such as come we leave it could be I could also implement the audio level solution and The 4575 RFC so we would have exactly the same user interface with CIP communicator as as with conference calls hosted by CIP communicator itself. I Was hoping to do that Thank you again


Here I am on the summit of Red Cloud took me two hours and 45 minutes to get up here from the trailhead And there's the what a horn which Robert Patrick and Jonathan and I will be climbing day after tomorrow. We're gonna do conflagrate tomorrow swinging around that's to the east obviously with the Sun rising and back to the south and We're gonna see Mount Sunshine come into view right there, and that's gonna be the path. I'm gonna take right now Scoot over there pick up another 14er and we're back around and there's Handys back To the west which I'll be climbing later on either today or on Monday climb on


Oh, koala tea, like koala, like the animal, tea. Don't let me do any more puns. It's less on Sprout, we're proud to be entertainment, and I'm on Sprout today, walking around to give you guys some much needed and very unreasonable life advice. We're trying to sell this stuff for Children's Miracle Network, people are buying it. Go to a strip club, pick up all the ones that are on the floor. It's for the kids. It's for the kids. Do you need some life advice? It seems like you can't do it. They ask you how you are, you just have to say that you're fine, when you're not really fine. Lots of stress, lots of turmoil with finals. Just play EDM music for the next like 127 hours. It'll be like the Dave Franco movie all over again.  How's your relationship with your parents like? It's annoying getting on the phone and talking to them. Whenever they try and call me or text me, I'll just be like, sorry, I'm in class right now. It'll be like 11 p.m. and I'm just in class. They think I'm like the most studious person ever. What do you think about the socks? Oh my gosh. I can't afford avocados, but I can afford the socks with dogs. Life advice, pet some dogs. Oh, hi buddy, hello. I'm stressed about finals. Adderall, drop out of college, start a hedge fund. You could get a sugar daddy or a sugar mama, or both. Why stop at one? I'm applying to grad school right now and I'm only filling out one application. I just kind of feel like plan Bs and Cs are for like suckers, right? Oh, I like it. This sign has literally no support. It's just like me. How do you show her? There's a lot of whipped cream everywhere. I don't know how much water is gonna help you at this point. Have you cheated on anyone lately? Has anyone cheated on you? No, I have not cheated on anyone. I haven't been cheated on either. So your problem is you're not dating shitty enough people. Clearly your life's not exciting enough. Currently, I live in Eshelman Hall doing ASUC things every day of my life. ASUC is wonderful. They fund us. Thank you very much. You should quit. Do what you love, spend every day like it's your last, wake up every morning, like it's very new. How much do I have to pay to pay you in the face? One, two, three, grab your glasses. This is what I get a lot to do for CalTV. Thank you.


Nancy, my daughter Maureen, our son Ron to be here, we're sorry the other members of the family couldn't be here but they had conflicts of schedule and are about 3,000 miles away. But you have made us feel very welcome. We have broken bread with you now even though most of you were not aware that that was going on down here. I've been asked by some of my friends in the press why we picked Hudson County for this kind of kickoff of the campaign. Now I'm sure they asked because Hudson County is the home of Democrats in such great numbers. No, no, and I hope a lot are here. As a matter of fact I'm the first Republican candidate to come here since 1968. But the answer to their question is I'm here because it is the home of Democrats because I believe today that in this country there are millions of Democrats who are just as unhappy with the way things are as all the rest of us are. It's fitting that on Labor Day we meet beside the harbors, the waters I should say of New York Harbor, with the eyes of Miss Liberty on our gathering and in the words of the poet whose lines are inscribed at her feet on the air-bridged harbor that twin cities frame. Through this golden door under the gaze of that mother of exiles has come millions of men and women who first set foot in American soil right there on Ellis Island so close to the Statue of Liberty. These families came here to work. They came to build. Others came to America in different ways from other lands under different and often harrowing conditions. But this place symbolizes what they all managed to build no matter where they came from or how they came or how much they suffered. They helped to build that magnificent city across the river. They spread across the land building other cities and other towns and incredibly productive farms. They came to make America work. They didn't ask what this country could do for them but what they could do to make this this refuge the