Let's say you go to a restaurant and order a hamburger. The hamburger probably doesn't come by itself. You probably have a choice to make. What kind of side item do you want? Do you want fries or a salad? Now, you know what choice you're probably supposed to make? One of your friends might tell you that you should get the salad because it contains more vitamins, minerals, and fiber. Notice the stated reason there is that the salad contains more vitamins, minerals, and fiber. You probably acknowledge that. That's probably not something you're gonna take issue with. The source of the disagreement is gonna be with the rejection of another claim that is not stated in the argument. That is the warrant. So a counter argument to the first argument would be that you should choose the fries because they taste better.  So the disagreement isn't over whether the salad or the fries taste better. And it isn't over whether the salad or the fries contain more vitamins, minerals, and fiber. One argument values health over taste. The other argument values taste over health. So if you make a claim about what somebody should do, you're telling them to adopt the policy. And if you support that claim by referencing evidence, by pointing to facts, you might be skipping a few steps. And this is a very common problem with first year composition papers. Someone will say that people should do this or the government should do this. And the reason they give will be a statement of fact. And they'll support that statement of fact with great peer reviewed sources, but they assume that stating those facts necessarily proves the claim that somebody should do something. And in order to understand what is missing, it helps to go back a couple of thousand years to ancient Greece and Rome where philosophers and rhetoricians, lawyers, people who had to figure out how to decide the sorts of things that we now consider as a matter of course, they looked at the different types of claims that could be made and divided them up into several categories based on the type of thing that claim argued and the type of reasons that would be necessary to prove that claim. And the term for these different types of claims is stasis. It's usually anglicized as stasis. This comes from the Greek for placement or setting or stature, but also faction or group. It could mean the sort of point at which the disagreement lies. If we're not in the same place, then we're not gonna be able to have a very interactive argument. In some of the earliest writing, there was only four types of stasis over the centuries. There have been as many as 12 or 13 identified, but for our purposes, I'm just gonna focus on five. Those are fact, definition, value, cause and policy. Now facts we've talked about already. A fact, in order to be a fact, something must be verifiable. It's something that other people can see, can check your facts, needs to be falsifiable. In other words, can you prove that it's not true? Can you prove that this thing does not exist? You can't make a vague statement or a platitude and say that this is a fact. You can make generalizations, but these need to be from representative samples. So if we're talking about how to deal with climate change, I can show you data of average global temperatures for every year for the last century and show you that over the past 10 years, we've had more record highs than in any other point. But those are just numbers. What to do with those numbers is a different question. Then we have to be sure we're using the same terms. We have to be sure we have the same concepts to apply to those. And that is the stasis of definition. So it might need to make clear that there's a distinction between climate change and weather. Weather changes day to day, even hour to hour, but climate is something that's more permanent and more global. Once we agree on our terms, then we have to agree on values. Is a rise in climate temperature such a bad thing? If you don't think it is, then I have to convince you that that is a bad thing. And not only that it's a bad thing, but it's so bad that we need to take measures. We need to make sacrifices of other things that we do value in order to slow it or prevent it. I also need to point out that climate is something that has a traceable cause. In other words, it's traceable to the emission of carbons by human industry. And if we're going to change it in the long run, I have to show that there is a plan that if we take certain actions, it will eventually cause this effect. If I can't prove that anything we can do will actually improve things, then it doesn't really matter what the facts are and what we want to happen. So once I find a cause and then I point out how certain actions will have certain consequences, then we have to decide on what to do about it. And that is the stasis of policy. Now there are different kind of policy arguments I can make. Well, the laws need to change or I can say we should only support businesses that use green energy policies or individually we should take these steps ourselves. But anytime we say somebody should do something, that is a policy claim. And we can't just support a policy claim with another policy claim. I can't say you should do this thing because you should do it. That would be a circular argument. That's why it helps to be able to put these different types of stasis together in a structure and use this to figure out how to structure our arguments. I can't make a policy claim and only support it with reference to facts unless we already agree on definitions on questions of cause and effect and questions of value. And these aren't simple issues. To try to make a policy claim when we don't agree on cause and effect or on value or on definition would be like trying to build this pyramid with one or more of the middle blocks missing. So back to our example. If I want to argue that you should eat the salad instead of the fries with your hamburger, I might start with the facts. This is a fact premise when I say that salads contain more vitamins, minerals, and fiber. It's falsifiable. You can examine it. You can look at it under a microscope. This is something you can test independently from me. And that's a very necessary foundation for the rest of my argument, but it's only the foundation. We also need to agree on the premise that vitamins, minerals, and fiber enable the body to function properly. This is a causal claim. And when I say enable the body to function properly, I'm going to shorten that by using the term healthy. So it's a necessary step that we both agree on what the word healthy means. If you think simple carbohydrates are healthy, then that's something we need to stop and define. And then I need to get you to accept the premise that health is more important than flavor when choosing a side to go with your meal. And if we agree on all those things, then we'll probably come to the same conclusion that you should choose the salad instead of the fries. But do we agree on all of those premises? You probably agree that salad contains more vitamins, minerals, and fiber. You probably agree that vitamins, minerals, and fiber enable the body to function properly. You would probably define the word healthy as enabling the body to function properly. But you may disagree with the value judgment when it comes to choosing this particular side to go with your meal that health is more important, that it's more valuable than flavor. And that's going to lead you to a different conclusion. This is going to lead you to choose the fries instead of the salad. That's why it's very important that we look through all the different premises, at least all the premises that we can identify in order to figure out which premises we share with our audience and which premises we do not share. And once we identify the premises that we don't share, we need to know what type of stasis they are. If we agree on the facts and we agree on the definitions and we agree on the cause, we still need to agree on value. And that might be the one that I need to isolate and that's going to be the one that I'm going to spin the rest of my argument focusing on. Trying to change your value judgment and to argue that it is more important to choose the healthier meal instead of the tastier one. And only once we agree on all of those premises will you be led to the conclusion about what policy you should adopt when choosing a side to go with your meal. And these different types of stasis judgments are usually happening whether we're aware of them or not. Just like pattern recognition and other types of system one intuitive cognition, we tend to jump from the premises of the fact to conclusions about policy, about what to do about those facts without stopping to think about most of the other warrants that are necessary to connect the fact to the conclusion. That's because in many cases our system one intuitive cognition does all the processing for us. So if you see a snake in a place where you were about to reach your hand, you immediately know that the facts you see, the scales, the coiled body, these facts mean that you should take action, remove your hand from striking distance. But you didn't have to stop and think about that. By the time your system two metacognition is aware of what you've already done, all it has to do is sort of look and see that yes, I've made the right decision. In these sorts of situations, the whole decision making process, the whole pyramid is built so quickly that you're not aware of all the other steps that were involved. That's what cognition is. It's the thinking that takes place before you're consciously aware. But those facts only justify that action if we already assume the premises about definition, causation, and value. First, we define what we're seeing. You associate the scales and the long coiled body with the concept, the category of a snake.  Some members of that category are venomous and they're all likely to bite. That means that a bite could cause pain, injury, and possibly death. Pain, injury, and death are universally recognized as unhappy outcomes. Preventing those outcomes is probably more valuable than whatever task this snake is preventing me from doing. However, if that snake is posing a danger to someone else, I may place more value on the safety of that other person than on my own safety. That's a value judgment and it's probably one that you'll have to make in a split second. But all those decisions actually take place before you come up with the policy decision about how to react to what you see. So it's the assumptions about the facts rather than the facts themselves that are the foundation on which all other types of judgment depend. So facts are the real verifiable data that exist in the world that trigger our inferences about patterns and heuristics that we use to function in the world. All these premises are inferences that we make after recognizing the facts. We connect the individual facts to familiar patterns, familiar frames about the world and how the world works. And I've talked about in another lecture how to separate facts from BS, from just vague statements that sound like fact but are actually just speculation. Claims that are unverifiable because they're so abstract. A fact has to be falsifiable. It needs to be something that other people can independently verify. If it's not verifiable, it's just conjecture, speculation. I'm supposed to just take your word for it.  