If we want to, we can structure the market differently. In this session, I want to talk about high-end professionals, people like doctors, dentists, lawyers, other professionals that are highly paid. Just to be clear, these are not, by any means, the wealthiest people in the country. These are not the people getting tens of millions, hundreds of millions, billions of dollars.  Okay, so the points I want to make, first off, say a little bit about where these people fit in the income distribution. Secondly, that our trade deals were designed to lower the pay of manufacturing workers, to subject them to international competition. So that was a conscious choice. We did not do that with high-end professionals. Okay, and lastly, there's also domestic protections, licensing regulations that also work to benefit high-end professionals that a factory worker doesn't benefit from. So again, the whole point here is we structure the labor markets to benefit those at the top, and instead, if you look at people lower down the income later, we structured the labor market to subject them to competition in ways that lower their pay. Okay, so where are they in terms of the 1%? So I'm picking on physicians, lawyers, dentists, because there's a lot of them. How many of those are in the 1%? Well, about 200,000. 27% of them were in the top 1% of the income distribution. The ones that aren't in the top 1% for the most part, they're in the top 2%. So these are people very much at the top end of the income distribution. Lawyers, similar story, they're not quite as rich, 14.6% of lawyers work in law offices. That's a pretty good number of lawyers, again, in the top 1%. Most of those who aren't in the top 1% are in the top 2% or 3%. So they're very much at the top end of the income distribution. Dentists, 26,815% of dentists are in the top 1% of the income distribution. So a large share of these people show up in the top 1% of the income distribution, again, in all cases, if they're not in the top 1%, they're probably in the top 2% or 3%. So these people are very much at the top end of the income distribution. At the high-end specialties, you're looking at people who are getting half a million a year or something pretty close to that. So you get lower-down family medicine, 236,000 a year, pediatrics, 221,000. So these are more generalist, obviously lower-paid, but, again, still enough to put these people in the top, say, 2% of the wage distribution. Just to be clear what these numbers are, these are supposed to be net. So this is what they're pocketing after they pay out whatever office staff they might have, whatever they pay out from all-practice insurance. So these are pretty high numbers, I think, by most standards. And this was a study done a few years ago. I was looking at orthopedic surgeons. These are $2008, so you'd want to multiply that by about 50% to put it in today's dollars. But the point here is that when we're looking at other wealthy countries, so I'm not looking at what physicians get in Mexico, in India, that would be much, much less. These are other wealthy countries, countries we think is comparable to the United States. So in Australia, an orthopedic surgeon gets about 42% of what an orthopedic surgeon gets in the United States. Canada, about 47%. My kingdom's closest, 73%. Most of their physicians actually get paid considerably less, so I don't know why with orthopedic surgeons they're doing well. Now one of the points, I also have density relative to the U.S. here. We actually have many more specialists in the United States than in other countries. Most other countries have somewhere around two-thirds of their doctors are generalists. In the United States, it's about one-third. So again, a lot of people will point out, a lot of doctors will point out, well, we're specialists, we have more years of training. That's interesting and worth noting, but to my view, we should think about this. We care about outcomes. So is it the case if I have, say, a hard issue, I'm much more likely to go to a cardiologist in the United States than a generalist than compared to other countries? So a lot of generalists in other countries are dealing with issues that you would see a specialist for in the United States. Well, that would be a good thing seeing a specialist if we had better outcomes. But there's really very little evidence to suggest that we're actually getting better outcomes. So people with heart disease don't in general do better in the United States. People with foot problems, seeing an orthopedic surgeon, they don't generally do better in the United States. So again, we're telling a story, we're paying more money, the person might have more training, but it's not clear we're getting any benefit from that. So there's no reason for me to be happy that my doctor might have had four or five or six years of specialized training if that doesn't improve their ability to treat me. So the evidence is I'll say it best mixed that seeing more specialist means that you get better outcomes. Now taking the other side of the story, very perhaps tragic story in some sense with manufacturing, there used to be very substantial wage premium for manufacturing relative to other workers. And the reason why that mattered was that the high paying manufacturing helped to raise wages really for non-college educated workers more generally. So people knew that if they got a job say at General Motors in the auto industry or in the steel industry, that would be a good paying job, middle class job. That became much less true over the last four decades. And you can see this very clearly here. The trend for production on supervisory workers changed in manufacturing relative to the private sector as a whole. And what you see is that there was a wage premium about 10 or 15 percent if we go back to 1980 where manufacturing workers got paid substantially more than the private sector average. That's gone the other way where by this measure it's about 10 percent less as of 222 than in the private sector as a whole. And I should add another important part of this story. There's a big gender issue here because there's no doubt about it. The vast majority of the people in manufacturing, this was true in 1980 and still true today. The vast majority of the workers in manufacturing are men. Typically spouses would be working as well. But the main income came from the male worker and in a factory. Working in a factory might be able to support a family. You can't tell that story, which isn't to say it's good that women were discriminated against or whatever. I'm just giving the reality about manufacturing jobs. And that helped boost the pay for non-college educated workers more generally because these were opportunities that existed. OK, so why did that happen? There's two stories that economists will tell. One of them is that, well, manufacturing is just a declining share of total employment. Here's the data I have going back to 1970. And you can see there's been this consistent decline in manufacturing employment as a share of total employment. And people tell this story, well, that's just technology. More productivity growth tends to be higher in manufacturing, which means you need fewer workers to produce the same amount of output. So that's been a long-term trend in the last 50 years. That's the story. Also, obviously, we're spending more money on services. All that's true, but it's also deceptive. Suppose we don't look at shares. We look at actual levels of employment. Well, what you see is that there was relatively little change in manufacturing employment over the year. It goes down a little bit, but basically what you see are cyclical ups and downs. So there's some downward trend, but it's very, very modest. Then you suddenly see a huge fall off in the period from the late 90s till even prior to the Great Recession. We can see clearly the Great Recession begins 2008, even prior to that. Well, why is that? Well, that was the period when the trade deficit exploded. What do you have here? We see a loss of over 3 million manufacturing jobs, roughly 20% of total manufacturing employment, in the period from 2000 to 2007. I'm picking 2007 because that's before the Great Recession. And that coincides with an explosion in the trade deficit. What's going on here is that manufacturing is declining as a share of total employment because we're seeing more employment outside of manufacturing, but the big fall in the number of jobs in manufacturing coincides with this explosion in the trade deficit from 2000 to 2007. And again, we see a further drop as we get into the Great Recession. We've recovered somewhat from that, but certainly nowhere close to where we were in 2000. So the absolute number of jobs in manufacturing is plummeted. And then on top of that, it's put downward pressure on the wages of manufacturing workers more generally. So trade has been a very, very big part of the story of both employment and wages in manufacturing. Okay, the next point, compensation and manufacturing. This just is a comparison with other countries that I think help makes the point. So this is looking at 1997. So before the big explosion in trade deficit, if we look at where the United States stands relative to other countries, this is hourly compensation, so this is including benefits like health care, retirement benefits, whatever other benefits workers get. The United States is pretty much in the middle of this group. So Canada, Netherlands, Germany, again, these are wealthy countries we think is comparable to the United States. So we're pretty much in the middle of that group. Okay, well, where do we stand in 2011? Okay, we're at the bottom there. So we see that in this 14-year period from 97 to 2011, coinciding with the explosion in the trade deficit, our manufacturing workers lost a huge amount of ground relative to manufacturing workers in other wealthy countries. Okay, so why is it that our highly paid professionals didn't see the same impact on their wages from trade? Well, we didn't design our trade deals that way. Our trade deals did very little to free up trade for highly paid professionals. So our trade deals were very consciously designed to make it as easy as possible to import manufactured goods from Mexico, from China, from other developing countries with the idea those goods would be cheaper but also would put downward pressure on the wages of manufacturer workers in the United States. We didn't design our trade deals the same way with doctors, with dentists, with lawyers. With doctors, we could have said, okay, there's all these really bright people in India, why don't we make it possible for them to study to our standards and then come practice medicine in the United States? We'd do all these extra doctors here, lower the pay of doctors, make health care costs cheaper. We didn't do that. We have a requirement that to practice medicine in the United States, doctors have to complete a U.S. residency. So this means doctors not just in India but even in Germany and France, countries we think is comparable to the U.S., they have well-trained doctors, they can't just come and practice in the United States, they'd get arrested. They have to complete a U.S. residency. That's a serious protectionist barrier. And why is it there? Well, doctors don't want competition. So they require that you have to complete a U.S. residency. And I should also point out, the number of residency slots are very limited. So you have a lot of doctors, a lot of people that train as doctors that would actually be happy to come and do a residency program. We very much limit the number of residency slots and we limit the number that are open to foreign medical students. So even if you're prepared to go through that barrier, it's very, very difficult. Okay, we also limit scope of other healthcare professionals. Okay, so there are other healthcare professionals, doctors, physicians assistants, nurse practitioners, highly trained professionals. And in many cases, they're probably better for specific purposes than doctors because they see more cases. And they're very much constrained by licensing requirements and what they