From Midtown Manhattan, it's theCUBE, covering Big Data, New York City 2017, brought to you by SiliconANGLE Media and its ecosystem sponsors. Okay, welcome back everyone. Live here in New York City, it's theCUBE's special presentation of Big Data NYC. This is our fifth year doing our own event here in New York City, our eighth year covering the Hadoop World ecosystem from the beginning through eight years. It's had a lot of evolutions, Hadoop World, Strata, Conference, Strata, Hadoop. Now it's called Strata Data, happening right around the corner. We run our own event here in Manhattan, talking about the thought leaders and the experts, CEOs, entrepreneurs, getting the data for you, sharing that with you. I'm John Furrier, the co-host of theCUBE, with my co-host here, Jim Kobielus, who's the analyst, lead analyst at Wikibon on Big Data, and Chuck Yarbrough, who's the vice president of Penthouse Solutions, part of Hitachi's new Ventara, a new company created, just announced last week, of Hitachi and a variety of their portfolio technologies into a new company, out to bring in a lot of those integrated solutions. Chuck, great to see you again, CUBE alumni. We chatted multiple times at Pentaho World, going back, Tony, 15. Always great to be at theCUBE. What a couple of years it's been. Give us quickly, the hard news is pretty awesome. You guys have a variety of things at Pentaho, with Hitachi, that happened. Now the market's evolving. What's this new entity, this new company? Yeah, so the big news, Hitachi Ventara. So what that is, two years ago, Hitachi Data Systems acquired Pentaho, and so fast forward two years, and a new company gets created from Hitachi Data Systems, Pentaho, and a third organization at Hitachi called The Insight Group, so Hitachi Insight Group. Those three groups come together to form Hitachi Ventara, and... What's the motivation behind that? Obviously, I mean, I can almost connect to dots, but I want to hear your perspective, because it really is about pulling things together, the trend this year at the show is, as Jim calls it, hybrid data, hybrid integrated data. Things seem to be coming together. Is that part of the purpose? What's the reason behind pulling this together? Yeah, I think there's a lot of reasons. One of them is what we're seeing, not just in our own business, but in our customer's business, and that is digital transformation, right? This need to evolve, and so Hitachi Ventara is all about data and analytics, and a big focus of what we do is what Pentaho has been doing for years, which is driving in all kinds of data, big data, all data. I think we're getting on the cusp of closing out the big data term, but it's all data, right? It's data everywhere, every application. And applying analytics across the board, and one of the big initiatives, part of why Pentaho was originally acquired, we were actually, Hitachi Data Systems was a customer of Pentaho when we got acquired, so we knew each other pretty well. And part of the reason for that acquisition was to drive analytics in and around Internet of Things, the IoT space, which is something that Hitachi, being a very large IT and operational technology, OT company probably does as well as anybody, if not better. So going back a couple of years, I'm just looking at my notes here from our video index. You visited theCUBE in 2015, but really the concepts have evolved significantly. I want to just highlight a few of them. What data warehouse optimizations about, we talked about that. Data refinery concept, 360 view, as applied to big data. Again, that was foundational concepts that all are in play right now. Absolutely. What is the update in those years? Because the refinery, everyone talks about data refinery, the oil, they use the oil example, but I mean, come on. I mean, data is everywhere. It is the most important. You can use it multiple times, unlike oil, as you were pointing out. So interesting you bring that up. So to me, data refinery in a digital transformation or really in an IoT world, where lots of data is streaming through. In fact, yesterday I read something by IDC that 95% of all data in the future and the data growth is dramatic. It's 10x what it is today in just a few years. 95% of that growth of data is IoT related. The question is, how are you using most of that, right? And what are you going to do with it? So that data is streaming through. There's a lot happening. We can do things at the edge, right? We can apply analytics and filtering and do things. But ultimately, that data's going to land somewhere. And that's where that refinery, think of it as the big data center refinery, right? Where I'm going to take that large amount of data and do the things that Jim does and apply machine learning and deep algorithms to really predict the future. I like to get your thoughts on the IoT. Jim and I were arguing, not arguing, discussing with others on theCUBE about the role of the edge, because obviously the refinery, the data can come back depending on what kind of data it is. Or you push compute to the edge. Kind of known concepts, people have been discussing that. But the issue has been, how do you view the edge? I'd love to get your reaction to that question, because a lot of people are saying, you have to think of IoT as a completely different category than just cloud, than just data center. Because the way some people are looking at IoT, I know it can be semantics, whether it's industrial or just straight internet of things, device or person. But it's a different animal when it comes to like what you call it and how it gets put into a bucket. I mean, most people put a lot of things in the IT bucket. But some are saying, IoT edge should be a completely different category of how you look at those problems. Your thoughts on how that IoT conversation should shape. The question I always ask when I'm talking to somebody about the edge is, well, what do you mean, right? Because it is something that can be defined a little bit differently. But in an industrial IoT context, I think we look at it as one, you have to know what those things are. You have to really understand them. And part