We have to make generalizations in order to learn from those individual events or individual objects. This is called generalization. And you know there is a fallacy called hasty generalization. This is where we start with a particular event or particular person or particular object and we then jump to a conclusion that all objects in that category or all people in that category are all the same. That's a hasty generalization but we have to make different types of generalizations. We don't want to be hasty in our generalizations but we do still have to make generalizations. So remember our white swan premise. If I assume that all swans are white because all the swans I've ever seen are white, then I'm making a generalization. And for a long time this generalization was made on a representative sample of evidence. Given what people in the Americas and Europe and Asia and Africa knew, that was a pretty good generalization. It explained all of the available evidence. So as you travel around the world, all the swans we see in North America are large, waterfowl with long necks and white plumage. So we can make the generalization that swans are large waterfowl with long necks and white plumage. We can go down to South America and see the same thing. All the swans there are large waterfowl with long necks and white plumage. Go to Africa, same thing. Europe, same thing. Asia, same thing. Large waterfowl, long necks, white plumage. It's only when we get down to Australia that our generalization is thrown into question. There the swans are large waterfowl and they have long necks, but they don't have white plumage. They're black. So each individual swan I see in all of those continents is a fact in itself, the color of its feathers is a fact. But I make a generalization to try to explain the facts that I've seen and use that generalization to predict that the next swan I see will also have white feathers. However, discovering that swans can be black requires me to rethink not only our grasp of the facts, but also how we define our concept of swan. Do we need to revise our generalization of the swan from a species of waterfowl with long neck and white plumage? Or do we need to create a new definition for a new bird? Is the black swan a different species? Do we put these two types of birds into two separate categories and then give them each their own definition? Or do we revise the definition to include black and white swans? Knowing what the facts are is only the first step in deciding what inferences to make about those facts or what conclusions they can support. So we need to generalize in order to learn from experience. In order to know what to do with the individual facts we discover. That means putting these individual facts into larger categories. But we don't want to overgeneralize because then we'll assume we know more than we actually do. The purpose of a category isn't to contain everything. It's to keep some things together but separate them from other things. And that's metaphorically how definitions work. The word definition comes from the classical Latin word, defineri, which means to end or to terminate. That's the same root word is in the word finite or infinite. The F-I-N in the middle means the border, the limitation. And we have to put borders around our terms so that we can limit what we mean. If a word means too many things, take for example the word thing. If I ask you to go in the other room and get that thing for me because I want to do this thing with it, I'm not giving you enough information to know what it is I'm trying to describe. Whatever the fact in the other room that I'm referring to, I haven't connected it with a specific enough concept for you to be able to follow through with what I want. So if I describe a recent cultural phenomenon, say this is a thing now, or if I say that I have a thing about those kinds of things, I'm not really explaining myself very well. You have to make a lot more inferences about what I'm trying to say. And if the listener or the reader has to make too many inferences, that listener or reader is more liable to misinterpret what it is that you actually intend to say. There are too many things in the world. So we need to define things as narrowly as we can. So when we establish definition, we're establishing criteria for categories that we can use to separate things in the world. So with our white swan example, if we want to be able to distinguish a swan from other types of animals, we can start by defining it as a bird. We separate birds from other animals based on criteria like they lay eggs and they have feathers rather than fur or scales. Within the category of birds, there are aquatic birds and non-aquatic birds. Being aquatic, they share characteristics like the habitat they share with beavers and alligators, but they share characteristics like having feathers and laying eggs with other birds. So if we define a swan as being an aquatic bird, we separate them from other animals like fish and beavers as well as non-aquatic birds like eagles and parrots. But most of the time we encounter a swan, we encounter it with other aquatic birds like ducks. So how do we specify a definition of a swan that will help us distinguish it from a duck? Well, we could add to the definition, the criteria of having a long neck. With each criterion, we add to the definition, we get a smaller group of facts that fit into that category, and that's a good thing. Specific definitions help us distinguish objects in the real world. Defining a swan as a large aquatic bird with a long neck allows us to quickly and easily distinguish it from beavers, ostriches, parrots, ducks, but what about flamingos? They're also large aquatic birds with long necks. Well, there's the difference in color. So we can add the criterion of white plumage. Now we have a definition that's pretty good at defining, separating swans from other animals, but there's that problem with the black swan. Our definitions have to be revised from time to time to keep them accurate. Our experience in the world and our communication with other people leads us to have these operational definitions that we used to determine how to categorize the individual facts that we come into contact with. When we directly experience something in the world, we perceive it. The things that we perceive are called percepts, but as soon as we perceive something, we quickly cycle through our preexisting concepts for the ones that match the percept. So notice the difference between a percept and a concept. If you see a German Shepherd, you recognize it as a dog and as a pet. It's a pet, but it's not a cat. It's a canine, but it's not a wolf. We share these categories with most other people, but we don't always share exactly the same definitions. Definitions establish criteria for distinguishing between things. We may share many criteria, but we may also be carrying around criteria that are not shared. One person might perceive a fully grown German Shepherd and match that percept to the concept of a cute and friendly puppy. Another person might see the exact same dog, the exact same facts, and match those percepts to the concept of a threatening attack dog. In these cases, the concept has more to do with the inference about the dog than the dog itself does. And because our concepts differ, we can look at the same perceptual information, the same facts, and choose different terms to describe them. Those terms carry with them concepts and assumptions that frame the facts. For example, during Hurricane Katrina, when New Orleans was flooded, people were trapped for days with no food or drinkable water. People often had to forage for themselves and take food and water and other necessities from abandoned stores. At the same time, some people were breaking into stores that sell electronics, jewelry, and other high priced, non-survival products. This is usually described as looting, but it's less clear how to define the act of taking something that doesn't belong to you in a survival situation. Is that still looting? Is it finding? Is it foraging? Is it stealing? Is it appropriating? When the police do what they call it commandeering. These two photos were both posted within a few hours of each other during the flooding that followed Hurricane Katrina. In both photos, people are taking food from abandoned grocery stores. The captions for the photos are slightly but conspicuously different. The guy in the top photo is described as, quote, looting a grocery store, end quote. The people in the bottom photo were described as, quote, finding bread and soda from a local grocery store, end quote. These photos were taken by different photographers and published by different media outlets, so it's not like the same person is using one word for one person and a different word for others doing the same thing. But the contrast in the descriptions quickly became the subject of debate since the man described as looting was black and the people described as finding were white. Now, when the photographers were asked about the captions they chose, the guy who chose the word looting said that he saw the person go into the shop and take the goods and that's why he wrote looting in the caption. So we have an implicit definition of what looting is. It's going into a shop and taking the goods according to the person who chose the word. Whereas the photographer who chose the word finding recognized that there was an issue in how he would describe what was going on. So he discussed it with his editor about what word they should use. Should they call it looting? And because the photographer didn't actually witness the two people in the image, in the act of looting, then he couldn't say they were looting. Well, what is the act of looting? Well, this photographer said that I believed in my opinion that they did simply find them and not looted them in the definition of the word. These people were not ducking into a store and busting down windows to get electronics. So this is how he distinguishes between looting and finding. Looting would be ducking into a store and busting down windows to get the electronics. Whereas these people weren't taking electronics, they weren't busting down windows. They were just taking things that were floating out of a flooded store. This is what he seems to be calling the definition of the word looting. Well, when we have a question about definition, frequently our first source is to go to the dictionary. And in this class I always recommend that you go to the Oxford English Dictionary because you get more than just descriptions of definitions. You also get the etymology where the word comes from but also how the word's been used over decades and if not centuries. And in the case of looting, this is the same word that was once used to describe like pirate loot. To loot, the verb meant to plunder or to sack a city or a building. And then to carry something off that you got from that attack. And the verb loot was derived from the noun loot, which describe goods, especially articles of considerable value taken from an enemy, a captured city, et cetera, in a time of war or in a wider sense, something taken by force or with violence. So we have three different definitions of the same word. The criteria that both photographers seem to use as to whether or not to choose the word loot seem to include going into the store and taking something. Whereas the guy on top actually went into the store, the couple at the bottom actually took things as they floated out of the store. The second photographer who chose not to use the word looting also added the criteria about what was taken. And his description of electronics as the objects of looting matches the Oxford English Dictionary reference to loot as something of considerable value. Whereas the food would not be of monetary value so much as immediate survival value. But notice how both of these definitions are considerably less violent. They just include walking into an abandoned store not taking something by violence, not taking something from an enemy, which is a defining criterion of loot in the dictionary. So the words we use to describe facts in reality are shaped by concepts and conceptual frames that are themselves shaped by our personal experiences, assumptions, and prejudices. These concepts lead us to choose particular words when describing perceived facts. And those words shape the way that other people conceive of what we describe. So if we imagine being downstream of this communication process. In other words, this photographer sees someone taking something from a store. He describes that as looting in his article. The photographer might have been using the definition of looting as going into a shop and taking something. Even though he was unopposed, he was not using violence and he wasn't taking something of high monetary value. Then he writes that word and having no other sorts of information, a woman who reads the caption may have the second definition of looting, the one the other photographer used, which is ducking into a store and busting down windows, so doing violence to the store itself, if not to a person, and then taking something of high value, of only monetary gain rather than survival gain. And then if she uses that word to describe this man later on, let's say that this is someone who knows this person. Let's say this guy is trying to get a job somewhere and it turns out that there's a picture of him from the time when New Orleans was flooded, described as looting. And the guy who's gonna decide whether or not this guy gets a job says that, well, if he was looting, that meant that he was taking articles of considerable value from an enemy by force or with violence. And by now, we may be looking at some of the same facts, but a lot of inferences have been attached to those facts, the visual data, the visual perception that were not there in the original scenario. That is, the concept created a frame which led to a misinterpretation of what actually happened. The words we use when describing particular things bear with them the weight of conceptual frames. And remember how coherence works. We look at a particular object and we don't just see the object itself, but we look for a frame to put it in. These frames are encapsulated in definitions. And a lot of times value judgments are embedded in those definitions. Like when we describe something as natural versus unnatural. If I say that I don't wanna eat genetically modified corn because it's unnatural. I not only mean that it was engineered by human science, but I also mean that there's something wrong with it. Maybe it's unhealthy or maybe I just think it's unhealthy. But unnatural has a negative connotation. But someone could say the same thing about non-genetically engineered corn. Teosinte, the plant that corn was cultivated from looked very different and the cobs of corn, if you could even call it that, looked more like wheat because this is what it's related to. So over centuries of selective breeding, Teosinte was slowly engineered into becoming what we now think of in America as corn.  