Many people ask me, when Bitcoin will hit this and this price, I don't care about the price. I think the price is the least important part about Bitcoin.  And so you could compare the time that we're in right now to the late 90s. Will my children, will they think that in order for their money to be secure, in order for them to trade and exchange and have savings accounts, there is even the need and existence of a bank. The cryptocurrency jumped on the news reaching an all-time high of more than 50,000 people. This is backed purely by a belief. This is the biggest thing to happen in mankind. The world has been absolutely captivated by the drama of Bitcoin's meteoric rise and subsequent collapse. In December, the price of a single Bitcoin reached nearly US$20,000. By early February 2018, that price had dropped to below US$6,000. Prompting the economist Nuriya Rabini to tweet, the mother of all bubbles and biggest bubble in human history comes down crashing. A few days later, Rabini tweeted again, this time more ominously, predicting that Bitcoin's price would continue to drop until it hit zero. In the same week, the digital currency was dealt another serious blow when the general manager of the Bank for International Settlements likened the mania around Bitcoin to a combination of a bubble, a Ponzi scheme and an environmental disaster. Even before it lost nearly two-thirds of its peak value, Nobel Prize-winning economist Robert Schiller may not have gone as far as sounding the death knell for the cryptocurrency but he did express a fair share of skepticism. I tend to think of Bitcoin as an experiment, it's an interesting experiment it's not a permanent feature of our lives. What Schiller did do, however, was point to Bitcoin's underlying tech, Blockchain, as the deserved focus of our attention. We're overemphasizing Bitcoin, we should broaden it out to Blockchain which will have other applications. If doomsayers are correct and the preeminent cryptocurrency is on the brink of collapse, what, if anything, will be its legacy? What becomes of, say, Blockchain? But before we get ahead of ourselves, what is Blockchain? Blockchain is a database, decentralized database. It's immutable, which means the information in Blockchain is permanent. It's a database of all of us. It's a way to build a system of record with nobody acting as God in the center of that system. So we can build cooperative networks and cooperative applications on top. One of those things we can build is a cryptocurrency. There isn't a Bitcoin Inc CEO. There isn't a Bitcoin headquarters somewhere. There's no one controlling as a company, as a centralized institution for Bitcoin. But it's completely decentralized. We've gotten really good in Silicon Valley and in the Internet community at building databases and web servers and things that allowed us to build big central services. Think Uber, right? Think PayPal. But we didn't really make it easy to build decentralized systems. And so Blockchain technology and the decentralized web, which is what we're getting, it really are a way to get there. I mean, I think the Blockchain is going to do really interesting things around the transfer of value. But this idea that it's like a decentralized system that's going to completely bypass the banks, I don't understand where that hype is coming from because the banks are the first to use the technology just to make the transfer of value more efficient. Whereas a lot of the discussions in the cryptocurrency community focus on how do we avoid the banking system, how do we create an alternative to it, banks themselves are realizing that this underlying Blockchain technology, they can use this underlying technology to automate a lot of systems that today requires a back office staff that requires regulation and regulators watching what they're doing. These are systems that help prevent a lot of fraud from happening in the first place, right? Prevent somebody from selling the same object twice or spending the same dollar twice. And in doing that can help make trustworthy systems and auditable systems much less expensive for many of these banks. So they can do a lot of what they do today just faster and cheaper and with greater guarantees that the parties that they're working with will make good on their commitments. I think the question is less about the pipes and the mechanisms but whether the notion of money and currency is fundamentally changing. So will my children, will they think that in order for their money to be secure, in order for them to trade and exchange and have savings accounts, there is even the need and existence of a bank. That I think is the more interesting question versus whether the Blockchain is going to get rid of the banks. I think the Blockchain then becomes the technology in the tool that enables that sort of transformation in the relationship to value. While few banks are likely to endorse the exchange of digital currencies, many are waking up to the possibilities of Blockchain. And it bears repeating that this technology was first designed to support an alternative store of value which principally as a system removed the need for third parties like banks. If you see an erosion of trust, say in financial institutions or banks, how does that create some kind of vacuum that then gives the space for alternatives to rise up where people become more receptive to placing, taking a risk in something new, placing their faith in something new. And there I think there is very much a direct correlation that there was this trust vacuum and these cryptocurrencies rose up and people were more open to an alternative financial system. I think whether we're talking about cryptocurrencies or we're talking about the underlying technology of Blockchain technology, it really is about trust. I could give you any example where you could deploy a Blockchain and you would say, couldn't you do that with a centralized system? Wouldn't it be faster? Wouldn't it be cheaper? And the answer is always yes. It will always be faster and cheaper to do something centralized. But we don't do that because we don't want to have to trust that entity at the center. Bitcoin would work tremendously well if there was just a Bitcoin bank and all of us had accounts in and out. But the whole point of doing a cryptocurrency like that is to decentralize that. So this is a protocol for re-establishing trust in digital systems. We're at a moment where people are losing trust. Trust in institutions, trust in government, trust in whole professions like banking and lawyers and such. And we need our technologies to help give us a bit of that back. Give us what we had when we knew the people that we did business with face to face. For a very long period of history, we have largely been dependent on institutional trust. Trust flows upwards to experts and regulators and CEOs and political leaders. And the trust really flows through institutions. And what we're starting to see now is, of course, I'm not saying institutions aren't important, but the trust is starting to flow sideways to strangers and peers and to colleagues and even to anonymous spots. And what worries me about that is then we become in this very dangerous cycle of who is responsible and where does accountability lie. So a lot can go wrong because often individuals don't realize the responsibility they're taking on in this age of distributed trust. Where does that leave us? Are we in the early days of the internet left to one day wonder how we ever lived without these technologies? Or will the hype around blockchain prove to be a bubble in and of itself?   All of this is just a bubble. And I think the truth must be in the middle somewhere. In some ways it feels like 1995 in the early days of the internet where the first bits and pieces were coming together, where the first websites were launching, people were realizing while we could conduct commerce online we can build communities online. And lots of excitement, lots of experiments being conducted. But still some key architectural pieces that were still in the very early days and needed to be solidified such as the security of the connection between a website and your browser so that you could send a credit card number and know that it wasn't going to be listened to by everybody in between. And so we're somewhat early days although there are production deployments now of lots of blockchain technologies, real companies, realizing value from the application of this technology. But still early in terms of the market's understanding of it and in terms of how companies are comprehending. There's a lot of excitement. And I think we're in a time where these kinds of cycles get so compressed compared to 20 years ago that this duality is pretty understandable. It needs a whole new kind of social safety net. So I think this is, you know, people worry about the future of work. They worry about independent contractors on platforms. So if you don't have the institution as the default and you don't have mechanisms like health insurance and disability leave and parental leave, whatever it is, what is that new social safety net underneath the distributed network? But the real issue, like whatever the manifestation is the real issue is an issue of accountability.