of understanding those things is having a digital representation of what those things are. One of the things that, a digital twin, right? Or asset avatar, as we call it at Hitachi. Oh, I like that. So this idea of really managing those assets, understanding what they are, and then being able to know what the current state, what the previous state, things are like that are. And then that refinery we just talked about is sort of where that information goes to so you can do other kinds of analytics, right? But when you're talking about the edge, typically what we're seeing is the kinds of analytics that might happen at the edge are probably more around filtering, not quite as complex of analytics. That's what we're seeing today. Now, the future? I don't know. You have tiered analytics from the edge on in with more minimal, I mean not minimal, that's the wrong term. With the more narrowly scoped inference, like predictions and so forth being handled at the edge with larger, more complex models being, deep learning, whatever, being processed in the cloud, is that it? Yeah, that's exactly the way that I see it. Now, the other thing about the edge, depends on who you're talking to again, but what is an edge device? Or the gateways or the compute, right? So part of IoT is, in my mind, it's not cloud, it's not on-prem or it's not, I mean it's a little bit of everything, right? It depends on the use case and what you're operating. We have a customer who does trains as a service in England, okay, in Europe. And so they don't sell the trains anymore. They actually manufacture trains and they sell the service of getting a passenger from here to there. So, but for them, edge is everything that happens on those trains and tracking as a digital representation, the train and then being able to drill down deeper and deeper and one of the things that I understand is one of the major delays for train service is doors opening and closing or being delayed. So maybe that comes down to a small part and the vibration of it and tracking that, right? So you've got to be able to track that appropriately. Now, on a train, you might have a lot of extra space so you could put compute devices that have a lot of power. What's interesting, you said that edge in this context is everything that happens on that train, in other words, it sounds like all the real-world outcomes that are enabled, perhaps optimized, by embedding of the analytics in those physical devices or in that entire vehicle, that is essentially one way that you're describing the edge, which is not a single device, but it's a complete assembly of devices that play together amongst themselves and with services in the cloud. Is that a large number of frames here that's why I said I usually ask, what do we mean by edge? But yeah, if you've got millions, thousands, whatever, devices out there, feeding sensors, whatever, feeding this data, collecting, processing, there's some level of edge computing, gateways, processes that are going to happen. Well, my question for you, I'd like to get your thoughts because we, again, we're having, we love the hype of IoT, we think it's completely legit and it's going to continue to be hyped because it's obvious what you see with IoT is anything on the edge. But a lot of customers that we talk to are like, look, I got a lot going on, I got application development, I got breakout my security, I got build that up, I got data governance issues, and now you're throwing IoT over the top, I'm choking in the projects. So they come down to a selection criteria. How do they define a working IoT project? And the trend that we're seeing is that it has to do with their industrial equipment or something related to their business. Call it industrial IoT because if they have something in their business, like say trains, it's a critical part of what they do. That's easy to say, let's justify this. Everything else then tends to go on the back burner if they don't have clear visibility of what they're instrumenting. That's kind of what we're hearing. Do you agree with that? Do you see a pattern as well as what customers are doing by saying, I'm going to bring this project in and we're going to connect our IoT, whatever it is. Yeah, no, that's exactly what I see. Industrial Internet of Things is where I see the biggest value today. When you have trains or mining equipment or whatever you know, manufacturing line, right? And being able to fine tune those lines to either predict failures or maybe improve quality. Those are impactful and they can be done right now today. And that's what we're seeing is kind of the big emerging thing IoT is interesting to talk about. The reality is it's really digital transformation that we're seeing. Companies transforming into new business models, doing things significantly different to grow into the future. And IoT is an enabler of that. So you're not going to see IoT everywhere today, but I- The low hanging fruit is where it connects to the real business. But it's going to go across all verticals, right? No doubt. What solutions does Pentau have for digital twins or managing digital twins, the objects, the data itself, within an IIoT context? Is this something that you're engaged in already? So within the Hitachi, Ventara- Okay, the larger company. Bigger company. We have what we call our Lumata IoT platform. And in that, there is this asset avatar technology that does exactly what you're describing. Now, I'm going to throw a quick plug out if you don't mind, the Tahoe world in a couple of, about a month. The cube will be there. The cube will be there and we're excited to have the cube and we're going to give you complete information about that asset avatar with all the right people. Yes, because there's a movie in there somewhere I could feel it, Avatar 2. There's a lot of great representations of data. I want to give you thoughts on how the new firm is going to solve customer problems. Because now as the customers see this new entity from you guys, Pentau has been doing real well. We covered the acquisition and you were kind of left alone. Pentau was integrating in, but it wasn't like a radical