greatest home of freedom in history. They brought with them courage, ambition and the values of family, neighborhood, work, peace and freedom. We all came from different lands but we share the same values the same dream. Today a president of the United States would have us believe that dream is over or at least in need of change. Jimmy Carter's administration tells us that the descendants of those who sacrificed to start again in this land of freedom may have to abandon the dream that drew their ancestors to a new life in a new land. You're right that Carter record is a litany of despair of broken promises of sacred trusts abandoned and forgotten. Eight million eight million out of work, inflation running at 18 percent in the first quarter of this year. Black unemployment at 14 percent higher than any single year since the government began keeping separate statistics. Four straight major deficits run up by Carter and his friends in Congress. The highest interest rates since the Civil War reaching at times close to 20 percent. Lately they're down to more than 11 percent but now they've begun to go up again. Productivity falling for six straight quarters among the most productive people in the world. Through his inflation he has raised taxes on the American people by 30 percent while their real income has risen only 20 percent. The lady standing there in the harbor has never betrayed us once but this administration in Washington has betrayed the working men and women of this country. The president promised that he would not increase taxes for the low and middle income people, the workers of America. Then he imposed on American families the largest single tax increase in our nation's history. His answer to all this misery he tries to tell us that we're only in a recession not a depression as if definitions, words relieve our suffering. Let it show on the record that when the American people cried out for economic help Jimmy Carter took refuge behind a dictionary. Well if it's a definition if it's a definition he wants I'll give him one.   Maybe I should quit right now but I'll go on for a few more minutes. I have talked with unemployed workers all across this country. I've heard their views on what Jimmy Carter has done to them and their families. They aren't interested in somatic quibbles. They're out of work and they know who put them out of work and they know the difference between a recession and a depression. Let Mr. Carter go to their homes look their children in the eye and argue with them that it's only a recession that put dad or mom out of work. Let him go to the unemployment lines and lecture those workers who have been betrayed on what is the proper definition for their widespread economic misery. Human tragedy, human misery, the crushing of the human spirit. They do not need defining. They need action and its action in the form of jobs, lower taxes and an expanded economy that as president I intend to provide.  It's Jimmy Carter's. He caused it. He tolerates it and he's going to answer to the American people for it. Last week, last week more than three years after he became president he finally came up with what he calls a new economic program. It is his fifth new economic program in three and a half years and you know listening to him he talks as if someone else has been in charge for these last three and a half years. Now with two months to go until the election he rides to the rescue with a crazy quilt of obvious election year promises which he'll ask Congress for next year. After three years of neglect the misery of unemployment inflation, high taxes, dwindling earning power and inability to save. After all this American workers have now been discovered by the administration. Well it won't work. It's cynical. It's political and it's too late. The damage is done and every American family knows who did it. In 1976 he said he would never use unemployment as an economic tool to fight inflation. In 1980 this year he's called for an increase in unemployment to fight inflation. In 1976 he said four years ago that he would bring unemployment and inflation both down to 3%. Well who can believe him. Unemployment is now around 8%.  But here beside that torch that many times before in our nation's history has cast a golden light in times of gloom. I pledge to you I'll bring new hope to America. I'm looking forward to meeting Mr. Carter in debate confronting him with the whole sorry record of his administration. The record he prefers not to mention. If he ever finally agrees to the kind of first debate the American people want which I'm beginning to doubt he'll answer to them and to me. This country needs a new administration with a renewed dedication to the dream of an America. An administration that will give that dream new life and make America great again. Restoring and revitalizing that dream will take bold action on this day dedicated to American working men and women. May I tell you the vision that I have of a new administration and of a new Congress filled with new members dedicated to the values that we honor today. Beginning in January of 1981 American workers will once again be heated. Their needs and values will be acted upon in Washington. I will consult with representatives of organized labor on those matters concerning the welfare of working people of this nation.  