Hello, I'm Nathan. I'm Nemtek, first-year student working as an RA for Fortexer. These are my fellow RA students. We'll be mentoring some of you for your summer internship projects and we'll intend to be mentored by Nagesh and Firuza. They are at the very back. So we'll just dive into the project. But before that, you may not be familiar with some of the technologies that we'll be mentioning here. But don't be disheartened because of that. You're not expected to know a lot as of now. If you don't understand any of the technologies, it is fine. You will have time to catch up. You'll have time to learn new technologies. So the first project is IP-based hardware infrastructure management for the cloud and the mentor will be Minali. So cloud infrastructure management basically is the maintenance monitoring and making sure that the cloud stays up and running for as much time as possible. We don't want any downtime. We don't want any resources to go down or we don't want any of the drives to fail. Basically that is what cloud maintenance or cloud management means. So most of the management tools focus on migration, elasticity, load balancing, and of course system failure detection. If any of the resources fail, we want it up and running as soon as possible. System administrators already have tools that alert them or notify them whenever a particular system resource goes down. Example, a hard drive failure. So the motivation behind the project is that any downtime on a cloud is very expensive in the industry. Here also any downtime on the cloud will hamper the user experience by a lot. So we don't want any downtime. We want to make sure that the cloud is up and running as soon as possible. Even if there is any failure, we want to reboot the cloud. We want to reboot the drive as fast as possible. To achieve this, we are not simply a science system administrators and ask them to go manually check the drives because the server rooms can be quite huge. We cannot manually check each and every single drive to make sure that they are running. So we want a tool to make sure that we can reboot the disks using software basically. So there is already one such tool. It is called Najio's. It is quite old and it is very popular. So Najio's what it does is it keeps checking the resources. It keeps checking the resources and whenever there is a failure, whenever there is a change, it notifies or takes a set of recorded actions that has to be taken. Najio's the drawback with Najio's is that it performs at an operating system level, which means that if the entire system goes down, then Najio's cannot do anything about it. It can only notify. So it cannot reboot a system that is not responding. It cannot reboot a system that is already shut down. So we want, so therein comes the importance of IPMI, which is Intelligent Platform Management Interface. It is a protocol that provides several management capabilities for the cloud, irrespective of independent of CPU, BIOS and OS. So it gives a set of interfaces using which the administrators can monitor and manage cloud activities. So even the best thing about IPMI is, even if a system is down, you can reboot it using this protocol. Reboot it or manage it using this protocol. So the aim of this particular project that is IPMI's hardware infrastructure management is to create a tool using the IPMI protocol and provide it a user interface for using in a large-scale cloud setup that we have here. At the end of this project, we need a fully functioning tool with a functioning user interface. These are the references. Moving on to the second project. This is to build crowd sourcing platforms. The project mentor will be Himanshu Singh. So you may already, I think Dr. Mukta had already given a brief talk on, she has mentioned crowd sourcing. So crowd sourcing is basically a technique of obtaining information or input from large number of users all over the internet. Typically they are unpaid, like for example how Wikipedia works or how Cora maintenance works. So there is no singular authority. Generally, all the users all over the world contribute to crowd sourcing. So for this project, we'll be either using Python and Django or PHP and Rupal. So you may already be familiar with Python, it's a general purpose scripting language. And Django is a web framework that can be used for web development, rapid web development. PHP and Rupal, PHP is of course a web server-side scripting language and Rupal is something that is widely used here. It is a content management CMS, which is content management system. We'll talk about Rupal in the coming slides. And you'll also be making use of REST APIs. So REST is basically a set of standard operating procedures for communication between systems in the web. So if you create an API that follows a particular protocols, you can call those APIs RESTful APIs. So the motivation behind this is the same as the motivation behind crowd sourcing, which is that large number of users can take part in this process. Large number of users from diverse experiences can contribute to the crowd sourcing activity. And it allows people with different browsers, different machines, different systems, different platforms to test out your product, test out your website or whatever it is that you're building. And it is much better than using a code team to test out your product. And of course it is free of cost. And some of the likely applications of crowd sourcing that we can make is a clean India application with which you can take pictures of waste dumps that needs to be handled in new surroundings and upload it and others can comment on it or apport it to give it a higher priority so that the authorities can take action on it or the community itself can take action on it. Another example is data collection for supervised learning. You may be familiar with supervised learning. It's a type of machine learning in which you need huge quantities of already labeled data. So these labels have to be manually provided. This label manually can be done by crowd sourcing. You may have done Google capture in which you are given a signpost or you are given a road sign and you are asked to type in what this road sign reads. This is an example of a similar activity. So you can give a user a text, a video, an image and ask the user to tag or provide this description on this. And using this description, we can feed this data into machine learning algorithm and use it for classifying tasks. These are the references for the cloud sourcing platform. And the third project would be portal for collaborating communities and the mentor would be Nivea. This project is already under development and you will have to keep contributing to it. So this involves efforts from users in a single community to create, edit and publish OER. OER is Open Educational Resources. There is a presentation coming up on OERs. So it can be anything like audio, video, mage, text or text books. So the entire community will create, edit and manage and then publish these OERs. So we had to create a portal for collaborating communities, basically. The portal would be built using Drupal 7. And it would use other technologies like PHP, MySQL and REST API. The major components would be content publishing. To publish the basic content, then localization support for Indian languages, that is local languages. And user reputation component. User reputation component is to make sure that there are different levels of users. That is, a user can gain reputation by putting content or by commenting on content or by creating more content. User can have different levels of reputation or gain more reputation in the system. And this has to be done automatically. And of course, content versioning and even logging. Content versioning and even logging is there is a separate project on content versioning and even logging that we'll discuss. So this part of the objectives have already been achieved. The following objectives are yet to be achieved. So we need full integration with the content publishing part, content publishing workflow. We have to build an effective event logging system to keep track of all user events. Event logging means whatever user does, it has to be logged. If a user clicks on a particular link, if a user watches a video, if a user comments on something, every single target user does has to be logged. The reputation system again means when a user does something positive in the community, he has to gain some reputation and whenever he reaches a particular level, he has to get some badges and some more authority. And this has to be done automatically so that we have, by default, some administrative levels and some moderator levels and the like. And the next thing is integrating quiz module with the reputation system so that we don't have to worry about the results of the quiz and how points are earned and lost. So these are the links for portal for collaborating communities. So the next project is performance improvement of Open edX. I would be the mentor for this project. I think you have already seen a presentation on Open edX. You already know what Coursera and UDMA is. So these are MOOCs, basically massive open online courses or websites that provide massive open online courses. edX is one such site and Open edX is a platform using which you can create a website that hosts these courses. So we have created, in IT, we have created such a website, IT BombayX, you have seen a presentation. So the aim of this project is to improve the performance of Open edX by combining several technologies. So RocksDB is open source database software that is built by Facebook to improve their database storage. So it performs exceptionally fast on flash storages. MyRocks is an additional MySQL layer that is put on top of RocksDB so that we get the best of both worlds. The motivation is to improve the performance of IT BombayX so that the users get a better experience. So as for the objectives, we'll have to spend a lot of time familiarizing oneself with the technologies to use like Open edX platform. Open edX is a very complicated platform which makes use of MongoDB, MySQL and other several technologies. And you'll have to familiarize yourself with MyRocks and RocksDB also. So then we'll have to identify the strength and weaknesses of the existing system, then integrate RocksDB with MySQL and MongoDB. Then we'll have to check for the performance improvement in the new system. We'll have to design several tests for that and document the entire process. So these are the references. The next project is event logging and content versioning system. I've briefly talked about event logging already. It's a mechanism by which all events associated with the user, all clicks, all video views, all image views, all search queries, everything that a user does in a system is logged at the server side. So the point of logging all these events is that you can run data analytics. You can learn what a user wants, what large number of users are searching for. You can perform data analytics on these data. For example, if large number of users are reporting on a particular event, you can take note of that. Or large numbers of users are tagging it, tagging something or navigating or have trouble navigating something. You can take note of that. So this has already been performed on existing systems like Open edX. So we want to build a new event logging system. And content versioning is basically like a lot of you might have already used content versioning. It basically keeps track of all the changes to the content. All your content and all the changes that is made to the content will be kept track of. And you can see all this history and all the contributions by a particular user. There are various tools available like GitHub, CVS, SVN. So combining both event logging and CVS, we can have very powerful data analytics mechanisms. For example, an event cannot capture how much a single user has contributed to a project. But looking at a user's history in content versioning system, you can see how important a user has been to a particular project. So this can be used to rank a particular user based on his contributions. And as an example is, for example, an event can tell you what type of contributions that a particular user has made, which CVS cannot tell you. This is screen grab from an Open edX event log. So you can see how events are captured as JSON files. And in the second and third figure, you can see how they are passed into readable formats, into different formats that are much more readable. This is a paper that discusses event logging. Next is OER repository platform. OER, as I've already said, is Open Educational Resources. So any text, textbook, any image, any video, it could be entire courses that are shared under Creative Commons licenses or free art licenses or no public license. All these, all such courses, all such content that are openly and freely available are called OER. So the point is to make OER repository, a platform for OER repository. So we would be making use of PHP. It's a server-side scripting language that can be amended into HTML, of course. And Rust API, as I've said, Rust is a standard for communication between different systems in the web. And you'll have to create APIs that follow the Rust standard. So dSpace is an open-source software that is widely used to create open digital repositories. So you can add content and provide access to these content for any number of users. The content can be anything from text images, any type of content. And Drupal is a content management software. When you think of Drupal, think about something like WordPress. So using Drupal, you can create web pages. You can add functionalities to your web pages variously using modules. There are several modules already available because it's open-source. People have already contributed a lot to the Drupal community. There are several modules available. You can add functionalities using these modules. And you can create new modules, modify modules, and contribute back to the community. So the motivation behind OER repository platform is the motivation behind open education, which is to say that by improving the open access to information and education, you can provide high-quality education available for everyone. So you can bridge the gap between universities and general public. And you can remove formalities like admission criteria. So these are not required in open education platform. It's freely available for everyone. And the current objectives are we have to explore the existing metadata formats for open educational resources. And we have to create a platform for uploading and searching these resources. Then you'll have to add metadata standards to the D-space repository that we'll be using. Then you'll have to design a REST API communication between the D-space digital repository and the Drupal front-end. I think these are the references for OER platform. And finally, word on what we expect from you. So obviously we'll need the finished project, which follows all the proper coding standards and proper documentation. And if possible, you'll have to contribute back to the open-source community if you'll be using open-source softwares. And if possible, you'll have to write a research paper describing in detail the work that you have done. And of course, you have to learn as much as possible about the topic that you have learned. These are the six projects that we are working on.