So modern corn is equally unnatural. It just took longer to change it. Through the same sort of selective breeding, we've also bred modern domesticated cattle from the ancient orcs. Ancient cattle herders didn't genetically engineer in a lab, the modern cattle, but by picking the ones that were more docile, the ones that were easier to herd, they took something natural and made it unnatural. The same thing with domesticated dogs. In fact, some breeds of domesticated dogs have been deliberately or unintentionally bred to become cuter and smaller and more like little toys than their wolf ancestors. But this causes problems. If you have a pug, you know they have respiratory problems. They have a hard time breathing because of the shape of their face. But domesticated animals are also unnatural, but we might want to change the criteria. We might want to say, well, that's different than genetically modified because, and as we add different criteria, if we say genetically modified are a different kind of unnatural from selectively bred, we have to add more criteria. We have to be more precise about how we define something. And notice what I'm doing here is not just adding words to a written definition like a dictionary definition. What I'm doing is selectively dividing. I'm drawing borders. I'm making finite the number of facts that could fit into the categories that I'm distinguishing. If you use the word progress when advocating change in the energy industry, what you mean by progress may be different than what someone else means by progress. One person's idea of progress might be, let's build more oil refineries and let's build more oil rigs so that we can gather more oil. Another person's idea of progress might be, instead of propping up a century old industry that is largely obsolete, let's spend more money and spend more resources developing wind and solar means of energy production. People bring different definitions, different concepts of what justice is. This is the motivating conflict in the Daredevil versus Punisher comic books and now Netflix series. Both of these guys are fighting for justice. They just have very different ideas about how you do that. Other abstract terms like freedom, liberty, civilization, loyalty, opportunity, all these terms are broad enough that people can use them to mean completely different things. And we typically take those words for granted. If I use the word justice, I might presume that you mean the same thing I do. I might presume that you are hearing the same concept that I'm trying to describe, that you have the same criteria for the term that I do. But that's very rarely the case. We see this when politicians throw words out that many of their constituents would agree to, would choose the same criteria, but other people might not choose the same criteria. So the politician Rick Santorum argued extensively that same-sex marriage does not count because it doesn't fit the definition of marriage. And he would argue that the definition is something that exists out there externally as if the definition was a fact. And he would use the comparison with a napkin. He says, I can call this napkin a paper towel, but it is a napkin, and why? Because it is what it is. But he says, you can call something whatever you want, but it doesn't change the character of what it is. So he's presuming that there is something out there in nature that objectively makes a piece of paper folded in a certain way, a napkin and not a paper towel. But let's say you're the other guy in the picture with Rick Santorum there. If you ask that guy, can you bring me a paper towel? He's probably gonna know what you're talking about. If there's not a roll of paper towels around, he'll look at the dispenser with those little square objects and retrieve one of those. Whereas if you ask him for a napkin, he would retrieve the same thing. We negotiate our definitions depending on context, depending on expectation, depending on the immediate usage. So this comparison really backfires for Santorum when he tries to then use that to justify forbidding same-sex couples from getting married. He says when some people come out and say that marriage is something else, it can be any kind of relationship and we can call it marriage. Well, no one actually says that. But he says it doesn't make it marriage. Why? Because there are certain qualities and certain things that attach to the definition of what marriage is. But he doesn't bother to make the argument for a definition. He doesn't say this is why we should define marriage this way. He just assumes that a definition is an object in the world as if a definition was the same as a fact that it was independently verifiable. When definitions are by nature, not independently verifiable. They have to be agreed upon in order to function. And we tend to forget this. We tend to use words that our particular social group, our discourse community uses a lot as if that was an objective meaning. And when we do this, we're using what the philosopher Richard Roydy calls final vocabulary. Roydy says all human beings carry about a set of words which they employ to justify their actions, their beliefs and their lives. These are the words in which we formulate praise of our friends and contempt of our enemies, our long-term projects, our deepest self-doubts and our highest hopes. They are the words in which we tell sometimes prospectively, looking ahead, sometimes retrospectively, looking back. The story of our lives. I shall call these words a person's final vocabulary. It is final in the sense that if doubt is cast on the worth of these words, their user has no non-circular argumentative recourse. Circular argument is when you say, this is true because it's true because it's true because it's true. You don't have any good reasons so you just keep repeating the claim as if the claim was its own reason. Those words are as far as he can go with language. Beyond them, there is only helpless passivity or a resort to force. The passivity being, well, let's agree to disagree or let's just stop talking about it. Or the resort to force trying to force someone to accept your definition without a good reason. A small part of a final vocabulary is made up of thin, flexible and ubiquitous terms such as true, good, right and beautiful. We all recognize these terms as opinions. If I say that this movie was good and this movie was bad, you don't take that as a statement of fact or even a claim about a fact. You take that as a particular question of value. It's an opinion. But as Roydy points out, there are many more terms that he says contain thicker, more rigid and more parochial and is more local terms. For example, professional standards. Well, what is a professional standard? At some jobs, professional standards includes men wearing ties. At other jobs, you wouldn't want to wear a tie. If you're working around a machine, you probably don't want to wear a tie because you don't want it to get pulled into the machine and drag you into the machine with it. Different professions are gonna have different standards but the term professional standards ignores that. Also, terms like decency. If someone says, do you have no decency in response to something you do, of course, you think you have decency but you haven't agreed on the definition of that term decency. And when Roydy uses the word parochial terms, by parochial he means local. Like your particular discourse community, the people with whom you communicate most of the time, you probably don't have to be that specific in your definition. You probably don't have to say how do you define that term because you all use the term the same way most of the time. You naturally start to mold your concepts to fit the concepts they have in mind when they use the term. But that doesn't mean that that's all the term is. This is what he calls common sense. And common sense is one of those terms that we throw around a lot when I say, well, don't you know how to do this? That's just common sense. No one ever defines all of the criteria for common sense. Among what group of people is this common? Is it among all people? Is this something everyone in the world knows? Is it just something people in my community know? Is this just something that people in my area of specialization, in my career know? But Roydy says common sense is the watchword of those who unselfconsciously describe everything important in terms of the final vocabulary to which they and those around them are habituated. To be common sensical is to take for granted that statements formulated in that final vocabulary suffice to describe and judge the beliefs, actions, and lives of those who employ alternative final vocabularies. In other words, other people who don't share the same terms, I assume that I can judge them by my terms, whether or not they share the same criteria and their definitions. When common sense is challenged, its adherents respond at force by generalizing and making explicit the rules of the language game they are accustomed to playing. Someone might say that when I use a word like professional standards, I mean you wear a collared shirt and a tie. But that's just enunciating the concept that this person had. This isn't trying to negotiate whether or not that criterion fits in this particular situation. And if no platitude formulated in the old vocabulary suffices to meet an argumentative challenge, the need to reply produces the willingness to go beyond platitudes. And that's when we have to make arguments. That's when we realize that we have to make a claim and give a reason for a definition. We can't just presume a definition should be the one that we're familiar with. We have to say, in this case, I mean this word this way. Do you agree? If not, I want to give you reasons why you should agree or listen to your reasons about why it should mean something else. And Einstein agrees. He says common sense is nothing more than the deposit of prejudices laid down by the mind before you reach the age of 18. And prejudice here is an appropriate term as long as you don't confuse it with something like racial prejudice. We typically use the word prejudice just to mean racial prejudice or gender prejudice. But in this case, it's any pre-judgment. Common sense is pre-judgment. And if something is pre-judged, it means you haven't thought about all the premises necessary in order to reach the conclusion. It's a foregone conclusion. And just like every other foregone conclusion, when you come to, when you meet resistance, when you meet someone who comes to a different conclusion, that means you need to figure out what your premises are and see which premises you do not share. So if we can't depend on common sense to lead us to matching definitions, we tend to then default to the dictionary. And that's not a bad place to start. And I recommend you