As president as president of my union the screen actors Guild. I spent many hours with the late George Meany whose love of this country and his belief in a strong defense against all totalitarians is one of labor's greatest legacies. One year ago today on Labor Day George Meany told the American people as American workers and their families returned from their summer vacations they face growing unemployment and inflation a climate of economic anxiety and uncertainty. One year ago George Meany predicted just exactly what has happened to us. Well I pledge to you in his memory that the voice of the American worker will once again be heard and heated in Washington and that the climate of fear that he spoke of will no longer threaten workers and their families. When when we talk about tax reduction when we talk about ending inflation by stopping it where it starts in Washington we're talking about a way to bring labor and management together for America. We're talking about jobs and productivity and wages. We're talking about doing away with Jimmy Carter's view of a no growth policy and ever shrinking economic pie which means smaller slices for every one of us. That's no answer. We can have a bigger pie and that'll mean bigger slices for every one of us. And I believe that together together you and I can bake that bigger pie. We can make that dream that brought so many of us are our parents and grandparents to this land. We can make that dream live once more. Let us work to protect the human right to acquire and own a home. Make sure that right is extended to as many Americans as possible. A home is part of the dream. I want to work in Washington to roll back the crushing burden of taxation that limits investment production and the generation of real wealth for our people. A job and savings and hope for our children is part of that dream. I want to help Americans of every race creed and heritage keep and build that sense of community which is at the heart of America for a decent neighborhood is part of that dream. We'll work. We'll work to strengthen the small business sector which creates most of the new jobs we need for our people. Small business. The shopkeeper needs relief from government paperwork relief from over regulation relief from a host of governmentally created problems that defeat the effort of creative men and women. A chance to invest, build and produce new wealth is part of that dream. But restoring the American dream requires more than restoring a sound productive economy. Vitally important as that is. It requires a return to spiritual and moral values.  We need to restore those values in our daily life in our neighborhoods and in our government's dealings with other nations of the world. These are the values inspiring those brave workers in Poland. The values that have inspired other dissidents under communist domination who have been willing to go into the gulag and suffer the torture of imprisonment because of their dissidents. They remind us that where free unions and collective bargaining are forbidden, freedom is lost. They remind us that freedom is never more than one generation away from extinction. You and I must protect and preserve freedom here or it will not be passed on to our children and it will disappear everywhere in the world. Today the workers in Poland are showing a new generation. How high is the price of freedom? But also how much it is worth that price. I want more than anything I've ever wanted to have an administration that will through its actions at home and in the international arena let millions of people know that Miss Liberty still lifts her lamp beside the golden door. Through our international broadcasting stations the voice of America, Radio Free Europe and the others. Let us send loud and clear the message that this generation of Americans intends to keep that lamp shining. That this dream, that this dream, the last best hope of man on earth, this nation under God shall not perish from the earth. We will instead carry on the building of an American economy that once again holds forth real opportunity for all. We shall continue to be a symbol of freedom and guardian of the eternal values that so inspired those who came to this port of entry. Let us pledge to each other with this great lady looking on that we can and so help us God we will make America great again. Thank you very much. Now wait a minute, wait a minute. I have a surprise for you. I have a surprise for you to finish up with. We all know from recent experience what the lack of leadership can do in a country and today in Poland we've seen what the presence of leadership can do. There was a man, the man who started that strike and who led that strike in spite of the threat to his very life that existed every minute of every day that he was doing it.  God bless America.