Today I'm in Kabul, Afghanistan, a convergence of emergencies has brought the country to the edge of a humanitarian disaster. The Afghan people have been hit by years of conflict and insecurity, severe drought and the COVID-19 pandemic. Millions are now enduring an economic free fall. The coming winter could even lead to more vulnerability and suffering. We are in a race against time to help hundreds of thousands of people prepare, but there is hope amid the alarm. I am deeply impressed by the resilience and determination of the Afghan women, men, boys and girls to build a better future. And I must say, I am proud of the work that my colleagues, together with the humanitarian community, are undertaking despite immense challenges. The continued vigilance and generosity of the global community is needed to provide calamity and secure a better future for the Afghan people.


Considering that nearly half the world's population speaks a language that's part of the Indo-European language family, from Irish and Scottish Gaelic to Sanskrit and India, what do we know about the genetic legacy left by the massive waves of Indo-European migration? Well we can see the genetic fingerprints of the Indo-Europeans right across Eurasia, and it may partly explain why there are so many origin stories in the likes of Scotland and Ireland that discuss cultures from the steppe, an area just north of the Black Sea and to the east. The origins of the Indo-European seems to be a mixture of both Anatolia and the Pontic Caspian steppe, with various cultures over the centuries and millennia spreading Indo-European traits at outwards. The presence of steppe-related ancestry in so many parts of Eurasia is actually quite astonishing, and it's considered a strong marker of the legacy of these Indo-European migrations. Three cultures in particular were important in this spread, and they're important to understand before we move on to document some of the recent findings from genetic research. The Yamnaya culture, also known as the Pit Grave culture, was one, which existed from around 3300 to 2600 BC and the Pontic Caspian steppe. The rapid spread of the Yamnaya culture across Eurasia has led many to argue that this might have been quite a violent event, with the Yamnaya people potentially committing genocide against the native peoples they largely went on to replace, but that's a story for another time. Second up is a connected culture known as the Corded Ware culture, a culture that existed from around 3000 BC to around 2350 BC, and occupied a massive area, from a contact zone with the Yamnaya culture to stretching right across a large part of central and western Europe, and even into parts of Scandinavia. Thirdly there was the Bell Beaker culture, which existed from around 2800 BC to around 1800 BC, and occupied an area much further west, and western Europe, Britain and Ireland. Now a study from 2015 looked at this massive migration from the steppe across Europe, and found that western and eastern Europe came into contact around 4500 years ago, as the late Neolithic Corded Ware people from Germany who traced about 75% of their ancestry to the Yamnaya, documenting a massive migration into the heartland of Europe from its eastern periphery. This steppe ancestry persisted in all sampled central Europeans until at least 3000 years ago, and is ubiquitous in present-day Europeans. These results provide support for a steppe origin of at least some of the Indo-European languages of Europe. There are particular genetic markers that are associated with the Indo-Europeans, namely haplogrps R1A and R1B, which would have expanded from the Pontic Steppe, and which are common markers across Eurasia, with R1B particularly common in western Europe. This table shows a breakdown of R1B by region. It is very common in western Europe and other parts of Europe, but also found in people from Russia, South Asia and even parts of Africa. What is quite astonishing is how far the steppe ancestry spread across Europe. Ireland for instance is pretty far from the steppe area, and you wouldn't necessarily think that there would be strong steppe ancestry in Ireland, well think again. A 2016 study from Ireland looked at three Bronze Age males from Rathland Island, a tiny island off the coast of the north of Ireland, who lived between 2000 and 26 BC and around 1500 BC. The study found that each Bronze Age sample that were all male exhibits the Bronze Age associated with chromosome lineage, R1B M269, the appearance of which has been strongly linked with the steppe incursion into central Europe. Thus it is clear that the great wave of genomic change which swept across from above the Black Sea into Europe around 3000 BC washed it all the way to the northeast shore of its most westerly island. At present, the beaker culture is the most probable archaeological vector of the steppe ancestry into Ireland from the continent, although further sampling from beaker burials across western Europe will be necessary to confirm this. The extent of this change, which we estimate are roughly a third of Irish Bronze Age ancestry, opens the possibility of associated lineage change, perhaps the first introduction of the Indo-European lineage ancestral to Irish. If we look at other parts of Europe, we see a similar pattern. A 2021 study, for instance, looked at the genetics of Bronze Age individuals from Greece and the Aegean Sea area. The study found by the Middle Bronze Age around 4000-4600 years ago. Individuals from the Northern Aegean were considerably different compared to those of the Early Bronze Age. These individuals shared half their ancestry with people from the Pontic Caspian steppe, a large geographical region stretching between the Danube and the Ural rivers and north of the Black Sea, and were highly similar to present-day Greeks. The findings suggest that migration waves from herders from the Pontic Caspian steppe, or populations north of the Aegean, that bear Pontic Caspian steppe like ancestry. These potential migration waves all predate the appearance of the earliest documented form of Greek, supporting theories explaining the emergence of proto-Greek and the evolution of the Indo-European limeges in either Anatolia or the Pontic Caspian steppe region. As I've mentioned a few times now, we also know that there's a large degree of steppe-related ancestry that was introduced into Britain, as the 2018 study found. The spread of the beaker complex introduced high levels of steppe-related ancestry and was associated with the replacement of approximately 90% of Britain's gene pool within a few hundred years, continuing the East-West expansion that brought steppe-related ancestry into central and northern Europe over the previous centuries. Now the steppe ancestry didn't just spread west and south however, but east as well, as a 2019 study that looked at South Asia found. The primary ancestral population of modern South Asians is a mixture of people related to early Holocene populations of Iran and South Asia that we detect in outlier individuals from two sites in cultural contact with the Indus Valley Civilisation, making it plausible that it was characteristic of the Indus Valley Civilisation. After the civilisation's decline, this population mixed with Northwestern groups with steppe ancestry to form the ancestral North Indians, and also mixed with Southeastern groups to form the ancestral South Indians, whose direct descendants today live in tribal groups in southern India. Mixtures of these two post-Indus Valley Civilisation groups, the ancestral North Indians and the ancestral South Indians, drive the main ingredient of genetic variation in South Asia today. Now as we have seen, the spread of Indo-European cultures led an ancient genetic layer in many of the peoples across Eurasia, and is a clear genetic fingerprint of the Indo-European migrations. To find out more about the Indo-Europeans, the connections in the limeges and many other facts, please click here. Thanks for watching, please subscribe and hit the bell for ways to support the wellbeing in the description below. Thanks again for watching and I'll see you next time.