could do. There have been some good studies that look at issues like nurse practitioners being able to prescribe drugs without the supervision of a doctor. And the evidence is that they don't make mistakes. They're not prescribing crazy painkillers to people who don't need them. But we have restrictions in most states that limit the practice of nurse practitioners, physicians, assistants, other professionals who, as I say, in many cases will be as good or better than the doctors who are treating patients. We also allow the physicians to largely set Medicare reimbursement rates. Again, this is one of these things that when you look at you go, this is kind of incredible. So how does Medicare set reimbursement rates for surgeons? Well, they have a panel which is largely surgeons that decides on the reimbursement rates for surgeons. Well, there might be some conflict of interest there. Well, who cares? So that's how they set reimbursement rates for surgeons, for orthopedics, for cardiologists. It's set by panels that are largely composed of those specialists. Well, the argument is, of course, the specialists know what's involved. Well, that's true. But they also have an interest in getting high reimbursement rates. Now, that's a big deal both because Medicare is a huge program, but also because Medicare reimbursement rates act as a basis for private insurers. So most private insurers look at the reimbursement rates under Medicare, and they set a reimbursement rate usually somewhat higher, say 10%, 15% higher. But that's the basis for the vast majority of reimbursement rates throughout the medical profession. So the fact that you have boards that are largely composed of the specialist, in effect determining their own rates, well, that's going to put some serious upward pressure on those rates. So they don't face the same sort of competition. I always say it'd be like if the auto workers had a board that determined the pay of auto workers, well, they probably get paid a lot more money. They don't have that. Doctors do. Dentists, a similar story. Dentists have to complete a graduate program at a US dental school. They're good graduate programs in dental school in Germany and France and UK elsewhere in the world. Well, that's not good enough. You have to complete a program at a US dental school. So again, this is a classic protectionist barrier. But trade agreements, we want to make it easy as possible to import cars from Mexico and other countries. We didn't go in there and our trade deals go, well, we want to make sure we can get dentists, make sure they meet our standards, but we want to make sure that once they meet our standards, they can come and practice in the United States. We did not do that. Similarly with dentists, limiting the scope of practice at dental hygienists, that most people you see a dentist, at least my experience has been, most the time you're seeing a dental hygienist, they clean your teeth, they do the x-rays, the dentist comes in maybe for a minute and goes, how are things going? Well, most states, maybe all states, very much limit the scope of the practice for a dental hygienist requiring that they work under a dentist. So they have to have that dentist there. They can't just clean my teeth and do the x-rays. They get arrested. They need the dentist there to supervise them, even though their supervision may be pretty much meaningless in many cases. So again, that's a way of ensuring that they have more work, they have higher income. Lawyers, bar requirements, state versus national. If you want to practice law in New York as opposed to California, you have to take a separate bar exam. That's something that, again, we could have standardized. States could have higher requirements if they like, but they could say, okay, New York, California's known to have the hardest bar. So if you want to practice in California, previously they've been practicing in New York. You have to take some subsection of the bar. I may not have to get into details here, but the point is there are ways to rationalize this. Still allowing states to have their own laws. They don't have to have all the same penalties for robbery or whatever it might be. But you could standardize the system so that if you pass the bar anywhere, you could practice everywhere. That's a simple enough thing to do. I should also point out in California, they quite explicitly make the exam harder to limit the competition. Second category, unnecessary complexity. There's a lot of things that we do in our lives. Ride a wheel, we take out a mortgage, you have a divorce. These are very standardized processes where you could limit or radically reduce the need for lawyers by standardizing the process. Now I will say a lot has been done in that area over the last three or four decades. A lot of states, you could just download a wheel, more standardization with divorce. Mortgages, at least my experience has been every time I've taken out a mortgage, I have to have someone sit there and pay them a lot of money to be the lawyer when I'm taking out the mortgage. They don't do anything, but they get paid for that. So the point here is just that there's unnecessary complexity really in the vast majority of cases. We should simply be able to download the forms from the web and that's the end of the story. We shouldn't have to pay a lawyer for that. Also, lawyers benefit a lot from rents elsewhere in the economy. So intellectual property. This is the most rapidly growing area of law. It's also the most highly paid. So you have massive payouts to lawyers' law firms from all these various intellectual property issues. So a few years ago, every time Samsung or Apple came out with their new smartphone, the other one sued. So they would sue for patent infringement and it would take years and years. They'd always settle because they both had complaints against each other and maybe some money would go back and forth. But the point was there was a huge amount of legal expenses involved in that. So a lot of lawyers got very rich over Apple and Samsung fighting. And just to be clear, our point here is that it didn't do anyone any good. It's not good for the economy. Apple comes out with their new phone if you like the Apple go and buy it. Samsung has their new phone if you like that, go and buy it. That should be the end of the story. But because we have patent issues, there was a lot of money spent on fighting between the two of them in court rather than fighting between the two of them and who produces a better phone. That's where we want the competition, not who's got the better lawyer. But in the fight over who's got the better lawyer, a lot of lawyers got rich. Tax avoidance is another area that because our tax code is so complicated and it's particularly on the corporate side, we have a lot of lawyers who get very rich finding clever ways to keep companies from paying taxes. So again, if we had a more simple tax code, then you would get rid of a lot of work for lawyers. Which again, if we think about how we want the economy to be an efficient economy, we don't want lawyers except where we absolutely need them. That's not to attack lawyers, but it's just the reality. We want an economy where we need as few lawyers as possible. The fact that we need lawyers is a problem with our economy. We want to minimize our need for lawyers. Okay, so how much savings would we get from lowering pay and I'm picking on physicians because they're the highest paid profession and there's a lot of them. How much money would we save if we got the pay of physicians more in line with other rich countries? So I'm just throwing out a hypothetical. Suppose we reduce the average physicians pay by 100,000. So if we go back, the average physicians pay is currently about 300,000 a year. So if you lower their pay by 100,000, this doesn't mean every physician. So you wouldn't lower the pay of a family practitioner by 100,000, but you might lower a cardiac surgeon by 120, 130, 140,000. Lower it by 100,000 on average. What would that mean? Well, the savings would be about 90 billion a year. Okay, how large is that? Well, it's a bit less than half a percent of GDP or I'm putting up my friend here, the food stamp budget. That's about 100 billion, 105 billion a year. So it gets us most of the food stamp budget. So we would pay for the annual food stamp budget with our savings from bringing our doctors pay. It'd still be above the pay in other countries, other wealthy countries, but bringing it somewhere closer in line with what physicians get paid in France, Germany, United Kingdom. So there's a lot of money there. That's the point. It's a substantial chunk of money. Okay, well, the effect of trade on manufacturing. This again, to my view, it's kind of silly that we even have this argument. There are a lot of economists who are really invested in the idea that, oh, we could have more trade with China, more trade with Mexico, and it's not gonna affect the pay of manufacturing goods. Well, that was, to my view, just completely disingenuine. And part of the story here is that in economic theory, that was a predicted outcome. Putting up Stoper-Samuelson, because it's a very famous theorem, 1941, point is it's been around a long time. This isn't something that some clever grad student figured out a few years ago. Paul Samuelson, the Nobel Prize winner. He's the perhaps the most prominent economist in the United States. This is a well-known theorem. So what does it say? It says when you open up trade with two countries, or it could be multiple countries, that the country that the scarce good in one country will become cheaper and the relatively abundant good will become more expensive. So in the case of the United States, the scarce good, you could think of this different ways, could be capital, could be less skilled labor, that will become cheaper. So in other words, the work of manufacturing workers will become relatively cheaper because less skilled labor, less educated labor, is scarcer in the United States compared to, say, China or Mexico. So it wasn't like, oh my God, this happened out of the blue. Who could have imagined? It was predicted, well-established trade theory that this would be the result. So that should not have been a surprise to anyone. Now that's if you just had complete free trade. So suppose we said, okay, doctors in India, they could meet our standards, come practice the United States. We did that with every country in the world. We had a clear way they could meet our standards practice the United States. That would still be the outcome. We'd see manufacturing workers' wages fall in the United States relative to pay of doctors. But it's actually worse than that because we didn't have complete free trade. What we did was we subjected our manufacturing workers to as close to complete free trade as we could while we maintained the production for the most highly educated workers. So is it a surprise that the pay of manufacturing workers and less educated workers more generally fell relative to the pay of doctors and lawyers and other highly educated professionals shouldn't be a surprise at all. 100% predictable and of course that's what actually happened. Okay, so point here, policy choices. That this wasn't any sort of natural development. We adopted trade policies, put manufacturing workers in competition. Okay, that was a policy choice. And again, we did not do that with more highly paid professionals. The loss of millions of good paying manufacturing jobs put downward pressure on the wages of less educated workers more generally. Doctors, other highly paid professionals, they didn't have the same issue. They had both protection from foreign competition. As I pointed out, they also have a lot of protection in domestic competition. So point here is people want to attribute this to technology. That's just not true. It wasn't just that technology changed and suddenly the work of auto workers, steel workers, so much less valuable, we rigged the deck. Okay, so we made it so that workers with less education, manufacturing workers, they would get lower pay. We protected our most highly, highly paid professionals.