Good morning, good afternoon, good evening and welcome. My name is Anne Van Damme and I'm with the IHE Delft Institute for Water Education in the Netherlands. In this presentation I would like to report on the progress we have made in developing a global model for ecosystem services of inland wetlands. This project is a joint effort led by PBL, the Netherlands Environmental Assessment Agency, in collaboration with the Universities of Wageningen and Utrecht and IHE Delft Institute for Water Education, all in the Netherlands. The continuing loss and degradation of wetland ecosystems, particularly in some regions of the world, are well documented. Our understanding of the direct and indirect drivers that cause loss and degradation is increasing, for example agricultural and urban expansion that is driven by population growth and economic development. And despite an increasing awareness of the importance of wetland ecosystem services, they have often been neglected in global assessments and models, although this is now changing. Different types of valuation studies have demonstrated the importance and the high value of wetlands for biodiversity, for nature and for people. Here you see some examples of the ecosystem services of wetlands. In this study the ecosystem services printed in red will receive most attention. Often the value of wetlands is not taken into account sufficiently in decision making about development projects that may have an impact on wetlands. This applies especially to the regulating ecosystem services that include processes like hydrological regulation, nutrient retention and carbon storage. These are all difficult to quantify because of their dynamics, their natural variation and their complexity. Therefore it is difficult to make them part of the trade-off analysis or cost-benefit analysis. This is where models come in. They can help describe these processes, deal with their dynamics and complexity and make quantitative estimates of regulating services that can be used in decision making. So the objectives of this study are, first of all, the development of a process-based wetland model that can be parameterized regionally for different wetland characteristics and that can be driven by global climate and land use data. The focus of this model is on the regulating ecosystem services like water and nutrient storage, water quality regulation and carbon and greenhouse gases. In particular we want to calibrate the model for different wetland types in different climate zones. In the longer term we aim at applying the model in global studies and assessments of wetland ecosystem services. So how did we do this? First we reviewed wetland modeling studies that had been done before and we defined what we wanted our model to do. This was published in our paper in 2019. We decided for now, for practical reasons, to include only inland wetlands. Then we built the wetland model and I will explain more about that in the next slides. The wetland model needed to be driven by a hydrological model which would predict wetland area, water levels and flow. For this we used the grid-based global hydrology model PCR GLOBWP which was developed at UTEC University. We used global databases, particularly the global lakes and wetlands database, to verify the model estimates. We also used the global nutrient model GNM which is part of the image modeling framework of PBL to quantify nutrient input into the grid cells based on global land use data. In this way we could estimate the water and nutrient balance of each cell and use these to drive the wetland model. In the model a distinction is made between isolated or ponded wetlands that are fed by rainwater or groundwater and floodplain wetlands that are connected to the river network. Grid cells receive water with dissolved and suspended nutrients from upstream cells. Inside cells, nutrients are delivered via diffuse and point sources based on the global nutrient model and distributed to both wetland types. The hydrology model calculates the fraction and area of each cell that is occupied by wetlands and of course the river network also exports nutrients to downstream cells. The wetland model itself is a relatively small model with 9 input variables, 10 state variables and 54 parameters. Inputs consist of river discharge, water body area and depth, a fraction of open water in each cell and nutrient loads. The model itself includes three vegetation types, emergent, floating and submerged vegetation, soil carbon, dissolved oxygen and nutrients. The growth of submerged and floating vegetation in the permanently flooded zone is determined by competition for nutrients and light. While the emergent vegetation in the intermittent zone is regulated by the phosphorus concentration in the poor water. Output of the model includes greenhouse gas fluxes, biomass accumulation and carbon storage, nutrient and water retention or release and the concentration of nutrients in the water. Many of these processes such as for example organic matter degradation or methane production are strongly influenced by the dry wet alternations in the wetlands. In this way, the model captures the hydrological dynamics and how they influence wetland functioning. To calibrate the model, we started reviewing the literature to compile data sets on different types of wetlands in widely varying climatic zones. These data sets form a database on vegetation, biomass, growth rates, nutrient concentrations, nutrient content, carbon storage and emissions and water quality, particularly of nutrients. We will use this database initially to calibrate the model, but once we have enough independent data, we can also use it for model validation. On the map you can see the three wetlands in Sweden, Spain and Kenya for which we can show some initial results, but we are collecting data on other wetlands as well. So now I will show you some initial results of model runs that