The thing that's going well, I think the most important thing is just playing with the right fundamentals and techniques and also just building the chemistry with your teammates. Just learning how each guy plays in the tight end room, how the tackles play because you're always working with them and how the receivers play because you're always running route combos with them. Certain receivers do different things, releases, trailing is a real big physical guy, Robbie's a real smooth and quick guy, so just learning how to play alongside people just like that. You have such a unique combination of physicality and athleticism. So how exciting is it for somebody like you to finally know, alright, here we go, pads are on and knowing that the physicality of the game is going to keep ramping up from here on out. Yeah, I feel like it puts me at an advantage just because like in the route game I'm able to be a lot more physical than you are and just having jerseys on. So you know, it's real nice to be able to put the pads on and just go and get after guys. How do you feel about seeing your fellow rookie classmates make some plays out here? You guys talk about that? What do you think? Yeah man, we're just trying to be the best rookie class we can be. Yeah? Alright, but I mean you guys watch each other and see how they make play there. You know, every time we see something make a play we always go and congratulate them because we're really close, we all came in together so whenever we see each other make a play we're very excited and very happy. Is there any kind of like pact going in? Yeah, you know, we're young guys man, we gotta show what we can do, that's how it is.  Um, I'm not sure. I feel like NFL camp in terms of, you know, schedule is a lot easier than college camp. College camp was, you know, crazy in terms of that stuff. But just really, you know, just learning, relearning everything.  So now it's just different because you have to actually like think when you hear a play in the huddle, you gotta think about where you're going, you gotta think about your assignment, so you can't play as fast, but that's what we're working on right now is to just, you know, just learn the plays. So as soon as you get them from Ryan, it's just immediately in your head, you know exactly what you're doing and get your calls. So that's probably the main thing that you're focusing on right now.  But now we got the pass zone, got out there a little bit, started with some trash talk, you know, that's just how it go. From a defensive standpoint, do you feel like you gotta set the tone to that in terms of what you need to do? Um, I think it's always room for improvement. I think it's first day of pass, everybody's still getting adjusted. Um, you know, we're still stacking the days. I think we ended on a good note. Um, just keep building, stacking the days. Come out here tomorrow after a full day of pass and get better. How much can you and Zach benefit from this time together where you guys are both healthy, you got a training camp together to establish your connection for the season? Um, like I said before, I think it's just a good way to start off. And I'm not him coming in the middle of the season, you know, trying to get adjusted, you know, he's had a long time to get the plays down. And now he's just building that chemistry, not just us, you know, the defense, the line, you know, the guys behind us. Like, you know, he's still stacking the days, you know, getting better. And for you specifically, what are you trying to do to elevate your game this season?  You know, I think that's gonna take me a long way. And you guys, to kind of get acclimated before everything is okay, no excuses?   And was today at 15? Uh, a little bit.  Is it awesome? Oh, it's a lot more fun, a lot more hitting, a lot more plays to be made, you know, a lot more chess talking. You will see that go on, you know, the rest of the week. Who's doing the most trash talking on the team right now?    You're freeble, too, right? Oh, yeah. He gets in there when he needs to. Oh, a lot. You know, this is what, day four or five camp, so everything started wearing a little bit. And just to be able to see that progress from this year compared to last year and how my body feels, just how, you know, wake it up every day in the morning, you know, it definitely is paying off. Yeah, it feels really good. We're all talking to each other, communicating well. We played with each other for a while now.  I'm very excited for where we can go. How do you feel again?  You know, we always say one percent better and he's doing that. I'm excited for him. Really excited for us. We're gonna be, we're gonna be, we're gonna be fine. We're gonna be fine. Do we make too much of Bruce's weight in the 290? Yeah. Or is big just big? Big is just big. At the end of the day, it's can you play? It doesn't matter if you're 280, 250, can you play? And he can play. So I got all the faith in him.  Yeah, it makes us both better than the day. You know, just being able to, you know, he's one of the best these attackers in the league. So be able to go against him every single day. You know, that's a blessing. Ryan was on the ground for a lot last year. Yeah. You guys have it out there purposely, like, all right, that's not happening this year. Of course, you know, it's, you know, part of practice every single day is working on things like that. You know, obviously we love Ryan. We want to keep him upright as much as possible. That's what practice is for. So he's got to keep going, train in the right direction with it. No, for sure. You know, starting practice, we didn't come out to a hot start, but as practice started going, we got comfortable. You know, we started getting a tone and we started getting at it. How come you said you didn't get out to a hot start? How come? You know, bravely switched it up on us today. Right after stretch, we went straight to team.  And how important is that to kind of go through that through training camp, get those kinks out of the way and get mentally prepared each day? Oh man, it's very important.  You just got to get it all down now. So when the season comes, everything is smooth for you. And what changes when the paths come on? Oh man, it gets physical. You know, guys get the chirp in, it gets really physical. Especially when the heat is out, you know, guys get after it. Just be careful, be specifically locked in today because guys are excited about the pads you had a day off yesterday. What's the challenge of getting locked in quickly? Honestly, we just got to come out here every day locked in regardless if we have pads or not. It doesn't change our mindset. It just changes the fact that we just got to get banging a little bit. For you, how much did you look forward to this opportunity to prove what you can do? Man, I look forward to every day out here because I got to come out here and carve a roll out for myself. Nothing is sweet out here, nothing is handed to me, so I just got to get out there every day. What, in terms of the defensive aspect of things, we all know what you do special teams-wise, but you found a little niche there last year. Do you have to find the same niche? Do you carve out a different niche? You know, I just try to come out here every day and get better, whether that's on the field or, you know, this year I'm trying to focus mentally. And with my leadership role, just getting the young guys ready to go after it, especially in special teams. You know, guys coming from college, not. Special teams isn't really too big of a deal in college, but in the league, it matters. There's a lot made of the guys, quote, unquote, in front of you at your position defensively. Oh, for sure.  Oh, man, when I got here last year, they found a roll for me. You know, I just got to come out here and practice each and every day and go hard as hard as I can every day. So as long as I keep doing that, I know this coach is going to find a way to put me on the field.


Ovid Hosagrahara. Ovid, are you there? I don't know if his audio works because he's not speaking. We'll just give him a few seconds. Hello, can you hear me? We can. You're here, Ovid. I don't know if you can hear me, but we can hear you. Go ahead. Hello, can you hear me? Can you see the screen? So I'm here today to talk about the automated, automated semantic segmentation of large datasets. Sorry about the IT difficulties. I wanted to start this talk by introducing myself. I am sharing it. It does not want to... Getting to see your screen. Okay. Everything looks good. Is this shared? I'll try to make up for time here, but I'm here to talk about automating the process of semantic segmentation of large datasets. In this particular talk, I'll be focusing on three main things. The first one is a big picture. What is semantic segmentation? What is it useful for? A quick demonstration of the results of such systems. Then talking about architecture of how to build out systems to not only handle your extract, transform, and load, your ETL, your training and inference as a series of pipelines that allow you to process larger datasets. The third part of the talk is to talk about the design considerations. What we learned in putting together systems of this kind, discussing maturity models in building this and talking a little bit about the infrastructure that surrounds it and conclude. So to start with, as the human race is moving towards safer transportation, you might have heard about automated driving or self-driving cars in the news. And what goes inside these self-driving cars can be broken up into three sets of functionality. The first one is perception, where the car knows where it is, what it's doing, be able to localize itself and be able to sense the world through a variety of sensors. Cameras, radar, LiDAR, a whole number of sensors that you can put on the car, then goes through a certain planning process to decide what to do, which then indicates the control system, the actions that need to be taken. In today's talk, we're going to focus down only on the perception stack and focus even further down just to the camera feeds or the video or image processing side of it. And within that, focus down to just one particular kind of image processing called a semantic segmentation. Semantic segmentation is the classification of every pixel in the image or video, giving an ability to simplify your processing, to be able to figure out different classes of objects that the camera sees. And the reason you do that is because it makes it easier to validate localization modules and you can produce higher quality perception stack for the car to know exactly where it is. We want to automate this across larger data sets because the larger the more amount of data you can push through these pipelines, the better models you arrive at and faster design iterations. In this particular case, a video of a very standard data set being classified out into bicyclists, pedestrians and the road as well as the sky. So, though, why people are doing this is because there is a quote here from a research paper, an interesting research paper that talks about the amount of data you need to be able to push through these systems to be able to demonstrate reliability in terms of outcomes. And they talk about millions of miles of data, hundreds of billions of miles of data with a B. It's a lot of data, a lot of work to be done. And that's what makes the problem particularly challenging. In this particular case, as a problem statement, how do we accelerate the development of semantic segmentation modules, go from prototype to production, scale the workflows, capabilities and engineering processes to be able to automate this and turn the crank through design iterations faster? As headwind or challenges on this, we want to establish system and software engineering processes to take quick ideas, prototypes and production. This is a very rapidly moving