Hey, I'm hearing a lot about license plate readers. What are those? Oh, automated license plate readers, also known as ALPRs, are cameras that record vehicle movements and upload that information into a searchable shareable database. They kind of look like Vs. Wait, so a local cop can just type in my license plate and see everywhere I've been? Yes, but not just local police. Your travel history could be shared with hundreds and hundreds of other police and sheriffs nationwide. Wait, but that sounds really creepy. Oh, it's definitely creepy. EFF has been exposing how invasive they are for a while now, but we did have a pretty significant win against them in California a few weeks ago. The California Attorney General issued a legal interpretation saying that it is against the law for police to share collected data from license plate readers without of state federal agencies. Okay, I like the sound of a win, but what does it actually mean? This means that immigrants, abortion seekers, protesters, and everyone else who drives a car in California can stress a little less about these surveillance cameras exposing intimate details about where they've been and where they're going. We're still urging California and other law enforcement agencies around the country to limit their ALPR programs altogether, but this is a step in the right direction for protecting everyone's privacy.


everyone. Today we are going to learn about cooperating processes. The learning outcome of this session are student will be able to define cooperating process concept and illustrate producer consumer problem. Let us recall what is process? As we know process is nothing but program in execution. Many type of processes execute concurrently in the system. So, there are two types of processes. Independent process and dependent process. Independent process cannot be effect or be affected by any other process. And these are the processes which do not share any of the information with any other process. Whereas dependent process which is also called as cooperating process share information with other processes and it can be effect or be affected by any other process executing in the system. So, cooperating processes share information with each other. Let us see the advantages of cooperating process. Cooperating process allows information sharing as many users require same type of information to be shared. So, cooperating process allows such type of information to be shared between many users. For example, file or variable. Computation speedup. Suppose we have a task which is to be executed in faster manner. Then that task can be divided into sub-task and that sub-task can be run in parallel to get the result faster. So, in this way the cooperating process support computation speedup. And communication between the processes allows to get the result from that sub-task and computation speedup can be achieved. Modularity. We want to design a system in modular fashion. So, cooperating process allows to design a system in modular fashion by making it more convenient. Making the system inconvenient means it makes user to make it easy to use and allow different type of information to be shared with each other. For cooperating process inter-process communication model is required. So, that inter-process communication model allows cooperating process to exchange information with each other. There are two fundamental model of inter-process communication. They are message passing system and shared memory. In message passing system the processes can communicate with each other by exchanging messages. While in shared memory the processes can communicate with each other by using a shared region. A part of memory is shared between the processes which wish to communicate with each other. And using that shared memory region process can exchange information with each other. Message passing system is useful for exchanging smaller amount of data. While shared memory is used to exchange information which may be in big amount and shared memory gives maximum speed and convenience and it is more faster than message passing system. Because message passing system is implemented using system calls. So, it is quite time consuming. While in shared memory system call is used to establish only shared memory region. Once the shared memory region is established there is no kernel intervention when process is communicating with each other. And that is why shared memory is more faster than message passing system. Let us see one example of cooperating process that is producer consumer process. In producer consumer process producer produces an item which is stored in a buffer and consumer has to consume that item from the buffer. For example compiler may generate assembly code that is consumed by a similar. A similar in turn may generate object code that in turn consumed by loader and there is one more example of producer consumer that is server and client. Where server produces an HTML files and images which is consumed or read by the client web browser by where the client has requested that pages to the server. So, server and client is also one example of producer consumer process. Producer and consumer process must be synchronized. The producer can produce one item while consumer is consuming another item. They must be synchronized so that the consumer does not try to consume an item that has not yet been produced by the producer process. Now just pause a video and just think that which communication model is required to implement producer consumer problem. Write down your answer. Let us see as we know there are two communication model message passing system and shared memory. So, to implement producer consumer problem shared memory is used. Shared memory is nothing but it is a buffer. Buffer is a region which is to be shared between producer and consumer. It is nothing but a shared memory which is shared between producer and consumer process. The size of the buffer must be fixed or unbounded. So, unbounded buffer places no limit on the size of the buffer where only consumer process will wait till there is no item in the buffer. But producer process will never wait because there as size of the buffer is not fixed producer process goes on producing an item. While in bounded buffer size of the buffer is fixed. So, in bounded buffer the consumer will wait if there is no item into the buffer and the producer will produce and producer will wait if buffer is full. Now let us see the bounded buffer producer consumer problem. The common data structure between producer and consumer process are shown here. We can see that buffer size is equal to 10 and it uses two variables that is in and out which points to the in points to the next free position in the buffer while out points to the first full position in the buffer as shown in the figure. Here we have buffer with size 10 and in and out both point to the first position into the buffer currently. Now let us see the producer process. This is the code which is to be executed by producer process. Now suppose we have a buffer with size 10. Now here initially value of in is equal to 0 and out is equal to 0. Now it will we will check the while loop while in plus 1 mod buffer size. As we know more gives remainder value of in is right now 0. So, 0 plus 1 that is 1 more buffer size is 10 1 mod 10 gives 1 value of out is 0. So, 1 equal to equal to 0 this condition is false because 1 is not equal to 0. As condition is false we can say that their buffer is not full there is a space in a buffer. So, producer process will produce an item at the first position into the buffer. So, we can see here the producer process produces an item a 1 into the buffer at position 0 and value of in is incremented while the value of out is as it is that is out it as position 0 and in is incremented. Once in is incremented again this file loop is checked 1 plus 1 mod 2 mod 10 which is 2 is not equal to 0. So, again the next item is produced into the buffer at next position. In this way this loop goes on continue and producer process will produce an item as shown in the figure in always in the next position. Whenever value of in is incremented the value is produced into the next position into the buffer. Now, when in reaches to the last position that is at position 9 again the while loop is checked that is here value of in is 9 and out is 0. So, in this case the it shows that buffer is full because 10 mod 10 is equal to 0 0 equal to equal to 0 means buffer is full. Now, in this full condition says that producer process should wait because there is no space into the buffer this is what the producer process. Now, let us see the consumer process initially there is no item into the buffer. So, here producer process will wait now suppose consumer process has to wait. Now, suppose we have a buffer with items produced by the producer process. So, in this case the value of out here at position 0 while in is at position 9. As in is not equal to out consumer process will consume an item out is equal to 0 that is next consume it will consume an item from the next position into the buffer and value of out is incremented as shown in the figure. So, out is incremented and each one by one item is consumed by the consumer process. Once the out consume all items and it reaches at the position 9 here in is equal to out is at position 9 which shows that all items are consumed and as in is equal to equal to out it shows empty buffer. So, producer process consumer process has to wait here consumer will wait. Now, the producer consumer synchronization suppose producer and consumer is executing simultaneously initially there is no item into the buffer. So, producer will produce item at position 0 and increment the value of in it will place an item a 2 into the buffer at the same time consumer process value of out is equal to 0. So, it will consume first item from the buffer and increment the value of out producer increment the value of in produces an item consumer simultaneously consuming an item in this way the producer and consumer are synchronized. So, synchronization must be there in producer and consumer problem. So, this is how the synchronization takes place these are the references thank you.