shift. Now there's some movement. What does it mean to customers? What's the story to the customer? You know, I think it's great news for the customer because Pentau has always been very customer focused. But when you look at Hitachi Ventura, the wealth of technology and expertise, everything from all of the great IT oriented stuff that Hitachi Data Systems has done and been well known for in the past, still exists. But this broader focus of taking data and processing it in a variety of ways. To solve real business problems. All the way to orchestrating machine learning and applying algorithms and then with the Hitachi. So Hitachi. So what specifically in Hitachi is coming into this? Because again, this is again a focused solution company now with data, so Hitachi Data Center. Yeah, so Hitachi Data Systems is, think of it as the infrastructure company. Hitachi Insight was the really focused largely on the IOT platform development with some Pentaho assets and then the Pentaho business. But here's the thing about Hitachi. Very large, large company, fills everything, right? Mining equipment and all kinds of stuff. So nobody understands how all those things fit together better, I believe, than Hitachi. So, but some of the things that we have at that organization is this idea of the Hitachi Labs. And data scientists that are really doing interesting things, Jim, you'd love to get more embedded into what some of those things are. And making that available to customers is a huge opportunity for customers to now be able to embrace a lot of the technologies we've been talking about. I said last year that this year was gonna be the year of machine learning and if you look through the Expo Hall, that's what everybody's talking about, right? It's AI or machine learning. I'm wondering if you're commercializing R&D that's coming straight out of Hitachi Labs already or whether the Ventara combination will enable that. In other words, bringing more innovation straight out of the labs into commercial arenas. So that's something that we are absolutely trying to do, right? Because there's great things that these lab organizations and Hitachi, they're big labs. I mean, they're really legit. I kind of joke about that. And the kinds of stuff that they're able to bring about. Now Pentaho is part of the engine to help actually commercialize some of those things. So, it's exciting. Have you on. We're looking forward to Pentaho World. Give you the final word here in the segment. Obviously, the big data worlds evolve.  What's happening? I mean, it's AI washed and that's pretty obvious. Not a lot of blockchain discussion which is going to completely open up some things we've seen in the decentralized application market which is going to complement the distributed nature of how we see data analytics flow and certainly the immutability of it's interesting. But that's kind of down the road. But here, you're starting to see the swim lanes in the industry. You're seeing the people who've been successful and the ones who have fallen by the wayside. But now the customers, they want real solutions. They don't want more hype. They don't want another eighth year of hype. They want, okay, let's get into the real meat potatoes of data impact to my organization. Digital transformation. What's happening? What is going on in the landscape? So, I mentioned it before and to me it's digital transformation which is a big, huge thing. But that's what companies are interested in. That's what they're beginning to think. If they're not thinking about those things they're falling behind. Five or six, seven years ago we talked about the same exact thing with big data. It's like, hey, big data is really, it's a big opportunity and they're like, well, I don't know. Those that didn't adopt it aren't necessarily in a position now to transform digitally and to do some of the things that they're going to need to evolve into new business opportunities. And the big data examples of winners are the ones who actually made it valuable. Whether it's insight that converted to a new customer or changed an outcome in a positive way. They go, that wouldn't have been possible without data. The proof points kind of hit the table. That's right. The other thing is who's going to win, who's going to lose? I think people that are implementing technology for technology's sake are going to lose. People that are focused on the outcomes are going to win. That's what it is. Technology enables all that but you've really got to be focused on. I want to get one more quick thing before we go. I know we're tight on time but I want to get your thoughts. Obviously the open ecosystem of open source is going to a whole other level. The projections are code will be shipping at an exponential rate. It's been going to be a lot of onboarding of new stuff. So open obviously works. Community models work. Partnering is critical. So we're seeing that good partnerships, not fake deals or optical deals or Barney deals, what do you want to call it? Like real partnerships. You start to see technology partnerships. What's your view on that? How is the new event tower going to go forward? You're going to continue to do partnerships and what's the strategy? Yeah, I think the opportunity with Hitachi Vantara is we have a breadth that can touch many different aspects. So as Pentaho, we had great partnerships, very meaningful, but it always comes down to what are we doing for the customer? How are we changing things for customers? So I'm not a believer in those Barney kind of relationships. Those are nice, but let's talk about what we're doing for customers. Yeah, real proof points. Yep, and that's where it comes out to. You guys will continue to partner us. We will continue to do that. Okay, great. Chuck, thanks so much. Cube coverage live in New York City in Manhattan. This is the Cube with big data, NYC are fifth year doing our own event in conjunction with Strata Data. Now that's the new name of the show. I was straddle with Strata Hadoop. Hadoop were before that, but we're still the Cube covering eight years of the action here. We'll be back with more after this short break.