space in research. Every year there are new models and I'll be showing you a little bit of what that looks like. Verify and validate this to modules for to be used in safety critical systems and be able to enable self-serve analytics to the data scientists so that he can actually leverage the cloud, handle bigger data sets, write less and do more. So I'll jump to a quick demonstration of the results showing what a typical iteration cycle looks like. In this particular case, here is a video, it's taken on the streets of Medrid, off to your right is the famous football club Real Medrid in the stadium, their home ground. I picked up this video because it has a number of features and the front-facing camera of one of my colleagues' car captured a little bit of video. As you turn design iterations on this, the first iteration is using a pre-trained model with absolutely no training data and you can see that it tries to segment the image. This is actually a VGG16 model from about 2015. It doesn't do a very good job. If this is what my car was seeing, I would not feel very comfortable. But as you start processing through ETL pipelines, making sure that you're correcting for lens correction, adjusting and color and contrast, you can start seeing these iterations get better and better at automatic segmentation. So in this case, you'll see that it's able to pick up the trees kind of well and now it gets a little better. It's able to see the road and trees with certain amount of, with better resolution, it clearly gets better every design iterations. So what we're trying to do here is to build a system where you can turn the crank and produce a new design iteration, run against your large data set, validate it, measure accuracy. As a fourth iteration, we switch over to a much more modern model, deep lab V3. You can see that I'm now able to pick up the motorcyclist a little better. The road is still, it's still very noisy. But the idea here is that all of this is being done without retraining, completely hands-free in an automated fashion. When I do contrast corrected, now we are beginning to look like, you know, better video, it's able to pick up the traffic signs. We're able to gradually improve the quality of this. As a snapshot, if I were to switch out to a system where you have taken this, not just augmented your training data, but also added 3D simulation data, you get results where the semantic segmentation on top, the noisy image on here top keeps getting refined until a point where you're doing pretty good semantic segmentation. You're able to see the road and the sky and the trees, the pedestrians. Your car is able to see the world a little better for it to be able to do it off the camera feeds. So as a nutshell, this talk is, the topic of semantic segmentation goes pretty deep, the techniques for doing this. It can be roughly spoken about the technique and then the systems and how do you scale it. In this particular case, you can see the results of a very standard data set, the road, the sky, the trees, the buildings, other vehicles and pedestrians being properly classified. So with that, I wanted to leave a couple of slides here to say this isn't a science experiment. There are industries such as a white paper by General Motors and the math works talking about how to reconstruct the scenarios and how to compare it with real recorded videos, taking all of this with the data and scaling it up to a much larger data set out on the cloud. The other use case here is Caterpillar where they were comparing different algorithms to select them and taking it down all the way to their equipment. That's through code generation down. Some of this systems again scaling up to larger cloud data sets, which brings us to a system, the architecture of a system on how to do this. Now this picture is a system that we stood up on AWS for doing this kind of work. It's very busy. I'm going to try simplifying it a little bit. At the high level, you've got several building blocks for starters, a development environment, both desktop-based and cloud-based, that connects up to cloud-based storage, allowing you to ingest lots and lots of data. An ETL pipeline, extract, transform and load pipeline, where you're enriching data as it comes in, being able to run these inference engines and being able to connect it up to other systems and monitoring for you to be able to see how your models are doing SS accuracy, SS performance. Finally, some parts of this are actually expensive. So being able to run a training, and I will talk about a few benchmarks on these cloud-based systems, it becomes important to ensure that you can burst up to the cloud where you can use bigger hardware and more data and faster networks. And ingest the recorded data, not just recorded but synthetic data, simulated data to augment your training sets where classes are missing. So simplifying this and simplifying this picture, you can break it up as a few pipelines. The first pipeline being ingesting data, enriching it as it comes while you store it. The second one is as you train your data to turn the crank on the training and build better models. This is a space that you want to be able to, if there's one thing constant about this, is that these models are rapidly evolving. Newer models are being made available by the research community. And one thing we know for sure is that the state of the art is moving very quickly, so you want to be able to keep up with agility. The third is to be able to offer that up as services where you can actually connect it up to storage and then ingest larger data sets while monitoring performance. As a software stack, what we do is build out the system on AWS using a set of technologies for orchestrating, for rebuilding this, for securing the data, and just a quick snapshot of what the software stack would look like. Taking a little deeper dive, we can break each of these three pipelines into the local experimentation or how do you build these models? You can build them optimized for GPU execution and using higher level APIs allow you to write less and do more. So language abstractions such as the image data argumenter or train network over here allows you to supplement your training data with data that has been augmented for better accuracy. And all of this while tracking your experiments, sweeping it through different parameter, hyperparameter settings, tracking how well your algorithm is doing, so applications that will actually help you pick models as the quality of what you're building as you build it. As you do this, it becomes possible to burst this, burst the need and leverage bigger instances on the cloud. Defining your infrastructure as code allows you to version and reproduce these results and certainly leverage the latest hardware drivers and toolkits as they become available on cloud-based data centers. There are APIs for command and control of these systems. So it's a very natural syntax where you can define what kind of machine you want to and scale up based on your needs. This also allows you to take your capital expenses and turn them to operating expenses as opposed to buying big expensive local machines and being able to rent out a machine to be able to do this kind of training work. Once executed, you do have the ability to store the training model so that you can fetch it and operationalize it on the inference pipeline. In this particular case, I wanted to share a few benchmarks. In this case, when we picked up a VGG16-based network locally on NVIDIA Quadro, you can see a well-behaved trace in performance. The training time keeps reducing until we reach a kind of asymptote based on the kind and architecture of the network. So as your mini batch sizes go beyond 32, you start reaching limits of what your GPU memory can handle and performance degrades. So tuning these hyperparameters for the most ideal training, for the most amenable training will allow you to then take it from local instances up to the cloud where you deal with single GPU instances and then scale that upwards. The most expensive local setup I was able to find represents almost a 2x from my starting point. And when I do burst this out to the cloud, I'm running nearly 3x as fast and just completing a 5-hour training task on a Titan in about just over an hour and a half. So that becomes one way that you'd actually increase your, you can scale your compute elegantly. Once your model is available, plugging it into an inference pipeline allows you to build directed acyclic graphs of, to be able to resolve directed acyclic graphs of processing. So that any changes to your training model, your training data, any changes to the ETL code, all of this can be built and you want to be able to turn the crank automatically to be able to produce a new iteration. These can be deployed as cloud services and we provide on github.com reference templates for you to be able to quickly doubt-tail it into your systems. This pipeline metadata defined once can be orchestrated orchestrated in a completely automated fashion. To show you a few benchmarks of that, the performance of the frame throughput improves kind of linearly. We are able to scale up more machines, more CPUs. At this point we had about 350 CPUs fully maxed out and the same applies to GPU. Picking the right instance types will be able to use this resources optimally. Finally, changing it to operating costs, you are able to build this and use it as you need. The third pipeline is there's a lot of value when handling big data sets to enrich the data as it comes in because every IoT problem is a big data problem in disguise. You want to be able to enrich and do as much of this on the smaller data set as you aggregate it to prevent very large bottlenecks with very large processing later on. As the ETL, what you're seeing here when I bring up code is the entirety of the code. You're actually doing almost the entirety of the code. You're doing lots of work, writing less and doing more. In this particular case, on the ETL pipeline, things like lens curvature corrections, contrast and color, and there's a whole bunch of image processing tasks that happen to be able to ingest the data in a form that you can actually scale it well. In this particular case, exposing that on production server as HTTP based services gives you the ability to leverage the different engineering formats that higher level toolboxes for dealing with this and expose them as 24, 7, 365 services running proximal to your data and accelerated on GPU hardware. Putting it all together, these different pipelines bolt together in the form of build graphs where any deltas are in changes to either the stored version metadata, the training data by looking at the version metadata, changes to the code, changes to the model, all of this can be built out as a DAG that can then be triggered automatically or via a CSED pipeline connected to performance numbers, at which point this semantic segmentation system, that is the infrastructure stack behind it. Let's talk a little bit about the design considerations in building this. For semantic segmentation, leveraging cloud storage allows you to ingest larger data sets and scale up both your computing, both in terms of CAPEX and OPEX. These self-serve analytics, there are anti-patterns having research and engineering roles separated. By making it self-serve, you are actually allowing your data scientists and researchers to be able to work more efficiently without building these ivory towers and be able to work on a single problem by self-serving their needs for more compute. As accuracy considerations in our data set, you'll notice that certain classes, we have very few instances of symbols that shows in the quality of the segmentation, if you may. You can always supplement that and that's where simulation comes in. So in our recorded data set, we may not have enough of a certain class, but it becomes very easy to