we did for these individual wetlands sites. I should say that these are part of research that is still ongoing and therefore should be considered unvalidated. Nevertheless, for the Nyando Wetland in Kenya, we were able to do some comparison of model simulations with observed values from the field, and this shows that the model captures the growth of the vegetation and water quality in a realistic way. The Nyando Wetland is dominated by emergent papyrus vegetation, and on the left you can see that emergent vegetation in the intermittent zone dominates the vegetation. In the permanently flooded zone, the floating vegetation is dominant. In the same way, the simulated nutrient concentrations in the water are realistic when compared to observed values. The short-term variation you see in the graphs is caused mostly by seasonal wet-dry changes. We are doing similar comparisons of simulated and observed values for other wetland sites. Based on the simulations of these dynamic processes driven by climate and land use scenarios, we can then use the model to quantify ecosystem services and compare these among different wetlands. This is shown here for nutrient retention and greenhouse gas emissions in the three wetlands. Without going into the details here, we can see that the model calculates different values for ponded and floodplain wetlands of different latitudes of nutrient retention on the left and of greenhouse gas fluxes in the middle. The bars show overall averages for the 25-year simulation period, but of course the underlying time series for these values are available from the model. On the right, we have calculated the overall net greenhouse gas emissions for the wetlands over the whole period. The main point here is that the model can calculate these ecosystem functions and services based on realistic climate hydrology and wetland process dynamics. The last part of this talk I would like to use for some reflection on the model and on how it could be applied. The unique features of the wetland model are that it allows the calculation of wetland processes in a dynamic way, capturing the differences between permanently and intermittently flooded zones and the difference between isolated and river floodplain wetlands. Based on realistic climate and land use scenarios, this leads to clear differences in nutrient retention and greenhouse gas emissions. We are currently trying to validate the model for individual wetland sites that have enough data so that we can check if the model captures differences among wetland types and climate zones correctly. Of course this leads to challenges with the availability of data, but in principle it is possible and the initial results look promising. What does this mean for application of the model? First of all, once the model is validated for enough wetland types and climate zones, we could quantify wetland ecosystem services for different global scenarios and look at more policy-oriented implications such as, for example, the contribution of wetlands to the sustainable development goals, or to determine wetland restoration priorities, which could be interesting for the Ramso convention or for the CBD. We could use global climate scenarios to drive the model and look at the implications for wetland ecosystem functions and services in different regions. But we could also use the model the other way around, for example, to provide input into models that predict the biodiversity in techness in certain regions. An example is the globio-aquatic model, which currently uses land use and habitat data to predict biodiversity in techness, but the wetland model could provide this input in a dynamic process-based way to evaluate different scenarios for their biodiversity impacts. You can see that model development is still ongoing and the most important in this process right now is to calibrate and validate the model with observed data. We have shown the feasibility of coupling a general wetland model to global hydrology and nutrient models. One thing we would like to do is create regional parameter sets that capture differences, for instance, in vegetation types. For example, the emerging vegetation in different regions is typically dominated by species with distinct growth characteristics, and the model can take these regional differences into account to improve model estimates. We've already done some sensitivity analysis with the current version of the model, but this needs to be extended once the model is developed further. Then we can also look at other scenarios, for instance, for different nutrient loads or land use change or wetland management. For example, the model could look at the implications of wetland conversion to urban or agricultural use. This could be done for certain region or on a global scale, and this could be interesting for assessments such as the global wetland outlook. We are preparing a manuscript with a detailed description of the model for publication next year. To summarize and conclude, we have created a process-based dynamic model that incorporates wetland area, hydrology, biogeochemistry and vegetation to predict the main regulating ecosystem services of wetlands. The main objective of this model is a global scale analysis, such as the comparison of regions or wetland types on the basis of global hydrology, land use and climate. The first results show that it is possible to obtain realistic model results compared to observed data. These realistic results then lead to clear differences in ecosystem services outcomes for wetlands of different types in different regions, which demonstrates the potential of the model. We will now proceed with further testing, calibration and validation of the model, and we welcome discussion on its potential applications. With that, I conclude this presentation and I thank you for your attention. Don't hesitate to contact us if you would like more information. Thank you.