All right, we've got some technique about the star here. What's going on man? What are you doing here at the C3? I'm just coming in here supporting the cause. Cliff is a great friend of mine And he's been you know a big influence and me and my wrestling career and my ventures and my everyday life So I why not come and support this pretty cool scenario that's going on here. He said you get some Nike stuff going on What's going on here? Yeah, so I recently left ASIC So I was a global product line manager for ASICs and one of the cool things that I got to do Within ASICs was develop the wrestling global wrestling shoe line and now I moved over to Nike and You know, I can't really touch base on what exactly I'm gonna be doing because it's all confidential But you guys will see some stuff coming out pretty soon. Jordan Burroughs said a lot of good things about you Said that he was really worked a lot with you interactively on the shoe that he did. Yeah You know now that you're gone, that's probably gonna be a little different for him. Yeah, I don't know what's gonna happen there I know that when I Talked to Jordan about my departure. That was one of the conversations we had I'm very confident that ASICs in general will make sure that he's well taken care of There's some people behind the scenes that will make that happen I know Jordan I have a really tight connection because I wrestled he wrestled and having somebody with That background work on his shoe Really kind of brought it into fruition on what he wanted kind of his suit to define right Jordan Burroughs and his design and colors So I don't know how much influence he's gonna have moving forward But like I said, I think you know ASICs will definitely make sure that he's well taken care of But under you a wrestler he had a ton and that's yeah talk about wrestlers Wrestlers like dealing with wrestlers. Yes, no matter what we like dealing with one another. Yeah, I see it here It's cauliflower broken. I know that that's a person who I can I can deal with them And then I've got a brotherhood with kinship You know looking at that I mean he couldn't say enough good things now the creative You know it's gonna be a little different, but how can you take what you did at ASICs over to Nike and and get them to the next level? Well, it's just influence. I mean, you know, I learned a lot from ASICs and a lot of people within the ASICs Organization, so I'm gonna utilize that skill set to move and help and help, you know, move the needle for Nike I know they made a huge commitment with USA wrestling and I think that's cool I think it's it's better for the sport of wrestling when you have multiple companies, you know for actively Investing because now it makes everybody step up their game and you're right I mean the influence that we had with each other within Jordan and myself and not even Jordan and Adeline Grey as well and Gable and It just speaks volume that you know when you when you have the same lingo and the same terminology As a sport of wrestling it just it's more comfortable to deal with somebody like that, you know I look at Nike on two three years ago was on life support with wrestling They were they were gonna cut the wrestling line if you remember, I don't know if you remember Yeah, they were done. They were done with wrestling and then they just got a shot in the arm No one that they got that shot in the arm. They got this deal with USA wrestling What's that mean for you building momentum as a wrestler as a you know a wrestling guy going into Nike? What's that mean for you? I mean, it means a lot right first of all I mean security and for rider for my family, but like I mentioned earlier, I think when you have more companies Really competing it's better for the sport. It's better for the athlete Just it grows the sport in general because now instead of having one brand Really being that only one brand that support the sport now you have multiple brands And I feel like everybody it makes everybody step up their game and ultimately the consumer will be you know The benefit the largest beneficiary of that they have a signature shoot a while Nike has not done with an athlete Right. Okay. The last one I want to say collect Call that yep, what's the last is that something where you know, you can't really speak to what you're doing But is that something that you would want to do we get do we see guys with a shoe signature shoe line? That's something you'd be interested in. I mean, I would encourage it. I think it's important to know that you know the Investment that they've done with USA wrestling is huge right and I think an investment on a signature athlete It's even you it's even bigger because now these kids You know we have a bunch of kids here that are looking to go to the next level and they look up to the Jordan boroughs They look up to I don't like right a female perspective wise I think it's important that they do that because it just it's gonna stick to volume It's gonna really show that individual consumer.  I don't know if I could have a better guy that guy right there He's phenomenal. He's a great face. I wouldn't say I Jordan boroughs. He's probably the next guy that I would say kids that are gonna look up to because he's just he has a great story He's well spoken. He's charismatic. He's a great dude He's come from nothing, you know, and And Went over a lot of obstacles to be where he's at and I feel like a lot of especially in this world a lot of kids can really You know, and what James got on his side is he's out of college where Kyle Snyder's got a way to another year You know, that's what James got going for him. He's got time is on his side at least another year But I tell you that Kyle Snyder is gonna be the best thing to come out and in terms of signature shoes I'm gonna be surprised if Soon as his collegiate Careers over if he doesn't have a signature shoot by the end of the month. It's gonna be surprising to me I'd be a travesty to me man. That guy's solid people too, right great individual Working with wrestlers when they see you and they know that you're in credibility with your Relationships that you hit and you know from ASIC and some of those aren't transferable obviously the Jordan relationship But business-wise you can't bring him over. What do you think guys think of and girls think of or in wrestling? When they get to deal with you, what do you think that does to the rest? Well, I feel that the relationship that I built in my previous world with those athletes You just mentioned it just gives me a lot of like it opens the door because you know, some people are very Cautious of opening up and letting themselves loose in front of other people that they don't know But the fact that I have that relationship with Jordan and Adeline and Gable and Rulan and Kale It just it just shows that the new athlete. Hey, this guy knows how to work with these athletes He knows in lingo. He's he's worked with all these other wrestlers and you know And I just I want to build great relationships with those guys as I am a Individual that I'm a fan of about the sport.   Yeah, that was that was Grant and that was Eddie From APS and I think you know those guys At that time even opened my eyes because I was with the competitive brand You know that they were trying to beat I think they're doing I think it's positive I think it's showing again. Hey, we're not afraid to invest and we're willing to make that investment and get an individual That's been a six for such a long time and moving them over right now I think it's key because you get these influencers to move over Everybody else is gonna follow And it just keeps volume of Eddie and Grant that they can they have that power that have that Mentality that they can go in there And take athletes like that away from me. Do you work here or do you work in Beaver's? No, I work out here in Torx. So do you have to go up the beaver?  I mean you got Oregon State right there Only division one college wrestling program You know, that's there's not a lot of division one college wrestling program How can you guys help promote college wrestling here in the West Coast? Well, I think that's that again it speaks to the the commitment with USA Wrestling, right? Because now they know that Nike has invested in the USA Wrestling contract That's gonna should trickle down to the collegiate style of wrestling and it ultimately help bring more more Colleges and universities to this quarter All right No, I appreciate this man, you know, I have four kids my wife was a women's all-american my four kids are wrestling and you know My youngest daughter is gonna be wrestling in a you women's nationals And I think bringing them to this environment and having them surrounded by individuals like Cliff and yourself and Mark and even you know, like We had we were fortunate enough to have dinner with the burrows is And like my daughter and still to this day talks about that I just want them to understand that what I do is for them as well It wasn't for the sport of wrestling I wouldn't be


A total of 375,270 Russian soldiers and officers have been killed in action since the outset of the full-scale invasion of Ukraine.That's according to the latest update by the general staff of Ukraine's armed forces. Also, Ukraine's defense forces destroyed 6,171 main battle tanks, 11,455 armored fighting vehicles, 8,868 artillery systems, 967 MLR systems, 654 air defense systems, 331 warplanes, 324 helicopters, 6,934 UAVs, 1,818 cruise missiles, 23 warships slash cutters, a submarine, 11,848 military trucks and fuel tankers, and 1,389 units of specialized equipment die in the past day alone, Russia lost 750 forces, the report reads. Russia has suffered huge human and material losses in Ukraine and its army will emerge weakened from the conflict. The Russian armed forces will emerge from this war weakened, both materially and in terms of personnel.