generate scenes, be able to create synthetic data, be able to create synthetic scenes, which give you perfect semantic segmentation that you can use to augment your training data sets. The last part is this particular space, and the community is evolving so rapidly that it's very important to interoperate. So being able to talk on ONIX, to be able to communicate with ONIX standards and leverage transfer learning so that you can ingest the latest model structures, the latest model architectures, from a variety of different technologies and import, export them to standards that are common in the industry. Being able to connect up to automated simulators, being able to build these systems at scale so that they interoperate with the rest of the ecosystem is very important. The last mile is being able to take that and push it down to GPUs, to CPUs, to embedded microcontrollers. The last mile to get into the vehicle and retrait, that becomes another source of fiction. Trying to do this, trying to turn the crank quicker on that is always helpful. That is a topic by itself. So zooming out all the way, to build out a system like this that does automatic semantic segmentation, we can talk in terms of maturity. Starting with local experimentation, automating some part of the training, going down to model deployment, being able to performance, to measure your performance, to be able to automatically release your new models with full traceability and manage environments down to a system that supports MLOps, which allows you to do your machine learning, but be able to do it in an automated fashion, re-triggering both the training and keeping it in a traceable and governable way. There are frameworks here. I've just left a reference to one such maturity model, but there are others out there. The need of a maturity model allows people to assess their software processes in an organization to see how they can actually build these systems in a continuous way as you actually develop and deploy. The last part of this is while a researcher is testing his experiments locally, centralizing it at some point will allow you to collaborate with larger teams, and that becomes available. As security on the cloud, we do use best practices, access control lists for permissions and cloud services like IAM for making sure that the right people have access to the right data sets and the right services. So in conclusion, building out these systems, architecting it will allow you to turn the crank on automated semantic segmentations, scale it out on the cloud both in terms of compute, in terms of storage, and providing a smooth workflow reduces the friction for you to be able to turn the crank and produce new trained deep learning models. Doing this on the cloud allows you to enable self-service analytics, and these techniques, even though this particular case has to do with self-driving cars, are equally applicable to other domains such as medical, gear, exploration, etc. As key takeaways, build upon proven safety compliant and DevOps ready development tools that will allow, that will reduce friction because you are using battle tested strategies for doing this. Leveraging best in class simulation improves the quality of your output. It leads to faster time to market outcomes for ADAS development and using domain specific tooling will allow practitioners in this space to write less and do more. So reduce the effort that it takes to be able to do this, build it easily and quickly. With that, I'd like to conclude by pointing to the virtual exhibit hall. Please feel free to stop by, meet our team or post your questions at which point I should be open for Q&A. I seem to be having a little audio issues in terms of not being able to hear the Q&A, but I'll try to do my best. Hello, Arvind. Yes, I can hear you now. Okay, that's fantastic. So I'm glad we managed to solve those technical issues and we managed to get your talk. So that was a relief, had me very worried for a while there. That makes both of us. Okay, I could see you were starting to panic. But you have this very calm and very soothing voice. So it was a pleasure to listen to you as you were giving us this talk. That was wonderful. Thank you. Yeah, thank you very much. So I think what we would like is for you to give us a bit more information on real-world applications, perhaps outside of driving and how that might evolve in the future. Correct. It turns out that being able to spot a pedestrian in an automated driving scenario is not too much different than being able to spot a cancer cell in an MRI scan or to be able to spot features in medical imaging that are of interest. So deep learning systems like this are increasingly seeing adoption in being able to assist physicians for better medical outcomes. In the manufacturing space, we see people that are actually trying to improve their manufacturing processes to be able to do predictive maintenance, to be able to do it based on visual feedback from equipment, cameras pointed to their assembly lines. There's a whole variety of techniques, but that's a whole different industry. These techniques are also applicable in geo exploration for people that are analyzing very large data sets of geographic information to spot natural resources to explore the reserves of natural resources. So there's a whole variety of these kind of use cases across multiple industries and we'd be very happy if you connect up with us to connect you up with experts in those spaces. Well hopefully lots of our audience will do that via the networking section on the platform. So let me ask you another question Arvin. Let's imagine that you return to Big Things next year, Big Things 21. What do you think you'll be able to tell us then? What would you hope that you'll be able to tell us on how things have moved on? Yes, there's a very exciting branch of research, things like network architecture search, being able to find and spot the right architectures to make your problem fold correctly. Being able to do domain transfer, which is essentially map your simulation results to your recorded data and map that in a semantically accurate way, which essentially allows you to get to your billion miles. It'll take 400 years to drive a fleet through billion miles. But that's where simulation comes into the picture and there's a lot of research. These models are evolving very rapidly. Just with the videos I showed you, we went from models from 2017, 2018, 2019. We're actually moving closer and closer to the bleeding edge of this and if we do come back to Big Things Spain, we'd like to show you the latest iteration of that. That would be great. Havend, you showed us some very detailed slides there. There was a lot of information to take in. I certainly didn't have time to unpack it all. Will we get a chance to have a look at these slides? Can we download them or view them somewhere? Absolutely. We will try to make that accessible. I'll work with the organizers to make sure that we get them a print ready copy of these slides. You may not have the videos, but everything else will be here. It just remains for me to thank you so much, Havend, for your time. I'm so glad that we managed to solve those technical issues. Hopefully, we'll see you again next year. Thank you so much. I apologize for the technical issues. It's very nice to be able to present.


Hello everyone, welcome to Rural Water Resource Management NPTEL course. We are looking at groundwater hydrology and different components. In this week, we are focusing on certain components that are very important for understanding groundwater hydrology and for conducting recharge and discharge experiments and also to better manage groundwater hydrology. By managing groundwater hydrology, there could be good potential for rural water resource management. So this is the aim of this week's lecture on identifying groundwater parameters and to see how we could maximize rural water resource management. Groundwater plays a key role during climate change extremes like floods and droughts and also it creates a decentralized access to water rather than having a centralized water scheme. For example, irrigation schemes and or dams and canal water etc. So let's get into the peak discussion on what are the key properties for understanding groundwater hydrology. Today's lecture would get into hydraulic conductivity. In the previous lecture, we gave an introduction to hydraulic conductivity but did not get into full picture. How it is estimated etc. Let's start with the person who was very keen on identifying hydraulic conductivity. He was Darcy who was a French engineer by background and his work was to maintain the water fountains in France and he found that he need to have a better equation for flow of water through the system and there it was. He discovered the groundwater flow equation and also found out some key parameters. Let's first look at his experimental setup which is a very basic setup. Everyone can do it in the lab. So he had a column of sand stopper at each end. So you see here this is a tube in which Darcy had put sand full on both sides and then stoppers, stoppers like rubber stoppers or even cardboard. So something that can stop the sand from falling and also stop the water from dripping. So let's assume it was rubber stoppers and you had tubes to funnel the flow so that you could easily capture it rather than dripping down. So it was tilted to an angle so that gravity can act and pull the water. So that is the only force in this experiment. If you see there's no pumps on the bottom to pull the groundwater. It is purely through gravity and the intrinsic properties of the soil. For example, specifically permeability, permissivity, etc. etc. All of these which are functions of the porosity, the densities properties of the soil and also the properties of the fluid like viscosity and density. So what happens is first he slowly input a known volume Q into the experimental setup. Water saturate the pores. So first it was unsaturated soil. So when you start pouring water, slowly the water would saturate the pores. The same problem can be done with soils or rocks or any medium where groundwater hydrology is discussed. So first thing you saturate the pores which is fully, fully sanded with water. If you fully pour water on it fully in the sense of to maximize all the pore spaces to fill up with water, then water fills up and starts to drip down. So you need to monitor how much water you sand and how much water you get. Q and Q which means you want to get the full water. So first saturate the column so that whatever water you apply can be taken out on the other side. Constant volumetric rate of inflow and outflow of water was maintained at Q. So this was the only engineering concept where the water volume coming in should be equal to the water volume coming out. There is no storage of water here. Why? Because already it is saturated. Unless it is saturated, you would not have this condition. For example, let's take an unsaturated soil. What would happen is water would store in these pores and a minimum water would come out compared to the water you're applying, reduced water would come out. In this case, what is happening is since it's saturated, the full water passes through the column. Then he had two tubes to monitor the hydraulic head or the water level. So one tube was at the top end and another tube was at the top bottom end. So the equation was done to find the water flow between these two tubes, not inside versus outside, but between the two monitoring tubes. These small monitoring tubes can be assumed as a well, whereas your tube with the soil can be assumed to be the groundwater system like an environment. So in an environment like in a landscape, this could be your landscape, has a slope gradient so that water can come down and you have two monitoring wells. Now each well is recording difference of water table height because of