start with the Oxford English Dictionary for reasons I've mentioned earlier. But you'll notice as soon as you get there that your main problem is there's too many definitions most of the time. Some of them fit the precise way you're trying to use a word, the precise concept you're trying to use, but some of them don't. So if you're using the word to mean one thing and someone else might interpret it to mean one of the other definitions, that's a problem, even though they're all found in the same dictionary. But more importantly, dictionaries follow usage. They're not authorities in themselves. You can't point to a definition and say, the dictionary says this is the definition, therefore that's it. That's all it means. You have to accept the definition that the dictionary gives you. The dictionary tries to keep up with the way language is used. It doesn't set rules that everyone else then has to follow. This has been true for as long as there were dictionaries. Some of the earliest dictionaries like the dictionary of the English language composed by the prolific writer Samuel Johnson were attempts to try to regulate things like spelling, to try to at least get people on the same page, literally, when it comes to using a word a particular way. But what he found was that by the time you add all the different ways a word is used, you find that there are too many definitions for one word, that each of those definitions has to be subdivided into other definitions. And Johnson in particular didn't take himself too seriously as any kind of word authority. In fact, he defines his job as a lexicographer, as the writer of dictionaries, a harmless drudge that busies himself in tracing the original and detailing the signification of words. So not exactly an authority. When he's calling himself a harmless drudge, and he's at least saying that he's trying to be helpful, but he's not even clear if he's actually helping, but he's at least declaring that he's not harming by compiling these definitions. That means whenever we want to make an argument, we want to make a policy claim or a value claim or anything like that, we have to first agree on the meanings of the words and the way we use a word in a particular instance might be different than the way people normally use it. This is the problem that Jonah Laird confronted in his article that we read in the first case study when he had to define stress in a way that was different than the way most people thought of it. He recognizes that most people look at stress with a definition something like an unpleasant mental state with a few long-term consequences. But that idea of stress as a feeling is not an incorrect definition, but notice if he's gonna get you to understand Robert Sapolsky's research into the effects of chronic stress in the form of glucocorticoids on the immune system and on brain development, he's gotta get you to think of stress by another definition. He has to define, he has to separate out different potential meanings of the word stress and has to push aside the meaning of stress as a feeling and just focus on stress as a medical issue, as a chemistry problem. So he defines this new view of stress that our physical health is strongly linked to our emotional state. It's a molecular definition. It's one that can be quantified in terms of glucocorticoids in the blood and it's a critical risk factor predicting an even larger, an ever larger percentage of health outcomes. And more importantly, it's not an alternative. It doesn't replace another view of stress. It exists alongside the other definitions of stress such as stress as a sociological problem. He says stress can be described as a cultural condition, a byproduct of a society that leaves some people in a permanent state of unease, but it's not just that state of unease. It's also the effects that it has on the body and the effects that it has on the body may be noted in the feeling. So all three types of stress, all three definitions of stress might sometimes exist at the same time, but he's gotta separate them out so that you'll understand what Sapolsky's research is all about. Stress is a chemistry problem, not just a feeling. He further distinguishes between demand stress and control stress. When you're in control of a situation, you feel a lot of stress, but that stress goes away once you've accomplished your task whereas demand stress leaves you with more anxiety and it's more perpetual, it's more chronic. And this is the one that the Marmot study is going to show with the British civil servants employees. That study is gonna show that the demand stress leads to a much greater likelihood of death before the age of 60 than does controlled stress. But in order to understand the facts that some people are under one kind of stress and some people are under another kind of stress before we can figure out which kind of stress causes shorter life spans, we have to distinguish the two types of stress, we have to define them. And this is exactly what the Daily Mail article that tries to describe Lara's article fails to do. The Daily Mail article conflates all the definitions of stress into just one amorphous thing and the unnamed Stanford University colleague that the author cites conflates these definitions and describes stress as just a lingering feeling of tension followed by all these vague terms like fresh, like what does it mean if a person feels fresh? That's a very difficult to define concept. What is this focused calm and where does it come from that we saw in our case study leads to all kinds of problems? And that poorly defined idea of stress leads to the Alex Jones conspiracy theory appropriation of the word without actually getting the concept. So by the time the report of the Sapolsky stress vaccine makes it to the Infowars Prison Planet YouTube production, they completely misunderstand everything there is to know about Robert Sapolsky's research as described in Jonah Lara's article. So they take words like stress and vaccine and jab and this kind of thing and they have the word but they have the completely wrong definitions of all of these terms. And they frame those words in terms that completely reframe the entire concept when they use words like establishment media, scientific dictatorship, brain eating vaccines. These are all terms that import the concepts from the world of conspiracy theories and not the world of science. Then there are words like lobotomize, rewire, neuter, sterilize. And those could also be called outright falsehoods but they are words that are taken from scientific research, mostly very old and obsolete, scientific research that's no longer conducted but they seem to be used metaphorically here, their figurative language. And if that's the case, they're still misleading but they reveal a lot about how Alex Jones and his group define any kind of medical intervention. We saw the same thing with Jenny McCarthy's response to Brian Deere's article. She throws on around a lot of terms that are very misleading because they have too many potential meanings. And the one she seems to be using, the one in which the sentence would be true, does not actually describe what is happening in Brian Deere's article or what it describes. So if I take the word debate, there's no debate among scientists about any connection between the MMR vaccine and autism. Debaters have to back up their claims by logically connecting facts to claims and the anti-vaccine crowd has not done that. So instead, they use a fuzzy definition of the word debate that seems to include post hoc fallacies and confirmation bias thrown up by anybody who just has a knee jerk opinion about a subject that they really don't understand. When she says things like calling Brian Deere dubious and saying that his evidence is just a series of allegations and that the response to it is just hoopla. Brian Deere thoroughly documents his case and it's clear from reading McCarthy's diatribe that she's completely unfamiliar with the case that Brian Deere constructs. So dubious here simply means that she doesn't believe the version of the conclusion that she's read in another Huffington Post article. She doesn't even know what his allegations are and so the word allegation here is just used to focus on Deere's conclusions and completely ignoring his evidence. She then uses words like stronger and determined to fight for the truth which implies that there is a fight going on, that there is an attack, that people are attacking the mothers of children with autism because they think they're weak and they wanna hurt them or something like that. But these vague terms import a concept that's completely alien and completely inappropriate to Brian Deere's evidence that Andrew Wakefield deliberately forged evidence in his study. The terms used in Ginny McCarthy's author bio in the Huffington Post is equally problematic. If she's completely misrepresented Brian Deere's case we can hardly call her honest. And if she dismisses all science and all evidence based on her own personal belief or you can hardly call that a humble. She's described as an influential activist in the world of healing and preventing autism. Notice how far we are from any kind of falsifiable claim. For one thing, you can't heal or prevent autism. Autism is primarily a genetic phenomenon and it's a spectrum disorder. At one end of the autism spectrum there are people with Asperger's syndrome who just have a little trouble fitting in socially. Not that significant. And then at the other end you have people that cannot functionally distinguish between other human beings and animate objects in the world.  Sometimes they'll be worse, sometimes they'll be better just like every other child.  So saying that there's healing or preventing autism is making a causal claim and that causal claim is not supported by any kind of criteria. But to even know what healing autism would mean or preventing autism would mean those terms not at all defined. So before we can establish any kind of conclusion we have to agree on the terms that we use. And if we're using the same words but we're not connecting the same concepts, if we're not connecting the same criteria, if we're not connecting those words to the same frames then the way we're framing the evidence is gonna be different from person to person. And we're not gonna be able to make our case about a policy judgment or a value judgment or a causal connection. So if we think of facts as being the base of the pyramid everybody has to agree before we can go any further there has to be something in reality that we're all pointing to that we're all dealing with. Once we establish what the facts are we're not gonna be able to describe those facts unless we're using the same definitions unless we define our concepts, our frames to decide which facts fit into them and which do not, which are outside the finite boundaries of our conceptual assumptions. And when you write a paper you don't necessarily have to do these things in this order. If I wanna make a claim about cause and effect if I wanna say that one thing causes another I don't have to agree with my reader about what thing is better than the other or what we should do about it. But I do have to agree on what the facts are and how we define our terms. Similarly, if I wanna say one thing is good or one thing is bad we don't have to agree on what causes what and we don't have to agree on what we should do but we also have to agree on how we define our terms and what the facts in the particular case are. And in the next lecture we'll distinguish between these three types of stasis. Stasis of causation, stasis of value and stasis of policy.


This study found that over one-fifth of households in Ethiopia lack access to improved sanitation services. The study also identified several factors associated with this lack of access, including rural residents, on-premises water access, media exposure, and wealth status. To address this issue, stakeholders should focus their efforts on the identified hot-spot areas and provide resources to improve access to sanitation services. Additionally, they should raise awareness about the importance of sanitation services and encourage poorer households to use existing facilities or build their own. Finally, they should work to ensure that shared sanitation facilities are kept clean and well maintained. This article was authored by Addislam Wurky Dim Sesh, Ms. Rasha DeRise Tejegni, Sissy Maru Wubant, and others.