The start of the 2016 Minnesota deer hunting season is officially here. The governor's opener kicked off today and in just two days the real fun begins. The rifle season opens Saturday morning at sunlight and after harvesting nearly 133,000 deer last year, nearly 20,000 more than 2014, the DNR is excited for a big year this time around. But they say don't wait until the last minute to get your license. The Northwest region is going to have a pretty good deer season altogether. We've increased lottery permits in 184 around Bemidji here over what it was last year. If you're just now thinking about buying your license, you better get it as soon as you can. Tomorrow is traditionally our most busy day last year on Friday before opener or 94,000 license sold. Hopefully people have taken the time to check their deer stand, make sure it's still standing after the blows that we had this summer with the wind and trees knocked down and such. A little scouting just to understand how deer are using the area. We don't have the acorn crop that we had a couple years ago. So things are going to be a little different and just knowing the game is the best way to win at it. If you've enjoyed this segment of Lakeland News, please consider making a tax-deductible contribution to Lakeland Public Television.


Hello, and welcome to this session in which we will discuss the concept of change management. What is change management? This concept is typically used in an organizational change, such as implementing a new IT system or a software, restructuring the organization or introducing a new business processes. In this session, we would look at the process, the steps in a process, and in the next few sessions, we'll discuss more about risks involved in the change management and how to mitigate or to deal with those risks. But simply put, you are moving from a current state to a desired future state in some sort of a domain. And we're gonna be discussing specifically new IT or new software system. The goal of change management is to ensure that the changes that we are making are implemented smoothly, effectively, with minimal disruption to the organization. Because when a change is done, maybe you're gonna be some disruption, most likely will be disruption. Well, we're gonna minimize those disruption for our employees and most importantly for our customers. So this involved processes and methods for managing and controlling changes to a system or organization in a way to reduce or practically try to eliminate the impact of the impact on business operation and reduce the negative consequences. You don't want to tell your customers, well, I cannot deliver the product because I'm updating my system. That's not acceptable for the customer. The customer don't care whether your system is working properly or not. The customer is expecting the product on time. Therefore, effective change management would involve a range of activities we will cover next in an example showing you the process step by step. Before we proceed any further, I have a public announcement about my company, foreheadlectures.com. foreheadaccountinglectures is a supplemental educational tool that's gonna help you with your CPA exam preparation as well as your accounting courses. My CPA material is aligned with your CPA review course such as Becker, Roger, Wiley, Gleam, Miles. My accounting courses are aligned with your accounting courses broken down by chapter and topics. My resources consist of lectures, multiple choice questions, true-false questions as well as exercises. Go ahead, start your free trial today. No obligation, no credit card required. Now, why would a company, why would a company or an organization and for that matter an individual want to update, do some information system, IT infrastructure changes? We're gonna go over some reasons. Some of these reasons are obviously applied to you on a personal level, but let's go over them. Once is Ups or Lessons. What is Ups or Lessons in information system? Simply put, the software or the hardware that you are using become outdated. No longer meet the company's expectation. A case in point is Apple computers. When you have an iPhone, if you have an iPhone seven or eight, although the phone's still working properly, Apple will not allow you to update anymore. Apple will not allow you to put any more apps on it. Therefore, it becomes Ups or Lessons. So this might include hardware, software, Ups or Lessons that's no longer supported or updated by the manufacturer in this situation Apple or has been surpassed by newer, more advanced technology. So simply put, your technology is no longer good. I need to update, buy a new system or create a new system. The next reason is the most important is security. Well, if your system is not secure, it's worthless. And specifically cyber security threat because everything is on the internet are constantly evolving and companies need to ensure that their hardware and software are up to date to secure against potential attacks. So all their system may be more vulnerable to security breaches and upgrading might help mitigate those risks. So that's another reason why you will need to go through an IT update, which involves change management performance. As the company grows, the hardware of the software may change. And that's why you want a hardware and a software that can scale, that can grow with your business. But maybe you are not planning for that. So as the number of employees or customers, hopefully customers increases, the company will require more powerful hardware and software to handle the increase of workload. Also the cost is a factor in updating. If the cost of unmaintaining an outdated system exceeds the cost of upgrading, well, it's no brainer, go ahead and upgrade. And if you do that, you're gonna go through the change management. Comparability, that's kind of sense related to obsolescence. If a company needs to work with partners or customers who use different hardware or software, they may need to own a system that ensure comparability. In other words, your system is obsolete if you want to partner with someone else. For example, insurance company, they want to connect their system to the repair shops. Well, if the repair shop don't have a system that's comparable with the insurance company, they might have to upgrade. Also regulatory compliance, that's mandatory. That's what you have to do, the mandatory change, you have no option. In certain industries, you are subject to specific regulation, require compliance, to use specific hardware or software to ensure compliance. And here what we are talking about is the banking industry and the healthcare, among many others. But here we're talking about privacy issues, security issues you have to have, your system designed have certain features. So change management involves seven steps. Now in your CPR view course, in your CMA review course, or in some other course, they may make it eight, they may make it six, it doesn't matter. The point is there is a systematic way. The first step is in anything is to identify the need for the change. Why are you changing? Well, this may be driven by internal factors. The company decided to change because for the reasons that I mentioned on the prior page, they want to scale the business, improve efficiency, reduce cost or external factors, mainly regulatory environment. Well, let's assume a company may have identified the need to improve their customer service processes and realize that a new CRM system is needed. So if that's the case, we identified the change, we need a new customer relationship management system. Well, after we identified the issue, we plan for the change. Well, the plan should be developed to guide the change management process because it's a long process. The company will need to develop a plan for implementation for the new CRM system, including details such as the scope of the project, how long it's gonna take, resources needed because it's gonna cost us money, time, employees, so on and so forth. Three, we have to communicate the change. Now, we identified it, we planned it. There are people that need to be aware of the change. Effective communication is critical for the success of any change initiative. You cannot just wake up and say, tell your employees we're using this new system or you're telling your customers, all suppliers, you wanna communicate this information slowly and the more complicated and the more important that change is, the more that communication is. So the change of the plan should be communicated to all stakeholders, including employees, customers, suppliers to ensure that everyone understand the need for the change and the impact it's going to have. Step four is to assess and manage risk. Change management can be complex and carry a range of risks, which we'll talk about the risks in the next session, including operational disruption, increased cost and resistance to change. These risks should be minimized and managed throughout the change to minimize their impact. And we'll talk about how to manage this in the next session. Implement the change. Now you're ready to implement, to just put it into place. With the plan in place, communication completed and risk managed, the change should be implemented. Now you're ready to put it into action. This may involve deploying the system, the new technology, restructuring the team, if that's the case, or restructuring the department, implementing new policies and procedures. So the new CRM will be installed at this stage, employees will be trained on how to use it and the system will be integrated with other existing systems and processes. And always you wanna monitor and evaluate the change because once the change has implemented, you want to make sure it's effective and evaluate its impact. This will help to identify any issues or areas for improvement. Is it working as expected? If not, what areas do we need to improve? Because really, you don't know how good or not good a system is until you put it to work. And this will, you will ensure that the change is delivering the desired outcome. So here the company will monitor the effectiveness of the new system and evaluate its impact because this is a CRM on customer service processes and customer satisfaction. You'll have to capture that. The number seven is embedded the change. Finally, the change should be embedded into the organizational culture and processes. Now it becomes part of your system to ensure that it's sustained over the long term. You want the people to accept it and as a result to adopt it, work with it to improve customer relationships. So the company will embed the new CRM system into its culture processes, such as providing ongoing training and support for the employees on a regular basis, reviewing and redefining processes and continuously seeking to improve customer service because that's the whole purpose of the system in the first place. Now it's very important in the next session since we looked at the processes and we said there are risks in this process. We need to know how can we mitigate? How can we deal with that risk? But what should you do now? Go to Farhat Lectures and look at additional MCQs whether you are a CPA candidate studying for the CPA exam or a CMA candidate or taken an accounting information system or studying for some certification. Understanding change management, especially in the CPA exam 2024, that's gonna become a critical component of the exam. Study hard, good luck. I'm always here to help you and stay safe.