the pressure and it equates to the atmospheric pressure. So when water flows in, let's say it comes up to this height, which is h1 from the zero, the datum, the mean ground zero. So the zero was taken to be the zero where the stopper tube touches the ground and also the zero was common for all the measurements. So you have a datum which is zero and from there h1 was the first tube's water level height or the well's water level height and then the second well recorded a water table height at h2. The difference is del h and then you had the elevation, the elevation of the opening of the tubes or the wells. The opening of the well was z1 and then z2 for the second well. So all you needed is if you have a well, what is the opening, the base, the base of the well from the zero point is z1 and z2 and then you had the height of the water table, h1, h2. Suppose you don't have water, what happens? Your h1 is equal to z1, which is the base of your well. What else was measured? The distance between these two wells was measured, which is del l, change in l. So Darcy's law found out that q, which is the discharge coming out of your experimental system, was proportional to the negative area of the cross-section, the tube's cross-section or your cross-section in the ground and del h by del l, where del h is the height of the water table difference between well 1 and well 2 and del l is the distance between them. Once you have a proportionality, you have a proportionality constant and q is equal to minus. So to convert proportionality into an equal, you put a proportionality constant, which is minus k. Minus comes to the k now. So you have minus kA del h by del l and q is a volumetric concept wherein a discharge as a volume is being measured. Suppose you wanted velocity. Velocity is one dimensional, whereas q is three dimensional. So you take q divided by the area, the big q volume divided by the area, which is two dimensions, you remain at one dimension, which is length. So length is here. And you have your length, etc. here. So meters per second is your k. So let's look at q as a volumetric flow rate.  Hydraulic head is l in units. So you have a single dimension, whereas l portion point in flow direction is here. So what do you find is the units dh and dl cancel out. So q takes the units of hydraulic conductivity, which is meters per second, or centimeters per second, which is l per unit time. So why is it negative? Negative here is not a concept of reducing your flow, but to tell that the flow is in the reducing direction, which means it goes from top to bottom, high potential to low potential. So it is a direction. Negative here is a direction concept, not a reducing concept. So when you add this to other values, for example, you want to measure the value of water coming in a groundwater system, and you already have one liter. Suppose one liter is coming through this tube, you estimate it through this equation. You cannot put a minus sign saying that minus one and then another minus one liter in my cube or well is going to be zero liters in your bucket. No, it's not that. So negative is a direction. You need to use it. But when you're talking in absolute terms, the negative goes out, it becomes a quantity. So negative tells that your volume is coming, but in a negative decreasing trend. So slowly all the water would come down and it will reduce. So that is where the negative comes. So in absolute terms, you can remove the negative sign. So this is taken from Fries and Sherry, the groundwater book, as I said, which I'll use extensively in this course. Flow is in the direction of decreasing head, which means your head is here high. H1 is high compared to H2. So it is in a decreasing head direction. To let that know for the readers and for people who are using this equation, Darcy put a negative sign. It is not something that you can add and subtract. It is a sign to give the direction of flow. In other terms, Q is also looked at upon as the velocity. So you have your discharge and velocity. Discharge is a volumetric concept, whereas your velocity is a meter per second concept as a flux rate. It's a different diagram from a different book. Here you don't see the slant in your tube, but it's the same concept. Horizontal pipe filled with sand to demonstrate Darcy's law. Darcy's original experiment was actually vertically oriented, as I said like this. Here they've kept it in a planar vision to show that you can also push water. So they're pushing water and H1 well, or here it is HA well. A well will rise, the water level is rise to HA and then at B it rises to HB. So it is a reducing trend. From HA to HB, there is a difference and there is a difference between the distance of the two wells. So you take that as DL, the distance between the two wells as small DL or change, and DH is the change in your hydraulic head between the two observation points. So what it also means is when you do a groundwater equation, normally you take two points and find the flow. Then you go to the next two points and find the flow. DH by DL is also called as the hydraulic gradient because that drives the direction of flow. So DH by DL as a single unit can be called as a hydraulic gradient. So for example, if my hydraulic gradient is going in the negative side, you could show that it is DH is going on decreasing and so it is in the negative side. Conversion of units is very important. Hydraulic conductivity can be given in multiple units, but especially if you equate it, you'll get it as meters per second or L by T, length by time. So here let's look at gallons as a volumetric concept and feet square is an area concept. So if you take L cubed by L2, you'll have L, which is one dimension and days is a time, so L by T. So all these would be given the basic length over time concept, but in different units and unit conversions are well. So when you read different books, always pay close attention to the units. Do not assume it is always meters per second or centimeters per second because it will become a drastic change. I've seen a lot of reports where they missed it. They thought it was meters per second, but it was centimeters per second or millimeters per second. It depends on your experiment and how they estimate it. Hydraulic conductivity has good relationship with intrinsic permeability, which is having units as Darcy. So Darcy also has a unit after him because the intrinsic permeability was estimated during his experiments. And it came out very clear that it's not just the process or the property of the liquid, but also the property of the solid matters and permeability, which is the amount of permeability by the solid. How much the solid permits the water to enter is a key factor for groundwater hydrology and it differs between the materials in the soil or rock and also the size class. So if you look at here, clay, sands, well sort of sands, well sort of gravel all have a size difference. Similarly, they also have a range between themselves. Clay can range anywhere from size the given buffer or within a particular range. It has a different size classes. So depending on that, your intrinsic permeability also has a range, so as your hydraulic conductivity. So let's look at some hydraulic conductivity from this book, Applied Hydrogeology from Fetter, which is also a very good book for groundwater hydrology. So 10 power minus 9 to 10 power minus 6 centimeters per second. And you could see that it slowly gets bigger. The hydraulic conductivity gets bigger because when it goes to positive, it becomes bigger. So 10 power minus 2 is much bigger than 10 power minus 9. So you can look at how slow, how slow the water can move in a clay system. So 10 power minus 9 centimeters per second. So multiplied by the number of seconds in a year. And you could see that barely the groundwater would move only a meter, but not even a meter, much, much less. That is why if you remember in one of the courses, we looked at groundwater recharge and movement and I estimated it to be somewhere in years and days for a shallow aquifer. But for a deeper aquifer or a confined aquifer, it ranges from hundreds of years to millennia, which is a thousand years. And this is where a distance of even one kilometer from the recharge point to the access point for groundwater can take years. So it is very important to understand why it takes years. It is because the function of the porosity is a function of the fluid and the solid material. And you can identify that by just looking at the material. Like if it is a clay, then you can clearly say groundwater is going to move very, very slow. You can easily pull it out. That is different. That is your pump's capacity. You can put another pump and pull it out. But here what we're talking about is your recharge. And also if there's water far away and you want to put a pump and access all the water, it won't move. Only around the radial circumference of your well, you can access water. So that is why some wells are abandoned because they assume to be giving unlimited water supply, but they run out pretty soon. So it's very important to understand these dynamics and dynamic properties when you do groundwater management. So the basic thing here to take off is you can identify the material and then go back to these books to identify the hydraulic conductivity. You have centimeters per second. Then if you say you're going to put a recharge structure on the top, let's say a check dam. And if someone asks you how long does it take for water to move from the check dam to your deep aquifer or the well, you could estimate the distance, both depth. And then you can look at the hydraulic conductivity. Now you have the distance. You have the velocity. You can calculate the time it takes for the water to move. The next class, I'll give you some examples on how much time in a year you have. And let's take one year, for example, how many seconds you have. And given one year and this hydraulic conductivity, how much distance does this water move in clay? Let's do one example for clay and one example for well sorted soil. Let's do that in the next class. You can also go back and compare between books on the values. Let's do a quick comparison. Here clay ranges from 10 power minus 9 to 10 power minus 6 centimeters per second. And let's look at clay. You don't have clay. So let's 10 power minus 6 to 10 power minus 4. So silty sand is around here. So it has values at 10 power minus 4. 10 power minus 5. So 10 power minus 5 until 10 power minus 1. So you see that even between your well documented and well cited books, you have some changes. There it was 10 power minus 5 to 10 power minus 1. Here it is 10 power minus 6 to 10 power minus 4. There is some differences. So you need to be very careful in what you are estimating and which book you are consistently using. So use one book or reference consistently throughout your studies or even your assessments. Good. So we have looked upon hydraulic conductivity. We will stop here at the recap class. I will show you an example of how much distance water given your centimeter per second. And then we also looked at hydraulic conductivity as a property. It can change pretty fast. And we'll also look at some concepts of hydraulic conductivity in the next class in the domain specific. The ranges always know the ranges and there will be overlap in the ranges. For example, if someone gives you a hydraulic conductivity value and you need to estimate what type of material it is, it is also going to be difficult because let's take this one. If it is 10 power minus 4 they give you, it can be a silty sand or a silty lows or even a clean sand because it is in the range on the buffer on the border of the range. So all three materials can have a similar hydraulic conductivity. So it is your utmost duty to find where or which type it is. So always you can say it depends on where the sample is being taken, how it's been managed. But if you want the exact median, the median of the range, then you can say it is a silty sand. Let's look at water levels. Since water levels we discussed in your experimental setup in Darcy, now you know that given two water wells I can estimate the discharge between them or the hydraulic conductivity and the groundwater flow given by Q. So water levels are very important and that is why the Central Groundwater Board and all government groundwater boards are monitoring the water levels. Whereas the geology, the soil type may not need to be monitored regularly but still you can get away with having a long-term database on your soil or your geology material. What you actually need is the dynamic changes in the water level to estimate groundwater flow. Let's take an example from another water department. What you could see here is you could look at Kanchipuram District groundwater level from 1991 to 2019, almost 30 years. And what you could see is the water levels are going up in some locations and going down and up cyclic in some locations.  