So this is a special lecture. I love introducing you to all the bifematic stuff, but we have to limit it at some point to avoid turning this into a bifematic class. What I want you to focus on for this lecture is the conceptual ideas. In particular, physics and evolution, they are two sides of the same coin, but it depends on what aspects you're stressing. But you need to be aware of both and in particular need to know what you should ask a colleague who might be better on the other aspect. The other part that you should think of is that many of the concepts we introduce are similar, but they might be interpreted in different ways. In physics, the domains we talked about, the units that fold it right, I introduced them primarily in the sense of arguing that those were the stable units, it would be difficult to be smaller and it would be difficult to be larger for them to fold efficiently. In terms of bifematics, we rather tend to interpret domain as the units of evolution. I might steal a domain from another protein, such as that vaulted sensor. It turns out that they are in most cases the same type of domains. And that makes sense, right? If the individual units that I steal from another fold folds independently, they're going to be stable in my protein too, while if they were somehow intermixed with everything, in general, those mutations wouldn't work. So either is true, but it depends on what aspect of it you emphasize. Hopefully you will be guided a little bit by these study questions, and if nothing else, I hope to have with your appetite a bit for bifematics.


This is a Witchspace newsflash special bulletin. I'm Commander Burr. The annual in-game Halloween event has officially started after a cryptic message from Sally appeared in the forums which you'll find linked in the description below. We're not going to give any spoilers all I will say is that you will have a message from the Pilots Federation waiting for you in your in-game inbox directing you to a specific system. Upon arrival in the system be sure to scan any beacons that you can uncover using the FSS scanner in Supercruise first before proceeding deeper into the system. The stuff to be found is absolutely spine chilling. Good luck Commanders and we'll see you on the other side.


Hi and welcome to the session. Let us discuss the following question. Question says, the order of the differential equation 2x square multiplied by d square y upon dx square minus 3 dy upon dx plus y is equal to 0 is a2 b1 c0. D not defined? We have to choose the correct answer from a, b, c and d. Let us now start with the solution. First of all, let us rewrite the given differential equation. Given differential equation is 2x square d square y upon dx square minus 3 dy upon dx plus y is equal to 0. Now we have to find order of this differential equation. Now we know order of a differential equation is the order of the highest order derivative occurring in the differential equation. Clearly we can see highest order derivative occurring in this given differential equation is d square y upon dx square. So order of this differential equation is equal to 2. So we can write the highest order derivative present in the differential equation is d square y upon dx square. So its order is, now our correct answer is a. Now this is our required answer. This completes the session. Hope you understood the solution. Take care and have a nice day.