Welcome to this lecture on digital communication using GNU radio. My name is Kumar Appaya. In this lecture we are going to take a brief look at channel capacity. Channel capacity is related to the maximum error free communication that you can perform over a channel. We will largely focus on the binary symmetric channel, but briefly mention how these concepts can be carried forward to other channel types also. As an outline we will talk about rate and error free communication, we will briefly mention typical sets and their probability and then we will roughly specify the capacity of a binary symmetric channel and just mention the capacity for a Gaussian channel that is more practical and it is more related to the simulations that you have seen and summarize what the implication of capacity is in practical communication systems. When you look at rate for error free communication, so if you consider an NK block code as we have seen in the past several lectures and NK block code has a rate of K upon N. Now the block error let us say in a binary symmetric channel occurs if enough bits get flipped like the thing is that you have a noisy channel is a binary symmetric channel. If there are no bit flips there is no problem, if there are bit flips then you can potentially correct for those bit flips. For example, we have seen the hamming code as an error correction mechanism, we have seen the repetition code as an error correction mechanism and these codes essentially are able to flip the you know rather are robust to those bit flips and still say ok most likely this particular code was sent and therefore your K bit message is this. Now if hypothetically we demand error free communication, what does that mean? Error free communication is where you have a channel that introduces errors, but you have added enough redundancy so that all these errors can be handled. Let us take an example, let us say that you have a channel with let us say a binary symmetric channel with P is equal to 0.2, then the probability of each bit getting flipped is 0.2, but suppose that you use a code and reduce your rate and say use a repetition code and repeat everything 11 times then potentially the error rate can be reduced significantly the effective error rate can be reduced significantly because even though your channel is flipping bits the error control code is able to handle those bit flips, but the price you pay is that the rate is very poor in the case of 11 repetition code is 1 upon 11. The question that we are asking is what is the maximum K upon N that is achievable or possible as N goes to infinity. Now one key idea here is that we are giving a block length that is very very large that is I am allowing you to essentially send several bits a block of several bits and then asking you to give me the maximum possible rate. Now one advantage that you will see happening is that when you allow me to use the channel or send bits over the channel multiple times as you send bits over the channel multiple times what ends up happening is that there is a certain pattern in the you know pattern in the types of errors that the channel introduces and getting a handle on these patterns can allow you to design codes that can beat the errors that is basically the way you can get capacity. In practice if the block length is very high what is the maximum K upon N that we can get with an arbitrarily small error rate. Now practically as students of probability you know that whenever you have a binary symmetric channel with any non-zero probability of error for any finite N there definitely is going to be some you know some situation where a large number of errors happen irrespective of the code that you use you will always end up getting some decoding error of course. So let us change it a little bit suppose that if you want a practical idea suppose that I demand that the error should be less than 10 power minus 9 or less than 10 power minus 12 then maybe you may be able to design a code of course the easiest way is to just define a repetition code. The repetition code however is very poor because if you want an arbitrary arbitrarily low error probability you have seen that you need to increase N significantly and the rate is going to be 1 by N and as N tends to infinity 1 by N is going to be close to 0. I want the highest possible K by N which gives me error free communication with over a channel with errors. So let us look at a binary symmetric channel with error probability p. What are the most typical error sequences in N channel uses? Now this word typical has a special meaning or special connotation in the study of capacity typical does not mean you know typical means probable in the sense that it is not the most probable error sequence that is you know that if p is less than half then the most probable error sequence in a binary symmetric channel is the all zeros it is actually just so what we are asking is what is the most probable number of errors when bits are sent N times roughly. So let us actually just look at this in more detail let us look at this in more detail.  Let us now look at the binary symmetric channel with multiple uses meaning transmission of multiple bits. Let us say we look at 3 bits ok with 3 bits let us look at the error pattern I am deliberately going to look at the error patterns in terms of their hamming weight or the number of errors 0 0 0 0 0 1 0 1 0 1 0 0. Next I am going to look at 0 1 1 1 0 1 1 1 0 1 1 1. So if you now group this this is ok this is 0 errors this is 1 error actually till here this is 2 errors. What is the probability of these? The probability of this happening is 1 minus p the whole cube remember if p is less than half then among all these this will be the highest number. The probability of each of these p into minus p the whole square the probability of each of these is going to be I should have done this here and this is this is 3 errors of course 1 minus p and this has probability p cube remember if p is less than half let us say p is 0.1 then this is going to be something like 0.9 the whole cube is going to be 0.1 the whole cube and you are going to have a couple of more numbers which add up to 1 of course if you add up to 1 if you count the number of these sequences there 3 you know 3 times is 3 times this and this is the situation that we have the most likely is 0.9 power 3 ok yeah. Now, let us let me ask you a question let us say n bit error patterns let us actually now also write the total probability here ok let us not write it in terms of this number let us write the cumulative probability by taking into account all possible sequences just for the just for our reference.  Now, rather than target individual error patterns suppose we try to target the number of errors that the channel will introduce among multiple channel uses. In such a situation which is the most which is which is which one of these is the most likely is there is where the typicality starts occurring. In other words the most typical sequences are those where even if you look at large numbers of large block lengths where all the probability is for example 1 minus p the whole cube may be a large number, but 3 p into 1 minus p the whole cube may or may not be. So, for example, if you look at n bit error patterns ok let us say that we say 0 errors the total probability of 0 errors is 1 minus p power n 1 error 1 minus p for n minus 1 n times p ok. If you want me to be very formal you can write this is n choose 1 p power 1 1 minus p power n minus 1.  So, having an n which is large may actually cause a single bit single error patterns as a total we more likely than 1 the all 0. See the all 0 error pattern is definitely the most probable, but what is the probability of getting 0 errors as n goes to infinity 1 minus p power n is going to be 0 as n goes to infinity for any non-zero p. So, I am saying when you make the block length larger I am 99.999 percent or more almost 100 percent sure that I am not going to get all zeros what is the most likely group of error patterns that I can get and that is what I am looking for.  So, if you have k maybe k is not a good notation because we use that for our block length and everything. So, I will change it to let us say l errors let us just for you know just for here also let me change it to l errors. So, now if you think about this the probability of getting probability of getting l errors the probability of getting l errors not an a single l error sequence.  Now, this is could be you know n choose l times p power l times 1 minus p power n minus l maybe much more than 1 minus 1 minus p power n in which case this is probably what you need to target because as you make the block length larger and larger what are the most likely error sequences that is the question. To give you some intuition as to how this is going to work out let us say you have a biased coin let us say you have a biased coin let us say probability of heads is equal to p 1 minus p let us say. Property heads is 1 minus p then the probability of the sequence h h h h h h if you toss it n times is 1 minus p power n and if you now say probability of k heads let us say not k the gain I am making the same mistake let us say l tails n minus k n minus l heads this is n choose l p power l 1 minus p power n minus l. If I ask you the question that if I toss the coin n time where n is a number which is really large like 1001 million what is the l that is maximum what is the most likely number of heads that you will get intuitively this is a binomial distribution of course the PMF of binomial distribution I am asking you what is the peak of the binomial distributions you know in terms of l which is the most likely number of tails that you will get or heads that you will get and that is going to give you an indication. Because if you then know this pattern then these are the error patterns you can design your code so that you can have one code word and another code word which if you flip that many number of bits still uniquely decodes to the same code word what was the motivation with which we went to the hamming code one bit error pattern resulted in zero errors after decoding similarly if you know the number of errors we can always design our codes in such a way that you can always you can handle that many errors and that is where the capacity aspect comes in now the most probable sequence is the all zero sequence or you know all heads or no errors but the most probable number of errors is obtained by maximizing I've chosen k please bear with me n minus k p power k 1 minus p power minus k over k not k factorial over k and it turns out that k is NP why you can actually look at the binomial distribution and if you plot the binomial distribution you will always find that k being close to NP is the peak if you want a more intuitive answer you can think of the binomial as the summation of independent Bernoulli and if you keep adding independent Bernoulli then you from the central limit theorem you know that the mean is you know it's going to look like a Gaussian in fact if you plot a Bernoulli for a large n and the PMF of a Bernoulli for a large n it looks like you know sticks which resemble a Gaussian shape and the peak always happens close to NP the reason I am writing approximate is because k is an integer and NP may or may not be an integer but the peak always happens close to NP this gives you an indication of the fact that the most likely number of errors is going to