In this presentation, we will discuss methods to calculate ending work in process and units completed. In a prior presentation, we went through the flow of inventory from raw materials. Support accounting instruction by clicking the link below, giving you a free month membership to all of the content on our website, broken out by category, further broken out by course. Each course then organized in a logical, reasonable fashion, making it much more easy to find what you need than can be done on a YouTube page. We also include added resources such as Excel practice problems, PDF files, and more like QuickBooks backup files when applicable. So once again, click the link below for a free month membership to our website and all the content on it. To the finished goods inventory and then the sale of that inventory and the expense of it in the form at of cost of goods sold. As we did that, you'll note that we have these items or these times when we had to transfer from department A, for example, to department B in our process cost system and from department B to the finished goods. The journal entry to transfer from department A to department B would be to credit department A and debit department B in essence decreasing department A and increasing department B. In our example, we're discussing like making candy. In that case, we would take it out of department A once the candy is done, put that into department B where the department B applies the wrappers or something like that. And then we're going to take it out of department B once the wrapper is has been given and put it into the finished goods. So then we'll have the completed product from department B to the finished goods. We're going to need some method, however, to note how much is still in department A and how much is still in department B, how much is still in department B and how much is in the finished goods. There's two methods typically to get to that determination. Or in other words, each department, in our case, department A and department B have to do two numbers or have to calculate two numbers. One is going to be the cost of its ending work and process inventory, what is still in the ending work and process at the end of the time period. In the case of department A with us making the candy, what is still in the work and process not completed. And with regard to the packaging, what is still work and process and not completed, what is not moved out to ending inventory and cost of its completed units that were transferred to the next stage of the production process. We're going to have to figure this out and it's not the easiest thing to figure out. It's not as straightforward because the units are not totally complete. So we're going to have to determine how much is going to be still in process, how much has been transferred out in each department. In our case, we were had an example of two departments, the production department and the packaging department. Note that when we think about the journal entry, we're thinking about a dollar amount, of course, because we're thinking about financial statement transactions. To get there, however, we're going to have to think about units, units of inventory. So we must calculate the units within each department, the units of inventory within each department. A couple methods we can use to do that. One's the weighted average method and the other's going to be a first in, first out method. Now you might recognize these methods as the flow of inventory or flow assumptions that we would make if we were a merchandising company purchasing and selling inventory and making some type of flow assumption. It's a similar type of assumption here, but the application of it will look a little bit different within the manufacturing process. Weighted average method. Calculated unit cost by combining costs and outputs from the current and prior periods. So in other words, we're looking at costs and outputs and in some format combining them with the current and prior periods. Makes no distinction between work done in the prior and current periods. It blends together the units and costs from the prior and current periods. So in so doing, we're going to blend them together. We're going to basically, in essence, that's going to be the averaging process and assume the averaging process will basically work itself out. The average will work and be okay to use. The weighted average method because of this blending process because of us not basically making a distinct cutoff between the work in the prior period and this period. In other words, because of the blending together of units and costs from prior and current periods is often a little bit easier if not quite as precise and that would be one reason to use a weighted average method versus the first and first out method. First and first out method calculates unit cost based only on costs and output from the current period. So we're looking at the costs for the current period and applying out those current period costs with a first and first out assumption. Our comprehensive problem will use the first and first out method and our example problems and our illustrations will be using the weighted average method.


So, I think most of you have used different multimedia applications, and you've seen in different lectures, maybe in ITS 323 or similar lectures last semester or previous semester's examples of multimedia applications. We're going to talk about a selection of such applications and the issues of sending audio, video across the Internet. We will give some general characteristics of what we mean by multimedia applications, and also then look at what are the aspects of the network that impact upon performance, and how do we measure the performance of multimedia applications, things like delay, sending rate or throughput, and jitter or delay variation. And then we'll go through several example protocols for sending audio and video across the Internet. For example, voice over IP. We may skip over the streaming stored audio and video or mention it briefly and then finish with IPTV, sending high quality TV across the Internet. Let's briefly introduce some of the requirements for our multimedia applications, and then we'll go to a quick example. So, what do we mean by multimedia applications? Well, different ways we can characterize. One is based upon the direction, so the direction of communications. For example, streaming one way or unidirectional communications. So, streaming audio and video across the Internet is an example. So, there's an online radio station and you listen to that radio via the Internet. Radio, music, and video are the typical examples. And video may be recorded, like YouTube, or it may be live video, like watching some sporting event as it happens. So, it's one way in that usually there's some source of the content that generates the content, and that's streamed in one direction to the viewers or the listeners. We often distinguish between stored and live because they have different requirements in terms of performance, especially delay. Stored is like YouTube. In YouTube, there are a set of servers, but let's think conceptually there's a single YouTube server in Google, Google who owns YouTube. Google have a YouTube server with all their videos stored on it. When you upload a video, you send it from your computer and store it on Google's or the YouTube's server. When you want to view the video, then that video which is stored on the server is streamed to your computer in one way. When you sometimes, when you view a video on YouTube, when you press play, it may take several seconds before the video starts playing. So, what happens is there's some buffering taking place and that when you press play in your client, that is on your web browser, then the YouTube server starts streaming the video. It's already stored at the server and there may be a several second delay even, up to 10 seconds delay before it started. But once it starts, usually it plays smoothly. We look at different causes of why it wouldn't play smoothly later. That's an example of stored video. The video is already created and it's stored in a file on the server. Live video is video or live audio or video is content which is generated. The file is generated at the server as at the approximately the same time as you want to view it or listen to it. So that's, for example, a live sporting event where someone is filming the sporting event and as the content is generated, it is then streamed to the viewers. The main difference is the requirements in terms of performance. With live audio and video, we need a much smaller delay from when it leaves the servers until it is received by your computer. Because being live means what you view or listen to should be approximately happening at the same time as when it's actually happening in real life. That's the idea. If someone, if there's a football match and you're watching it on the internet, so it happens at this point in time and then it's streamed to your computer and if it takes 10 minutes from when it happens until you receive it, then that's not so good for the user because it's a 10 minute delay from when it happens until you receive and when someone kicks a goal, then maybe your friend calls you before you see the goal being kicked because they have been watching it at the game themselves. So that's not very useful. So with a live streaming of audio or video, we usually need a very small delay from when it happens until when you receive it, in the order of seconds preferably, less than seconds. Whereas with stored audio, watching a movie, for example, from when you press play until when you receive it, you can usually tolerate a delay of more than multiple seconds. As long as once it starts on your computer, the client, it plays smoothly, that's the main requirement. So there's different requirements in terms of performance. The other type of multimedia applications, two way bi-directional communications when people are talking to each other across the network. Voice calls, video conferencing, so these are interactive, they're users interacting with each other and they again have different requirements in terms of performance. When you talk to someone over a phone, whether it's across a normal telephone network or across the internet, then the delay between from when you say something until when they receive something typically should be in the order of less than hundreds of milliseconds. If the delay from when you say something until when they receive it, and hear it, is multiple seconds, then it becomes almost impossible to have a conversation because you say something, there's a delay and then the other person receives it while they're waiting for you to say something, your data is being transmitted, they start talking and people start talking at the same time. So with interactive applications, there are even stricter requirements on performance, especially delay. Sometimes we refer to them as real time applications. Because of the fact that we usually require strict delay or have strict delay requirements. But in fact stored audio and video, the delay requirements are not so important. So with a voice or video call, or even live audio video streaming usually want a small delay and a small jitter. So we'll talk about what we mean by jitter. Essentially the the variation of the delay and other applications, non-multi-media applications, what do we mean web browsing, downloading files. In other applications, normally we care about reliability. You download a file, you want to get the exact same file as what's at the server. You don't want any difference. Reliability is essential. But you can tolerate some delays. You start your download of a one gigabyte file. The delay from when you press start until it actually starts transferring can be the order of seconds, not a problem. So also varying delays are not a problem when you're downloading a file, for example. With web browsing delays in the order of seconds start to become a problem because the interaction time is not good. Whereas with multi-media applications, delay is usually the main factor that impacts on