All right. Hey, I'm Mark Gilarducci. I'm director of the governor's office of emergency services. And so we're here today in Grizzly Flats in the Caldor fire. The fire continues to burn very aggressively here in El Dorado County, and I'm in this community because you know, we're doing damage assessments. We're looking at the impact that the fires had and it's really an opportunity for us to point out what we talk about in being prepared for wildfire. Communities like this exist throughout the state of California, and unfortunately this fire came through this community very rapidly and and it really the community is a total loss. These happen very fast and and if you live in in fire prone areas in California, it's really incumbent upon all of us to be as prepared as possible. That is making sure you're you have defensible space, you have a home hardening effort going on, hardening your home as much as you can to make it as resilient as possible against wildfire. Having evacuation plans. Here we're in this this small community with a lot of roads that are moving in and out and at night or if it's smoky, getting out could be a challenge. So understanding what your evacuation routes look like both during the day and at night because things may look different at night is very important. Having that family plan, knowing your evacuation routes and then having communications capabilities, battery backup capabilities, whether through generator in case the power gets lost or through battery backup, to be able to get alerts and warnings should you be told to evacuate. In this community we evacuated them early and in fact we since the very tragic fire in Paradise in 2018 we've really been moving to evacuate communities sooner rather than later waiting for the fire to come into the community. Evacuating early gets people out of harm's way and then allows for the firefighters who are coming in to focus on firefighting and not on rescue. And I would just say that I think that a lot of the factor in this fire and in the fires that we are currently having in California, the lack of loss of life has been principally because of the work we've been doing on evacuating early.  So important. All of this is while tragic behind me, this community total loss. These are materialistic items, truly sentimental value, truly heart-wrenching, but no lives were lost here and that is the most important thing and working together then the community can rebuild. So that's the message today, very important. If you live in California being prepared for all hazards is important, particularly if you live in wildfire country knowing that these fires can start fast, can move fast. Today's climate-driven conditions make these fires extreme. In nature, they move in extreme rate of spread and they can be extremely unpredictable. And so we just want to make sure people understand that and are as prepared as possible. Thank you.


I'm Darren Marlar and this is your Daily Dose of Weird News. Trump loyalist Roger Stone, who's under investigation for possibly colluding with the Russians to defeat Hillary Clinton, has a Richard Nixon-backed tattoo. ***Question, how many tattoo parlors do you have to visit before you find one that has Richard Nixon in the catalog of choices? Bullfighter Antonio Romero was gored by a bull during a recent bout in Mexico City. The bull's horn ended up 11 inches up Romero's rectum causing severe injuries. ***Yeah, you think? On Tuesday President Trump signed a nearly $20 billion funding bill for NASA, setting the goal of putting humans on Mars. First to go, everyone working at CNN and MSNBC. San Diego's Stone Brewing has unveiled a beer made with treated sewage water. The recycled water-pale ale is called Full Circle. Stone's CEO, Pat Tierman, says, �This particular water will just help us not require so much natural water to come in and give us a more reliable source. So for us to be able to reuse, that's part of our mantra. That's part of what we do. It's like the old saying goes � You can't buy beer, you can only rent it. And apparently, you can rent it again and again. In Maplewood, New Jersey, a 5th grade teacher thought it would be a good idea to have a slave auction where white kids sold black kids. Oh, correction, make that former 5th grade teacher. There is no coming back from this one with your job intact. FBI Director James Comey confirmed Monday that his agency is investigating possible ties between Donald Trump's campaign and the Russian government. A Trump spokesperson said that the two sides never shared ties or any other clothing. Those packets of wildflower seeds Cheerios is giving away to help grow flowers for honeybees. Turns out, in some states, those seeds are considered noxious, invasive and illegal. So be glad you don't work in the General Mills Promotions Department, and our In Tomorrow Morning's Emergency Meeting. Police in Delaware picked up a 21-year-old Octavio Lopez Hernandez on his third driving under the influence arrest after he was found passed out in the drive-through lane of a McDonald's. Master Corporal Mark Hoffman, a Dover police spokesperson, said in a news release that Lopez Hernandez was arrested around 1 a.m. Saturday after police were called to the restaurant. He was taken into custody without incident and charged with third offense DUI, a felony. Sounds like somebody's got a serious drinking problem. A Philadelphia man is suing a New York City bar, saying he was refused service because he was wearing a Make America Great Again hat. Actually, I have to side with the bar owners on this one. Not because you're a Trump supporter, but because it's impolite to wear a hat at the table. Next week Wells Fargo will become the first major bank to upgrade all of its ATMs so that you can use your cell phone to withdraw money. Just think, you might dial a wrong number and still hit the jackpot. The new Samsung Galaxy S8 will have Samsung's version of Siri called Bixby. Okay now, if it's the voice of Bill Bixby, you'll want to pass.  