be NP so the most probable group of errors seem to have or seem to produce NP bit flips but the problem is you do not know where those NP bit flips are because if you know where those NP bit flips are then you are done but your code which is consists of you know 2 power k code or 2 power k codes have to be in such a way that if you flip NP bits still you are able to map back to the same code word and not go to another more than NP is fine but if you flip NP code NP bits of a code word it should still be minimum distance to the same code word that is what we are going for now while this is the intuition typically the decoding process is not that simple for the for finding out capacity but let's go further what if we choose n length code word that separated sufficiently so that NP minute flip does not hamper decodability in the case of hamming code you said I want to separate them in such a to such an extent that one bit flip does not decode ham you know hamper decodability now it's NP now here is where we are going to actually just make a packing based argument the total number of code words okay total number of code words or other total number of sequences which are n bit is 2 power n remember n is a large number so you have total 2 power n sequences now we we said that let's take n bit code words in such a way that flipping them NP times okay flipping them NP times results in the results in some sense the same number of okay results in unique decodability so it's like this meaning there is a particular n length sequence if you flip any NP bits then you get several several several code words how many such code words do you get n choose NP okay roughly you don't believe me it's very simple take take any n length code word how many different n length vectors can you get by flipping NP bits it turns out that it's just n choose NP so it's just the binomial you just have to you know how many you know how many L bit flips can I perform n choose L into so now the number of code words sorry the number of code words that I can accommodate in this 2 power n space is going to be 2 power n divided by the ball which is of size n choose NP let's give you some intuition over here let us say this is the space of 2 power n n bit sequences this is a code word let's say we call it X any NP bit flips NP flips will result in several other n bit sequences and we want another code word to have another NP bit flips which cause this to not get confused with x1 similarly we have another x3 with another NP bit flips which you do not want to get confused with x1 and x3 so it's almost like you want to be the spheres to be packed in into this 2 power n bit sequences and they should just not cause any confusion so how many can you get 2 power n divided by n choose NP why is it n choose NP because each of these spheres has n choose NP elements in them because you said NP errors are most likely fine I'm just going to make sure that any NP errors does not cause any confusion so approximately 2 power n by n choose NP is the number of n length code words that you can support I have used sterling's approximation here sterling's approximation approximates you know n factorial for large n in fact it's proportional to n power n so if I write that I get n power n by NP power NP into n minus NP power n minus NP you may argue that NP is not an integer but for now bear with me approximate let's say that we have managed to choose a close enough P and such that NP is an integer then 2 power n by n power n in n power n by NP power NP into n minus NP power n minus NP is the number of code words if you just take this to the numerator you will you'll notice the n power NP and n power n minus NP result in power n n power n gets cancelled and you end up getting 2 power n into P into 1 minus P this is the number of code words therefore if you now use n block length that is n uses of the binary symmetric channel and you want to find out the number of code words that you have the number of such code words is 2 power n P power P 1 minus P power n minus P which means what is the rate that I get so this is actually going to be okay so for n for n length sequences you are able to send 2 power n P power P 1 minus P power n minus P the actual number of bits you're sending per channel use is 1 upon n and we just log we take log to the base 2 why do we take log to the base 2 suppose you have 4 code words you can send 2 bits suppose you have 8 code words you can send 3 bits suppose you have m code words you can send log to the base 2 m that's the reason why so we just take log to the base 2 of this and divide it by n because we want a per channel use or per binary symmetric channel kind of rate and this will turn out to be 1 plus P log P plus 1 minus P log 1 minus P therefore this is going to be it turns out the rate achievable over a binary symmetric channel okay let us actually do some intuitive calculation this P log P plus 1 minus P log 1 minus P is a number which you can confirm is going to be between I believe it will be between 0 and minus 1 in fact that's why typically they take a negative sign over here let's actually just do a small kind of operation let's just say 1 plus what happens when P is 0 when P is 0 okay then P log P right that's the tricky part P log P it turns out is like 1 log 0 and log 0 is minus infinity all those kinds of things are there we are just going to take P log P to be you know 0 over here so you get I'm going to call this R R is going to be 1 because I'm just going to take this to be 0 similarly if you say P is equal to P is a number less than half so let's take half this is going to be 1 plus half log half plus half log half right because P is half 1 minus P is also half this is equal to 1 plus a half log 1 plus log to the base 2 of half and log to the base 2 of half is going to be minus log to the base 2 of 2 which is 0 so the intuition says P is equal to half implies that the rate achievable is 0 what does this mean see here's the thing if P is equal to half corresponds to the case of like a fair coin that is the most typical sequence is the one that contains an equal number of heads and tails or an exactly n by 2 errors now if you have n by 2 errors then there is a problem because let's say that the worst situation you will encounter is you use a repetition code if you use an n length repetition code and I flip n by 2 bits then you are in trouble because you have an ambiguity if you have an n if you have n ones you flip half of them you're going to have an equal number of zeros and ones you can't decide similarly if you take n zeros and flip half of them you're still going to have an equal number of zeros and ones so intuitively speaking when P is half the rate is 0 because even a repetition code will hardly work and that's the reason why P is equal to 0 no bit flips in one binary symmetric channel use I can get one bit why because that's the case where the binary symmetric channels and zeros and zeros and ones as ones so if you know go a little further there's this notion of the so-called typical set this is just to formalize the statements that we made earlier you can just understand this in terms of probability if you have x1 x2 xn as iid random variables drawn from a finite alphabet x the typical set a epsilon n this is basically making it formal contain sequences that satisfy h of x-epsilon is less than equal to minus 1 upon n P of x1 x2 xn less than equal to h of x plus epsilon where h of x is the entropy of x defined as summation over x minus P log to the base 2 of P is a binary entropy now in this case our finite alphabet here is 0 comma 1 and hx minus epsilon hx plus epsilon is less than equal to minus 1 by n P x1 x2 up to xn what does this mean so let's say that you know you take the noise sequences in noise sequences that result from a binary symmetric channel you can also take them as bit see bit flip sequences it turns out that for large enough n and for any epsilon you give me like epsilon is number which is like 0.01 0.001 something like that then the probability of the seek the most likely seek you know sequence of errors it turns out is going to be between hx plus or minus epsilon in other words let me just put it in this way for typical sequences probability of will always be less than or equal to 2 power minus n h of k and it will be more than this is something that will happen in this particular case if you take a seek you know if you take any np any sequence that are np ones in the case of a binary symmetric channel they will essentially satisfy this where h of x is defined as this is not very different from what we saw earlier here we saw that here this is maximized for L is equal to np right that same thing is being specified here in fact if you go through some references on information theory you will find that this proof of this basically rests on the weak law of large numbers and the noise sequences in a binary symmetric channel are typical because as you make in larger and larger it turns out that only the sequences that have np ones are going to start appearing and these sequences are going to be you know having probability of 2 power minus n h of x approximately and therefore they are the only sequences that you have to handle therefore when you design your code words they have to essentially be able to handle these typical error patterns. So, as n tends to infinity typical sets sequences amass all the probability in other words let us say the all 0 sequence all 0 error sequence 0 probability all error sequence 0 probability ok you know close to np errors they have non-zero probability each of those sequences has probability 2 power minus n h of x and they amass all the probability. So, all you need to do is to account for these. So, therefore it is almost like this you have 2 power n sequences you need to partition them so that any np length errors are handled that is you need to design your code to account for the fact that the 2 power n sequences which you you have the rather the nc n bit sequences that you have they partition the 2 power n sequences into those that are robust to these typical error sequences. If you want a formal kind of picture you have 2 power n h of y and if you work it out let us say y is equal to x plus noise you will find that if you choose x to be equally likely 0 and 1 and n is any distribution of binary symmetric channel noise you will find that 2 power n h of y h of y is actually 1. So, and there is this concept of h of y given x so you will have to evaluate the you know distribution of y given x is 0 or 1 and place this actually, but that is actually 2 power n h of y by 2 power n h of n what does this mean this it turns out is 1 minus h of p it turns out that the capacity of the binary symmetric channel is 1 minus h of p, but this is the same as what you got earlier which is 1 plus p log to the base 2 of p plus 1 minus p log to the base 2 of 1 minus p. So, this is exactly the capacity of a binary symmetric channel formally speaking however you need to work you need to actually prove the fact that this rate is achievable and you need to also prove that any rate above this is not achievable for that you can refer to a good textbook like fundamentals of information theory by Kovar and Thomas or any other textbook, but the intuition that you have is that n length sequences are present n length bit sequences of those you choose those in you know 2 power k sequences such that any n p flips result in no ambiguity