performance. They usually delay sensitive applications. If the delay is too large, the time from when the server sends the data until when you receive it is too large, then sometimes the application will be unusable. The users will not find it convenient. Both the delay and the jitter, the variation of the delay. If they become too large, then multi-media applications do not work so well. Whereas web browsing, file downloads, emails require reliability. The server has a file sending to you. The file at the server and the file at your computer, when you download it, they must be exactly the same. It has to be 100% reliable. With multi-media applications, often we can tolerate packet loss or data loss. So there's a video at the YouTube server and it goes, it's a video for 10 minutes and streaming to your computer and you're watching the video on your computer. You can still view the video even if some of the data of that original video doesn't arrive at your computer. Even if some of the data is lost, the application is still usable. What does a user see? Maybe some of the pixels in the video that you view are not displayed correctly. Wrong color or there's a slight artifact on the video for a short amount of time. But usually we can tolerate some loss in multi-media applications. Whereas with normal data applications, data loss is not tolerated. Most of you have seen this or if not seen it, then have seen something similar to it in other courses. Voice communications is a special case of audio communication. So voice, someone talking. Usually voice, the majority of voice communications, the frequency range of the analog signal is from about 300 hertz up to 3400 hertz or roughly from zero up to 4000 hertz is the human voice. So when we want to send the human voice across a network, we need to take that analog data and encode it as some digital data to send in our packets. So we look at different ways to do that. But note that the frequency range in the order of hundreds of hertz up to 3000, 4000 hertz. So a bandwidth of less than 4000 hertz is, well, to be, that's the most typical form of voice. But in fact, it can be a larger bandwidth than that, going lower frequencies and even up to possibly thousands of kilohertz. With music, the dotted line here, we have a larger range of frequencies, a larger bandwidth here. So we have different audio signals. So when we want to send these sources of content across a network, we need to encode them into some digital format and transmit that digital format across a network using some protocol. So we need to briefly mention the ways at which we can encode the analog into digital and then look at some protocols for how do we send across a network. And I think most of you know how to encode, or the simple way to encode analog communications, voice communications. And one example is using pulse code modulation. If this represents our analog input, what we do is we take samples of the signal, of the input strength, and map those samples to some discrete value. And that discrete value can be represented as a binary value, and that's our digital data. So we sample in this case, these points, map to some value, 1, 9, 15, 10, 5, 2, 2, which are represented as four bit binary values. And we've mapped our analog input to digital output. And this digital output can then be stored in a packet and sent across the internet. So we need some way to first convert the analog data to digital data, and then send that across our network. So at our source, for voice communications, you have a mobile phone is our mobile phone, our user talks, analog input to the mobile phone, the mobile phone has to take that analog input. And if we want to send it across as a digital signal or as digital data, then convert that into some digital form into bits. Thanks to analog input and produces some digital output. And then that can be transmitted with a mobile phone, it's transmitted with digital transmission. If it's a voice over IP software, then you talk on your computer into the microphone, the computer converts that into some digital form, which is then sent in packets across the network. So we're focusing on how to do the conversion first. How do we can we send this digital data as an analog signal? Yes, we can. Yes. It depends upon. So here's let's say, instead of a mobile phone, this is your computer, you're using Skype or similar software on it, you talk into your microphone, the computer converts that into digital data. And then that sent using your network connection to wherever if you're using ADSL for your home, internet access, then it's sent from your computer to a modem. And then the modem sends that as an analog signal across the telephone network. But the data that it's sending is still digital. Okay. So at the source, we convert the analog input to the digital form, and then send it across the network. How does it deliver it across the network as either analog or digital signals? That's not so important. It's at the source, how do we convert it? And of course, at the destination, when we receive it, we receive some digital input. And then we need to take that and convert and get some analog output. That's our goal. And of course, the analog output at the destination should be the same as the analog input at the source. If I'm talking here, what the person here is here on their speakers should be the same. Or similar, it should convey the same information. So one way to do this conversion from the analog input to the digital form here is using, for example, pulse code modulation. We take samples of the signal at fixed points. So we record the input value mapped to some discrete value, which can be represented in binary. And hence we have our digital output. Send that digital output across our network. The receiver takes the received binary values and maps it back to the discrete values and outputs, say on a speaker, audio at particular levels, according to those discrete values. So we play back what we receive. What we care about is the two main things we care about in terms of evaluating the performance of this process is the quality that we receive at the receiver, at the destination. We have some quality on input. What does the destination here? What quality of the signal or output do they get? And the other thing is what is required to send this data across the network? How much capacity do we use? With pulse code modulation, what impacts upon the quality of the output? The frequency of what? The frequency of which we capture the samples. The rate at which we record the values. This is our input. What we'd like to get at the output is a perfect reproduction of this shape, this signal. But what we're doing at the input is we're recording not every possible value along this line, we're recording just a selection of the values. So when we reproduce the analog output at the destination, we take those selection of values and use that to create the output. So what would our output look like in a very simple form? So if this is the source input, the digital data produced, these bits are sent across the network. The receiver receives these bits, so they receive 0, 0, 0, 1. And when they receive these bits, which map the four bits mapped to some level one, they produce an output at that level for some period of time. So on the speakers, for example, at the receiver's computer, they output some audio at that level for the period of time, which is equivalent to the sample period here. Then they receive the value 1, 0, 0, 1, which is level nine. So they output a value at level nine. And then 15 is the next value received 10, 5, 2 and 2. So this is an example of what the receiver would produce as an analog output, because what the receiver does in the simplest terms is takes the received digital data and maps that to a particular level and plays that back. In this case, creates an analog or audio output on the speakers. What we want is that the output to be identical to the input, or at least, well, if they are identical, then we'd say that that's 100% quality, perfect quality. Is our output the same as the input in this case? No. The input is a smooth line. The output is this square, rectangular-shaped line. So how do we increase the quality, the sampling rate or the frequency at which take these samples will impact upon the quality? The more frequent we sample, the closer the analog output will be to the original input. And the other way to increase the quality, other way to increase the quality of our audio, the size of the sample, so this value here. Here we had four bits for every sample, which why do we have four? Because we divided the space here into 16 levels. If we divided this same space into say 32 levels, which we could represent with five bits, then each sample we take is, the level we get is more, is closer to the real value. The real value here was 1.1 or 9.2. If we have more levels here, then the sample value will be closer to the real value. And when we reproduce it, the reproduced value will be closer to the real value. So to increase the quality at the receiver, increase the sampling rate, sampling frequency, and, and or increase the number of levels or the size or the length in bits of each sample. And that's summarized here. So the two factors that impact upon quality are how often do we sample, what's the rate, and how big is each in the number of bits. The larger the sample size and higher the sampling rate, the better the quality at the receiver. What's a good sampling rate? Well, it depends upon the input content. For voice, well, in general, Nyquist sampling theorem tells us that it should be twice the highest frequency component. With voice, let's say the highest component of your voice is four kilohertz, the sampling rate should be twice that, which is eight kilohertz. So there's some theorem to tell us what's the recommender value for sampling different types of data, assuming we know the frequency or the bandwidth or the spectrum of that data. What should the sample size be? Well, people have done different tests depending upon the input, and again, they determine the appropriate values for good quality voice. For voice, typically it's seven or eight bits. With eight bits sample size and a sampling rate of 8,000 samples per second usually gives a good quality voice, and it's a typical unit for telephones, especially. Let's look at some examples of that. First example is in these pictures. These are pictures of the output at the receiver. The input was a perfect sine wave, just a simple sine wave. And these are what we get at the output depending upon different or with different sample rates and number of levels. So the first picture is if we have 50 samples per second. So we have a perfect sine wave as input. We take 50 samples per second. The period of the, it was a one hertz sine wave. So from here to here is one second. 