You have a monsoon and then a dry period, monsoon dry period. So the same thing is captured in your water levels. It also tells you a secret that the well is in the shallow aquifer because as per the class we discussed, the recharge can take days to a year if it is in the shallow and your rainfall pattern happens every year and same way you are seeing a pattern going up and down every year which tells that it is a shallow aquifer. Suppose it's a deep aquifer, you don't see the cyclic pattern that often. It will be buffered because it takes long time for the water to come. The other reason for the cyclic pattern is also the extraction. A farmer would extract water only in the summer season, not in the rainfall season if it is a good rainfall. So when they extract the water level comes down. So along with summer where evapotranspiration occurs also, you are extracting water due to the pumps in groundwater usage during summer. And that leads to these kind of images of depth to water level in your monsoon post monsoon etc. So August here would be your monsoon season and that is why you have good green color in central India etc. So green and blue are healthy water levels and what you see here is there is good amount of water levels in your wells most of the wells because it has been recharged by the recent monsoon in 2019. So given your water level now you have a database for water level. Now you understand how they collect data is by putting a tube or a meter in the well and estimating the depth of the water level, how deep is the water level from the ground. So you have the ground surface and you put a tape here to measure this level. Suppose it is 5 meters. So you would record it as 5 meters depth to water level. Now comes your different parameters in your hydraulic conductivity estimations. We saw hydraulic head, we saw hydraulic potential, how do you estimate it? So you estimate it by the methodology given by fees and cherry in groundwater book. You have your datum which is zero your sea level, your sea level is zero. So for every location you can get a datum which is your elevation, okay elevation from the ground surface. So let's say that you have this data to be 100 meters. So the ground is at 100 meters from your datum which is your sea level okay and your well is 20 meters deep. Of the 20 meters you have water level at a depth of 5 meters. So what is this water? It is 15 meters. So let's do the calculation here. I can also write it down for you. So you have this to be 100 meters, right? We have 100 meters from zero to ground and this is 5 meters and then this is 15 meters because the depth of the well is 20 meters. Now how do you estimate all the other parameters? The depth of the well is 20 meters or the height of the well is 20 meters and from the ground you have to estimate these values. So let's do one by one. From your ground surface which is at 100 meters you know h which is at this level at the 15 meters from the base of the well. So what would this be? So it is just 100 minus 5 correct? So the whole thing is 100 which you have until this and you know the depth to the water level is 5 so this would be 95. Since you know 5 meters is the depth of the well and you know the depth of the well is 20 meters which is 20. You have estimated the remaining which is 15 so 15 plus 5 is 20. So psi is 15. Now you know 20 meters is the depth of the well which means from the top it is 20 meters and the ground is at 100 meters so this would be 80 meters which is 100 minus 20. Okay now all these values have been estimated so the hydraulic head which is h is 95 meters all these were estimated just using one value which is your depth to water level and you should know the depth of the well the base of the well where the measurement comes through. So if you have a tape you can go through the depth and find the depth of the well or it should be on the logs when they made the wells and you have estimated the depth to the water level. Just using that level only that you need to monitor every other time you can estimate the all the other parameters.  Okay so all these parameters you would use but what would you use for your hydraulic conductivity experiment it is the 95. So one well is 95, suppose the other well is 90 then del h is what five meters and if the distance between them is 10 meters then del h is five, del l is 10 meters. Then you have the area of cross section and you can estimate q using the hydraulic conductivity. So all you need is the area of cross section between the two wells and the or you can even let go of the area because if you want q which is the velocity of the water you could just take k times del h by del l and here you do have del h and del l and the k you can take from the understanding the material of the soil and you can estimate your flow. So the end goal is to estimate the flow. So this is what hydraulic models do for you if you just give the water level okay. With this I would like to stop today's discussion on hydraulic head hydraulic conductivity and water levels. In the next class we would wrap it up and also look at the changes in hydraulic conductivity across the system. So this is how data is measured I would just stop here and please use units consistently all these units should be of the same SI or British or any unit you follow please use the same scheme for everything. With this I would conclude today's lecture. Thank you.


Hello, everyone. This is Professor Farhad. In this session, I will explain how Apple's 4-to-1 stock split will affect the Dow, and I will show you that in numbers, specifically how does it affect the Dow. Yesterday, July 30th, Apple decided to do so. In other words, if you own one share of Apple stock and the share was traded, let's assume it's traded at 400, at some point by the end of next month, you're going to wake up with four shares rather than one share, but the price will be $100. Now, why did Apple do so? Well, your value does not change. If you have one shares at 400, your value is 400. If you have four shares at 100, your value is still at 400. So as an investor, you are still in the same position before and after the split, but now you have more shares. Why did Apple do so? One reason that Apple quoted is because the price seems to be a little bit high for average investors. They want the average investor to perceive the stock as cheaper, so that's part of it. There are other reasons we're not going to go into this, but how does that affect the Dow and why does this affect the Dow Jones? The Dow Jones Industrial is a price-weighted index. It means the 30 stocks, the 30-member blue chip index, the stocks, which is Apple is part of it. The index is influenced by the price of the stock, and right now, Apple, I will show you in a moment, has the highest price in that group. And what's going to happen once the index is adjusted, it's going to be toward the middle, the 15th or the 16th most significant member. Right now, it's the most significant. It's going to go from number one to number 15th and 16th. The Dow price-weighted means that the value of the stock gauges is determined by the price changes of its components. So as the prices of the component changes, the Dow changes every day. So the overall value of the index is computed by adding the price of the component and dividing by the so-called Dow divisor. Now, this Dow divisor is giving to us. It's computed by the Dow and currently stand at 0.14745683, so on and so forth. This means that every dollar move of a company translates into 6.8-point swing in the Dow. So let me show you on an Excel sheet, how does this all fit together and what does that mean from a numerical perspective? So what I did is I took a snapshot from July 31st. So again, those prices are changes constantly from the Dow. And this is the last price as of July 34th. I took the snapshot around maybe 9.45, 10 o'clock. I don't remember what time. But here's what happened. Here's Apple United. I have them all from the highest price to the lowest price. And notice, just want to clearly show you right now, Apple was trading, when I took the snapshot, was trading at 400 and Pfizer is the lowest, you know, has the least influence because it doesn't have a high price. Simply put, if you add all the prices of the Dow, they are up to 3,836. You don't take 3,836 and you divide by 30. You'll get something around 127. That's not the price of the Dow. The divisor is not 30. When the Dow started the first day, it was 30. Now the divisor is what I just showed you, 0.1474568 so on and so forth. So we can say 1.54 rounding. So simply put, and why did the divisor change over the years? When the company have a stock split, when they have a stock dividend, when one company leaves the Dow and another company comes to the Dow with a different price, they have to adjust the divisor. So divisor is given to us. Simply put, at this moment, when I capture those prices, if I take 3,836, the prices of all the Dow divided by the divisor, the Dow was trading at $26,016. Now what does that mean? It means each dollar in the Dow, if I divide the, if I divide $26,016 by $3,836 every dollar in the Dow, so each dollar in the Dow contributed to $6.78 change in the Dow. So when a stock, if Cisco system goes from 4616 to 4716, this will increase the Dow by 6.78. So each dollar will increase the Dow by 6.78. Simply put, let me do a quick computation for Apple today. So if Apple today increased by 2%, what does that mean? It's right now, it's trading at 408. If it increased by 2%, what I did is I took 408 times 40871 408.71 times .02, it will increase by $8.17. So if I take the $8.17 multiplied by the factor 6.78, the Dow will go up by 55 points if Apple goes up by 2%. At the end of the month, when Apple becomes, when Apple split 4 to 1, the price of Apple becomes $102.18. This is, this will be the new price, the new equivalent price of the price right now. So when we have the new price and the Dow goes up by, not the Dow, and Apple goes up by 2%, so 102.18 times 2%, that's going to give us the, the Apple will increase by $2.04 pennies. We're going to multiply this by 6.78 and it will contribute only to 13.686 points to the Dow. So notice it makes a huge difference. Why? Because Apple, it was at 400, the highest, the highest, the highest member of the Dow. And once it becomes 100, it's going to go down to someplace in this 100 someplace here. So someplace in this area, it will be someplace in this area. Therefore, changes in Apple will not affect, affect the Dow as much as today. That's the whole point of it. That's the whole point of the stock split and how does it affect the Dow? I hope you like this recording. As always, I'm going to invite you to visit my website, farhatlectures.com, if you're an accounting or finance students, especially if you're studying for your CPA, CFA, CMA exam, study hard, stay safe, and best of all,