so you want to handle only the typical error sequences and that partitioning intuitively gives you this capacity. A quick kind of aside to AWGN if you consider a real additive wide Gaussian noise channel in this case x square is less than equal expectation of x square is than equal to p that is the constraint that is placed because in the case of an AWGN channel your x can have a higher and higher power and your n is the noise will say Gaussian noise of course it is wide and mean 0 and variances sigma square. It turns out that the capacity of this channel is half log 1 plus p upon sigma square intuitively this makes sense because if you allow p to be higher and higher you can choose excess to be more and more robust and you get a higher and higher rate and the number of possible excess that you can potentially choose is related to c of course much like in the binary symmetric channel case you cannot directly just use excess you need to create code books of multiple x you need to basically be allowed to use the channel multiple times and that way you can get a large number of channel uses and that's where you can achieve this capacity using typicality once more. So as an intuition if you use the channel n times just like in the case of a binary symmetric channel you have x1 x2 up to xn and the noise essentially gives you x1 plus n1 x1 plus x2 plus n2 and so on. So use the channel n times then this n is actually the you know n as opposed to the noise in the previous one apologies for the confusion. The signal to noise can be considered a sphere which has volume roughly root of n times p plus sigma square power n okay because your y has volume and root of n of p plus sigma square power n and the noise sphere has root of n sigma square power n as its volume because it's like you keep generating it's like you keep generating n length sequences of noise and plot it as a higher sphere in n dimensions it's a sphere which has volume its radius is root n sigma square so volume is proportional to root n sigma square power n of course I'm ignoring the constants that go there I'm going to take the ratio here if you take r the ratio r is equal to 1 by n log root of n plus sigma square np plus sigma square by root n sigma square whole power n this n comes out and cancels this end you get half log p plus sigma square so again by taking random Gaussian sequences you can show that the number of codes number of the rather the k or rather the rate that you can get over the channel is half log 1 plus p upon sigma square for again more on this you can refer to a good information theory textbook so to summarize bitter rate in practicals channels is of course quite high coding reduces bitter rate but suppose that I demand an arbitrarily low bitter rate what is the highest rate that I can get the channel capacity is the ultimate limit and as the block lengths go to infinity you can reach close to this in fact modern codes such as LDPCs and turbo codes in several situations are able to achieve very close to the channel capacity with of course some error margins taken into account and for more information you can check some good references is in particular the Shannon Hartley theorem that extends this to the case of channels with bandwidth so if you have a wide band channel you can actually say that per Hertz achievable rate is this much and you know this is the capacity and you can look at several information theory courses and modern coding related references for more information on information theory and coding and their connection thank you


Welcome to Spotlight Advanced. I'm Colin Louther. And I'm Liz Wade. Spotlight uses a special English method of broadcasting. It is easier for people to understand, no matter where in the world they live. Think of an event in your life that you remember very well. You can think of any memory as long as it is clear. Think back. Where were you? Who were you with? What time was it? Can you smell anything? Taste anything? Why do you think you remember this event so well? Memory is strange. Sometimes a person might remember something that is extremely important to them. At other times, she may remember a fact that does not matter at all. It may be difficult to learn something she wants to remember, like an English vocabulary word. But this does not always have to be the case. There are actually special methods that you can use to improve your memory. Today's spotlight is on how to use your memory to remember anything. No one's memory is perfect. What a person remembers can be unreliable. Even the most intelligent people forget things. This does not mean a person has a bad memory. It means he's not using his mind to his advantage. The brain has two kinds of memory. The first is short-term or working memory. This kind of memory is useful for a very short time. Experts say you can store four sets of information in your short-term memory at the same time. Information in short-term memory will last about 20 to 30 seconds or less. Then it disappears. The second kind of memory is long-term memory. Long-term memory is what people usually think of when they say they have memorised something. Memories in the long-term memory stay. A person can remember them again and again. These are memories like the names of people you know, how to do a task, or memories from a special event from a long time ago. The trick of remembering is moving information from short-term to long-term memory. No one knows exactly how this happens. But scientists do have theories. Richard Mose is a writer at HowStuffWorks.com. He writes that going from one kind of memory to the other is all about attention. You must focus on particular things. To properly create a memory, you must first be paying attention. You cannot pay attention to everything all the time. So most of what you see every day is filtered out. Only some information passes into your conscious awareness.  People also remember things that are similar to what they already know. This is because of the structure of the brain. Most of the brain is made of cells called neurons. When we learn something, different neurons connect to each other. When these neurons stay connected, it forms a memory. The more connections a memory has, the stronger it will be. For example, think of a person you know. When you think of that person, the same neurons become active in your brain every time. Memories can also link to other memories, making them stronger still. This is why memories with multiple senses last longer. For example, a memory where you experienced something by hearing, tasting and smelling may be very strong because you used more senses in the experience. These memories connect different parts of the brain. Think of the person you just remembered. Do you remember what their laugh sounds like? Do you remember the sound of their voice? Finally, we make memories when we have repeated experiences. Each time we do the same thing, our brains make new connections. Scientists say our neurons activate or fire. Richard Mose uses practicing music as an example.  This makes it easier to repeat this firing later on. The result? You get better at playing music. You can play it faster, with fewer mistakes. Practice it long enough, and you will play it perfectly. Practicing or repeating information is one of the most popular methods of remembering. If a person has vocabulary cards, they are probably using this method. But this is not always the best way to memorize. There are many methods which use the other ways we remember, or combine them. Another memorization method is called a mnemonic device. One well-known mnemonic device is called an acronym. Acronyms are helpful in memorizing words. To create an acronym, find a list of words you would like to memorize. It is usually helpful if there is something similar about them. Then take the first letter from each word. Organize those letters into a word or phrase. You have now made an acronym. Each letter in the final word stands in for another word. So to memorize many words, you only have to remember one. One famous acronym for learning English conjunctions is fanboys. The F stands in for the word for. A stands for and, the rest stand for nor, but, or, yet, and so. Acronyms work because they make the information simple. Another memorization method is visualization. In visualization, you think of an image or picture that represents the thing you are trying to remember. For example, imagine a person is trying to remember the name Melanie. He might think of a picture. In the picture, a woman is holding a melon fruit. She is crushing the melon with her knee. The sound of the two images will remind him of the name Melanie. The image is also very strange. It is easier for the mind to remember unusual things. Visualization works because it makes the foreign information into something familiar. One of the most interesting mnemonic devices is called the method of loci. It is also called a memory palace. To create a memory palace, a person must think of a familiar area like a house or a street. Then, she must imagine a journey through that space. In the journey, she stops at different familiar areas. In each of these areas, she places an item. The item must have something to do with the thing she is trying to remember. Melanie Pinola is a writer and mental athlete. She competes with others to remember long lists of numbers or words. She wrote about the memory palace technique for lifehacker.com. For everyday use, the memory palace is helpful for remembering a list of things. Start a journey beginning at a place you know very well, like your home. Begin at your door. If you want to remember a grocery list, imagine the items you need. Imagine a container of milk overflowing on your doorstep. When you get inside, perhaps two giant stakes attack you in your doorway. Continue to your living room to find pretzels dancing on the rug. Again, the more movement, strange experiences and senses you put into your memory palace, the better for your memorization. This may seem like a lot of work, creating more information than the person needs to memorize. But the method of loci is actually a way of hacking the brain. To remember something, the brain needs a network of information. Without this network, the memory will fade quickly. The method of loci creates a new network. It uses multiple senses, and then it attaches the network to something familiar. This way, what you are trying to remember enters the long-term memory. There are many more mnemonic devices, but most memory methods involve one of these three steps. Make it simple, visualize it, that is, imagine you can see it in your mind. Connect the information to something you already know. If you can master these simple tips, you will be able to remember huge amounts of information. What will you memorize now? Do you have any special ways you remember things? What are they? Will you try a new method? You can leave a comment on our website at www.spotlightenglish.com. You can also find us on YouTube, Facebook, Instagram, and Twitter. The writer of this program was Dan Christman. The producer was Michio Ozaki. The voices you heard were from the United Kingdom and the United States. All quotes were adapted for this program and voiced by Spotlight. This program is called How to Memorize Anything. Visit our website to download our free official app for Android and Apple devices. We hope you can join us again for the next Spotlight program. Goodbye!