50 samples per second, we have 50 bars in this plot here. And 100 levels, which means from the bottom point to the top point we divide evenly into 100 different levels. And we take samples of our perfect sine wave of input and this is what we get in terms of output. And you can see that in the other five plots with different values of the number of levels and the number of samples per second. So in the top row the same sampling rate, 50, 50, 50. But what we change is the number of levels, 100 down to 10 down to 4. And you can see graphically as you decrease the number of levels, you have fewer levels, the quality at the receiver goes down. Why do I say the quality goes down? Because the output, the yellow plot is the output is, is further away from what the original input is. So the difference between the input and output is larger as we go to the right as we reduce the number of levels. If you can imagine a sine wave, this looks like a sine wave, this one looks much less like a sine wave. So we, when we reproduce at the output, it's less like what the original input was. And the second row shows with the same number of levels, 10 levels, with a decreasing sampling rate, 50, 10 down to 4 samples per second. And again we see, as we decrease the sampling rate, the output is further away what the original sine wave was. That is the quality at the receiver goes down. So we want a high sampling rate and a high number of levels to give a high quality output. Let's give a different example. This is a website, there's a link on our course website to it. It's just a website that I've done some demonstrations. I've recorded some voice, 5 or 10 seconds of voice. And then what they do is that they save them, or they take that analog input and use different methods for converting it to digital. So one of them they use PCM, but they use different sampling rates and different sample sizes. And the formats that they produce are listed here. So for example the first one, their sampling rate is 11,025 hertz. That is they sample 11,025 times per second. So there's the analog input, they take their samples, using PCM and 16-bit PCM, which means each sample produces a 16-bit number. In the slide here that is this number is not 4-bits, but 16-bits. And they have 11,000 samples every second. And then they've done it. So what they do is they take the original analog input and record it using that sampling rate and the number of bits. And then they save it as a WAV file so we can play it back and hear it. And then they do it with different values. For example 8,000 hertz, 16-bit PCM. They reduce the number of bits down to 8 bits. And in fact you don't have to use PCM. There are variations for how do you encode that analog input to a digital output. PCM is a simple one, but there are others. Micro law is similar in fact to PCM. Users are some logarithmic coding. And you can also apply other algorithms which also include some compression of the data. So in general there's some codec or encoder that takes the analog and produces a digital output an encoder. And at the receiving end there's a decoder that takes the digital and produces the analog. So together we have an encoder and a decoder or a codec is the general name. We'll list some of the codecs later, but they've done it with different codecs. So PCM, micro law, some delta PCM, GSM, the original codec used by GSM mobile phones. So when you talked on your GSM mobile phone then you, there was a particular codec used to take your voice and save it in a digital format. And as you may have used MP3 is another codec. MP3 is used to take music typically and save it in a digital form. So take that analog and produce some digital output. So they've used MP3 to encode the audio and some other ones. Let's play some of them back and see if you can recognize which ones are the highest quality. And then we'll look at some of the performance characteristics. How are we going to play them back? Let's start, let's start with the top one. I have an audio player. This is, I've just opened that first WAV file which uses 11,000 samples per second, 16-bit samples. And let's see if our audio works and play it back. It's just some Thank you for installing ExpressDictate. You will find ExpressDictate will improve the way you dictate by letting you dictate when you're away from the office. Send your dictation to your typist immediately over the internet and keep track of your work in the typing queue. So what we're going to do is play several of these and you're trying to distinguish which one sounds better. It's the same person talking each time. Let's play that one again and then Thank you for installing ExpressDictate. You will find ExpressDictate will improve the way you dictate by letting you dictate when you're away from the office. Send your dictation to your typist immediately over the internet and keep track of your work in the typing queue. Let's go down to GSM actually this one. Thank you for installing ExpressDictate. You will find ExpressDictate will improve the way you dictate by letting you dictate when you're away from the office. Send your dictation to your typist immediately over the internet and keep track of your work in the typing queue. Can you hear the difference? Let's try. Thank you for installing ExpressDictate. You will find ExpressDictate will improve the way you dictate by letting you dictate when you're away from the office. Send your dictation to your typist immediately over the internet and keep track of your work in the typing queue. Thank you for installing ExpressDictate. You will find ExpressDictate will improve the way you dictate by letting you dictate when you're away from the office. Send your dictation to your typist immediately over the internet and keep track of your work in the typing queue. If you're installing extra dictates, you will find that the dictates will improve the volume of your dictates by letting you take when you're away from the office. Time to dictate into your type is remarkably over the internet and keep track of your work from the type. That one's much more obvious in terms of the poorer quality than the others. So we've only played three at the moment. This one is in fact using MP3. The first one was using PCM, an 11,000 hertz sampling, and the middle one was using GSM. So yes, you need to listen carefully to be able to distinguish the quality, but at least in these three, you can notice that this one's worse than the other two. And I think if you listen to them one after the other immediately or longer, many times you'll be able to detect which one is best. Sometimes it's hard to determine. So probably between the first few, it may be quite difficult. They're using 11,000 and 8,000 hertz with the same number of samples. The first two, it would be quite difficult to work out which one is better. But the other one we played is this cell phone case. One more time. Thank you for installing ExpressDictate. You will find ExpressDictate will improve the way you dictate by letting you dictate when you're away from the office. Send your dictation to your typist immediately over the internet and keep track of your work in the typing queue. Thank you for installing ExpressDictate. You will find ExpressDictate will improve the way you dictate by letting you dictate when you're away from the office. Send your dictation to your typist immediately over the internet and keep track of your work in the typing queue. This one should be the best quality of the ones that we've played at this. And I can detect it, I don't know if everyone can detect it, but this one, compared to the other two at least, is the best quality. You cannot see it from this plot, that's not the idea, it's from listening. And the reason it's the best quality is because it has the highest sampling rate, it has the highest number of levels, or the number of bits per sample, and it has no compression, so there's another factor that's involved. The ones we played, the PCM one at the start, let's record them. With 11,025 samples per second, each sample is 16 bits, they've done some calculations and that's equivalent to 176.4 kilobits per second, because 11,000 samples per second, each sample 16 bits, 16 multiplied by 11,000, we get 176,000 bits per second. So for example, to save a one minute audio file using this codec, we need to store 1.2 or 1.29 megabytes bits, let's calculate, 176 point, let's be more precise. We have 11,025 samples per second, each sample is 16 bits, and for one minute, we have 16 seconds. So how many bits do we get? You need your calculator for this one, 11,025 by 16 by 60 is 10 megabits, okay? Divide by 8, convert to bytes is 1.3 megabytes approximately, okay? So to store audio, one minute of audio using this codec takes 1.3 megabytes of space. Or similar, to send audio across a network using this codec, if we have no other form of compression, we need to send 176 kilobits per second to get that audio from the source to the destination. Because we generate, we're generating 176 kilobits per second at the source, sampling the audio as it comes in every second, we generate 176 kilobits, and we need to deliver them to the destination so they receive 176 kilobits per second and generate the audio output at the same rate as which it's, or the same frequency at which it's generated here. So for this voice application to work, where we're communicating across the network, we must be able to send that digital data generated at a speed across the network of 176 kilobits per second, and it must be received at that rate. If it's received at a slow rate, 100 kilobits per second, then it means, A, they're not receiving all of the data, or B, they are receiving it with some delay, and it will not play back the same. If you don't receive the data, it will not play back what was said.  So an important measure of performance that we use in voice and generally multimedia applications is what sending rate is required to deliver that data across a network. The smaller the sending rate, the better. Now this is what confuses some people. Normally when we talk about throughput, we want a high throughput. But here we're talking about getting some data from A to B. We want to use a smaller portion of the network as possible to get the same data from A to B. So the applications which use a smaller sending rate to get the audio from A to B are better in terms of that performance metric. So the smaller sending rate, the better when we compare these codecs. So the first one, which gave us the highest quality, requires a sending rate of 176 kilobits per second. Another one we used, so there are others. If we drop the sampling rate down to 8,000 hertz, everything else the same, then we change this calculation to 8,000 here and you get 128 kilobits per second. That's the required sending rate. So the second one is better than the first one in terms of the sending rate required, the bit rate. But it's worse than the first one in terms of quality. So there's a trade-off here. We want high quality, but we want a small bit rate or small sending rate. The second audio file that we played was, so we skipped over some, the cell phone quality one, which is the original codec used in the GSM mobile phones. I'm not sure if it's still used in the current generation of phones, but the original one used had a sampling rate of 8,000 hertz and it used a codec to not just take samples and produce the binary value, but to also apply some compression. So use an algorithm so it takes the original digital form and reduces the number of bits that needs to store that data. It compresses it. And the sending rate required is just 13 kilobits per second. But we can distinguish the difference in quality between the GSM codec and the original PCM codec. It wasn't as good as quality, but a smaller sending rate, which is better. Or another way to think of it, if you have one minute of audio with the first PCM, you need to use up 1.3 megabytes of your hard disk. If you encode it using GSM, you use just 100 kilobytes of your hard disk, which is better. The last one we listened to used a different codec. Again, it has some compression, MP3. And that requires just 8 kilobits per second. Even better in terms of the sending rate, but even worse in terms of quality. So that's the trade-off in this case. The sending rate or the bit rate required, we want as low as possible, but the quality we want as high as possible. So we need to choose depending upon what our requirements are. The worst one, the lowest one, it opens. Let's see if we can save it. Okay, it's coming. Try again. My player cannot play it. MP3 is the second worst. That's the best. Keep brainstorming, express dictate. You will find express dictate will improve the way you dictate by letting you dictate when you're away from the office. Send your dictation to your typist immediately over the internet and keep track of your work in the type... I suspect it's not much worse than that one. That one's pretty bad for audio communications, but quite good in terms of the sending rate. So there are different codecs that are available. Returning to our lectures, we're not going to go through all of them, but just give... Okay, we were first talking about voice communications, people talking, same concepts apply for non-voice audio communications, music, for example, except that music generally has a larger bandwidth and users generally require or desire a higher quality output with