Hi, welcome to the Tennyson Center. I'm Keri Sterrett, the clinical director of therapy services. And I'm Karen Wolfe, supervising clinician. And we're going to start by telling you a little bit about the Tennyson Center. Tennyson Center for Children is comprised of three main programs that serve children and youth between the ages of five and 18. Our residential treatment program serves up to 36 children in five cottages and provides 24-hour a day residential care. Our day treatment program is an accredited therapeutic school program serving students from kindergarten through 12th grade. Our community-based program provides intensive services in the client's home and community settings. Approximately 85% of our clients have experienced trauma, such as abuse or neglect. All of our clients are receiving treatment at the Tennyson Center for social, emotional, or behavioral problems. Now we'd like to tell you a little bit about the staff that work here. Though there are many types of professionals that work at Tennyson Center for Children, police and community members will most often come into contact with our youth treatment counselors or YTCs. Youth treatment counselors are the staff providing the 24-hour day-to-day supervision and program implementation for our youth. YTCs are bachelor degree, entry-level professionals. Therefore, most of them are fairly young and relatively new to the field. Tennyson also employs a team of masters-level clinicians who are on campus during business hours and participate in a rotating on-call shift after hours and on weekends. We get a lot of questions about why Tennyson Center is not locked. Tennyson Center is licensed as a residential child care facility or RCCF. It is not a locked psychiatric hospital unit or a Department of Youth Corrections facility. RCCF facilities are not locked from the inside out, meaning any client can walk out the door of the school or cottage at any time. This is different from some other locked agencies that serve youth, such as youth corrections or inpatient psychiatric hospital units. Tennyson does not have any locks that prevent exit from the buildings or grounds. One other distinction is that many psychiatric facilities are able to provide PRN or in-the-moment medication administration to help calm a patient in crisis. RCCF facilities are not allowed to administer PRNs and can only administer prescribed medication at the prescribed time of day. A lot of questions also come up about how Tennyson Center staff intervene with clients who run from our campus. So I'd like to spend a few minutes talking about the interventions we use both prior to and during a client run from campus. Since we are not a locked facility, children can exit the school or residential cottages from all doors. Tennyson staff members are trained in verbal de-escalation techniques to help diffuse a crisis and verbally challenge the clients to make a better choice, such as remaining on campus. Tennyson Center staff members are also trained in physical restraints, but are only allowed to initiate a physical restraint if an immediate safety risk is present. Additionally, staff are trained not to initiate a restraint if the client is off campus anywhere in the community. Leaving campus does not typically count as an immediate safety risk. A physical management to prevent a client exiting campus is only initiated in rare occasions. Examples may include a client who is likely to run into oncoming traffic or a client at risk to become a victim of human trafficking. Physical restraints are always the last resort because restraints have been shown to be re-traumatizing for our client population. Additionally, if a child enters someone's property, staff will call the police rather than entering the property of a community member. It is important to note that once a client is on run, they are likely escalated enough that their brain is in survival mode, meaning they are not able to comprehend what people are saying or actively solve problems in the moment. Tennyson staff protocol is to follow the client at a safe distance because our kids will often go farther away from the facility if we are chasing after them. If a community member or a law enforcement encounters a Tennyson Center client on run, the following phone numbers should be called. During business hours, you can reach our front desk at<PHONE_NUMBER>. After 5 p.m. or on weekends, you may contact our resource team on campus at<PHONE_NUMBER>. Here's what's happening with our kids when they are under stress. Children who have experienced trauma are triggered into a stress response more easily because the alarm in their brain that senses danger is overactive. Our clients have big reactions to seemingly small situations because they are never truly at baseline. This stress response generally causes one of three things to happen. Number one, they fight and that looks like aggression. Number two, flight or running. And number three, freeze, which can be severe withdrawal. Police officers and community members are most likely to come in contact with clients in the flight mode. Many of our clients have had negative experiences with police officers and that is all they remember when officers enter the scene. Police officers are often seen as the people who took their parents away. Therefore, our kids may go into the fight mode when police are present because they see them as a threat. Regardless of how old they are or look, many of our clients function at a much younger level, both emotionally and cognitively. In addition, many of our clients revert back to the stage of development where the abuse occurred when they are under stress. This is toddlerhood or infancy for many of them. So they cannot understand things the way other children their age can when they are under stress. Our clients are at risk for reenacting their abuse. These clients will place themselves in the role of the abuser because that is who had the power in their home. Be aware that we also have clients on campus who are at a lower cognitive level due to being on the autism spectrum or exposure to drugs in utero. Thank you for taking the time today to learn about Tennyson Center. If you'd like more information, you can go to our website at www.tennesancenter.org.