music. When you're talking to someone on the phone, the audio quality can be one level and you'll be... except that. But if you're listening to music, usually you want a higher quality output. But other than that, same approaches. What about video? What is video? Video is just a set of... Well, video is a set of images that change at some rate to give this illusion of motion. Okay, so we have fixed images and we change them and when we watch it, our eyes think that there's some motion happening. So those images often called frames, still images. Of course, video is usually accompanied by audio as well. How do we measure the amount of information contained in video? Then we look at how big the frame is. It's an image. So we can measure the width and the height of that image or frame and with a digital image, each point is called a pixel and each pixel is some color and we represent a color using a binary value. So how many bits do we use to represent that color is the depth of that color of an image. So if you consider just an image, we have a number of pixels to create the width and the height. So the number of pixels is just width times height and each pixel is a single color represented by a binary value and how many bits to represent that color is the bits per pixel or the depth of the color. So for example, we have an image which is 800 by 600 pixels. The width is 800 pixels. The height is 600 pixels. So there are 800 dots across and 600 dots or pixels down. Each pixel is a binary value which indicates some color and we use usually different lengths. For example, a 24-bit color means that that pixel is represented by a 24-bit number. So we can calculate the amount of information stored in that image. The number of bits we need 800 by 600 by 24 bits and with the calculator is 11 megabits, 11.5 megabits. So if you have an image, just an image, not a video yet, an image of 800 by 600, 24-bit color, each pixel is a 24-bit value, then we have 11 megabits to store there. And a video is simply multiple images that change at some rate. So multiple frames that change at some rate, some frame rate. And we change them at a rate that it looks like there's some motion. And for your eyes, it's considered that normally around 15 frames per second are needed to trick your eyes into thinking there's some motion. If you change the image at 15 frames per second, then your eyes will think that that's smooth motion. So there are different standards for what frame rate to be used. One of them is 25 frames per second. Another common one is 30 frames per second. So what we do is we take our image and we change it 25 times per second. So the number of bits per second is 25 times by our 11 million. And how many? 0, 6. If you have a video and the resolution of that video is 800 by 600, and the color depth is 24 bits, and you're using 25 frames per second, then that video requires to be sent at 288 megabits per second. Who has streamed a video from a server to their computer? For example, a YouTube video. What's the resolution? What's the typical resolution in YouTube? Anyone remember? I think 480 is one value. You can actually choose the resolution. It makes the image larger and smaller in YouTube. So 480 usually is 720. It's the width that they specify. So either 480 pixels across, 720, which is a bigger one, pixels across, and then the height is related to that, depends upon whether it's widescreen and so on. So we're talking about, say, with YouTube, similar dimensions. This is slightly larger, but 720 by a similar value gives us if we're using 25 frames per second and the same color depth, to send that video from a server to your computer, you need to, if you're using 800 by 600, send it 288 megabits per second. Who has an internet connection of 288 megabits per second? Anyone home internet?  Wi-Fi. What's the data rate? You're usually in the order of 10 megabits per second, maybe higher in some cases, your Wi-Fi. 288 megabits per second would be needed to send this raw video to your computer. And it's not a very high resolution, in fact. With video, compression is also used. So before it's sent, it's, in fact, compressed. And it can be compressed significantly. So it's much smaller than this. And the compression usually performed by the codec. So both for audio and video, to save space where you store it or save the network resources when you send it across the network, compression is commonly used. Some examples of the raw data rates required. With PCM voice, normally with a eight kilohertz sampling rate, we need 64 kilobits per second. That's okay. We can send 64 kilobits per second across a network. PCM audio, music. We usually have a larger sampling rate. With music, we usually use stereo, two different channels. And 16 bits per channel means 1.4 megabits per second, which is the quality of CDs, normally. Not MP3, but a normal CD. Standard definition digital TV. For example, similar to the YouTube 720 video, 720 by 576 pixels. 24 bits per second, 250 megabits per second are needed. And with high definition TV, larger resolution, talking about 1.2 gigabits per second. So the raw video, the data rates are so large that most networks cannot support them. And similarly, if you try to save a file using the raw video, you'll quickly use up your hard drive. 1.2 gigabits per second, which is what, 1.5 megabits, 150 megabytes per second. Take a movie, which goes for two hours. So multiply by 7,000, then you've got, what, almost a terabyte, just to store one movie. This is the raw data rate. So audio and especially video usually use some form of compression. It saves the amount of storage, and it saves what needs to be transmitted. There are two general types of compression. Lossy and lossless. Lossy compression is when we reduce the quality of the information that we store. We take the audio with lossy compression. The result is an output which is a lower quality than the original. We lose some information. We lose information, we lose quality. Lossless compression is when we take our input, we compress it, and when we decompress it, we get the exact same as what we had originally. We don't lose any information, and we don't lose quality. What does zip use? Lossy or lossless? Why does it use lossless? Because when you unzip, you get the same information. With zip, you take a file. You use zip, which defines a compression algorithm, and you get a smaller file. When you decompress, you get the exact same original file. You don't lose any information. Lossless. MP3. MP3 is used for compressing audio, encoding and compressing audio. What do we use? Lossy. If you take a normal CD, not an MP3 one, but a normal original CD, and then you take a song on that and you encode it using MP3, then the size of the file will be much smaller than the original file in the wave format on the normal CD. Because MP3 takes the original input, it reduces the size, throwing away some information. We lose some information. So when you decompress, when you play it back, you do not get the original input. The quality is reduced, but the advantage of lossy compression is that it can compress much further. With lossless compression, the amount that we can reduce the size by is much less than when we use lossy compression. For example, what is MP3? What's the typical length, say a four-minute song? Anyone guess how big an MP3 is, approximately? Four-minute song? How long are they? How many megabytes in a four-megabyte? About... Okay, let's say five to six, three to four, about, let's say, per minute. With an MP3, say for each minute, you have about one megabyte to store. Approximately, it depends upon the algorithm used. So if you have a four-minute song with an MP3 compression, it's down to four megabytes. What if you don't use a compression? How large would the song be if you record it from a CD? Well, for a single song of four minutes, it's, in fact, using this rate. What a CD does is it uses 44,000 samples per second, 16 bits per channel, two channels, it's stereo. 1.4 megabits per second, four minutes is 240 multiplied by that, which is, what, 360 megabits, which is 360 megabits, which is 45 megabytes. Take a four-minute song, and from an original audio CD, and save it in a wave format, and wave typically uses, well, you can use these parameters for the sampling rate, and you'll get a 45 megabyte file. Take that song and compress it using MP3, and you may be down to four megabytes, about 10% of the original size. So without compression, with MP3 lossy compression, about 10% of the original size in this simple example, which one's better quality? CD or MP3? A CD, which is the wave file. There's no compression there. It's the original quality. With MP3, the quality is reduced, but the file size is significantly reduced. So with lossy compression, the amount that the final size is compared to the original is in the order of 5% to 25%. Here it was 10% in our example. It depends upon the input and the algorithm. So you take a 100 megabyte file, you may be able to reduce it down to a 5 megabyte file, depending upon the algorithm. With lossless compression, you compress much less. That is, you take a 100 megabyte file with lossless compression, maybe you end up with a 50 megabyte file, 50% of the original size. An example of lossless audio, anyone who gives me an example of lossless audio can leave and finish for the day. Lossless audio. What's an algorithm or a codec? Anyone? Okay, FLAC. You may have seen that. Apple has one. I think it's called ALAC. Free lossless audio compression. I think Apple, lossless audio compression. They take the original file, say our 45 megabyte file for our 4-minute song, and they compress it. And let's say they compress it down to half the size, 22 megabytes. But when you play it back, the quality is the same. It's identical quality in the playback to the original. There's no loss of information there. So FLAC is an example of a lossless compression algorithm. We don't lose quality, but of course we end up with file sizes larger than if we use lossy compression. So that's the trade-off there. You can go, and everyone else can go. Let's stop there. And what we'll do next week is we'll give a demonstration of these, some of the different codecs, over the, here's a list of some of them. I'm sure you've seen a lot of these and many others. Have a look at your media files at home, your movies, your music. Usually you can see the properties and see what codec they use, see what data rates they use, see the different parameters. So have a look at some of your own files and see what algorithms, parameters they use. And we'll summarize on them next week.


Mostly not. The sound samples you can find online try to simulate what a cochlear implant sounds like, but they often rely on inappropriate technology and depict the sounds as quite robotic and noisy. Our research shows that, thankfully, they don't come anywhere close to what a medial cochlear implant really sounds like. Well we have some really interesting research on this from CI recipients with single-sided deafness. This shows that users often describe their cochlear implants as sounding somewhat muffled and especially recipients with shorter electrodes even frequently describe their cochlear implants as sounding a bit like Mickey Mouse. Thankfully that isn't the case with medial users because the design of our cochlear implants mimic natural hearing. There are even some medial recipients who don't notice any difference between the normal hearing ear and their implanted ear. One major factor is the length of the electrode. Every tone has a specific place along the cochlear so it is essential to stimulate the entire cochlear with an electrode array. Using a shorter electrode often results in sounds being tinier and less full and voices that sound high-pitched. Another important factor is sound coding. Sound is processed differently deeper into the cochlear so to follow natural hearing sound coding needs to take this into account which our technology does. So medial cochlear implants which have both long electrode arrays and the right sound coding are able to offer the closest to natural hearing to our recipients.