You wouldn't like him when he's angry. They struck a deal with the cast of The Big Bang Theory and so it will stick around for two more seasons and then wrap things up. Good thing. Another couple of years and we'd have had stories about Sheldon living in a nursing home. North Korean tyrant Kim Jong-un has released a propaganda video showing a U.S. aircraft carrier being blown up and a bomber shot down in flames. Okay, now it's getting serious. North Koreans are learning Photoshop. Facebook has introduced a new feature called Town Hall to get users more involved in the political process. Finally, it's about time Facebook created a way for us to talk politics. Trouble in Pennsylvania where state troopers in Shile Keel County say a 23-year-old woman claimed she shoveled the snow out of a parking spot only to have her 43-year-old neighbor's husband park his pickup in it. Well, the two women started arguing over the issue. That led to pushing, punching, and ultimately, yes, pulling each other's hair. Both were arrested and charged with disorderly conduct. Sounds to me like legislators need to get on the ball as soon as possible and pass a bill to outlaw snow. An Oregon woman is being accused of leaving seven children at her home daycare center while she went to a tanning booth. It's not as irresponsible as it sounds, though. She did strap them all down with duct tape before leaving so they wouldn't get into trouble. Parents at a Florida school called Innovative Park Middle School want the name changed because of the acronym IPMS. The school hasn't even opened yet, and already they're creating trigger warnings. I would comment about how stupid it is to make this an issue, but you gotta admit that as long as it probably took them to come up with the name and all the iterations they probably tried out before settling on this one, you'd think somebody might have written down the acronym at some time just to see what it looked like. Because of the current bread shortage in Venezuela, the government has taken over all of the country's bakeries. Government bread? Gee, that sounds like it's gonna be some tasty stuff. French authorities called to a robbery location found the suspected thief stuck in a hole he'd made with a hammer in a store window. When police arrived, they found the man half in and half out of the hole. Why can't all criminals be as courteous to law enforcement as this guy? To capture themselves. If you liked the video, please give it a thumbs up and be sure to subscribe if you want to see more. And click that little bell next to the subscribe button to be notified when I post new videos. This episode is made possible in part by my Patreon supporters. For Daily Dose of WeirdNews.com, I'm Darren Marlar.


What's up trade hackers welcome to today's update today's Thursday April 2nd Starting with the trade hacker question of the day Can you explain the mid and natural price when placing a trade? Let's jump on the platform and take a look at an example. Let's just go to SPY SPY being the most liquid ETF there is so if we go to the trade tab and what I did here is I just set up a Typical short strangle, so I just want to have at least two legs on there and what you'll see here is that The price at this time there and we've got about 40 minutes until the market closes So the market's still open, but you see here There's about a five cent difference between the natural and the mid so what the platforms do is is keep in mind This is a theoretical price for this spread. Okay, and that's so that's what they're giving you It's a theoretical price The price that the spread is actually trading at is What you get filled at okay, so the markets obviously are always moving and And so they give you kind of a theoretical now in something like SPY I will start right on the mid price and hit confirm and send to try to get an order filled if I was gonna place this trade There are other You know other option chains if you get in the weeklies that spread between the mid and the natural widens out a little bit If you get into some less lick liquid symbols Like let's just go to best buy as an example and I'll set one up real quick So if we're gonna do a short strangle, let's Sell the 22 Delta and sell the 20 Delta here So you can see see how much wider the the spread is between the natural and the mid We're talking about what is that? 17 plus 67 cents Between the mid and the natural so if I was gonna place this trade I wouldn't I wouldn't start on the mid I would move just off of it a few pennies and so in this case I would do something like this a lot of times you'll get you get Filled closer to right in the middle between them in in in much less liquid stocks or ETFs You have to go closer to the natural so it's just a theoretical price And so you can kind of just play around in that to see where you get filled sometimes all I'll move it down to somewhere like that and then confirm a cent and I'll actually get price improved So instead of getting filled at 336 which is where I set this at I'll actually get filled at a better credit of let's say 340 or 345 Sometimes you'll place it on the mid and you'll get filled at even better prices So it just depends on where the spread is actually trading where the computers and the market makers are actually filling those prices at But this just gives you a good idea The other thing to keep in mind at the time right now at this in this period We're in such a period of high implied volatility that what happens is the bid ask Spreads widen out. So you see here, you know on this 20 delta put you're seeing a bid of 170 and an ask of 257 that's a very widespread When we're looking at selling options or trading options in general we really want to Stick with the most liquid stocks the most liquid ETFs and have the smallest distance between that bid and the ask spread because that the difference between the bid and the ask You can almost think of that as commission. I mean, that's what the market makers are keeping And that's and that's what's paying the exchanges and the facilitators of your orders. So Tighter bid ask spreads more liquidity. That's where you want to be But that that's kind of the the distance between that's the difference between the natural and the and the and the mid going back to SPY just to Just to reiterate that and I gotta let me let me set that up again because there's one other point I wanted to make and that is, you know, typically well, I'll just do it right here So typically if you're if you're looking at the at-the-money options in a monthly cycle So this is May 43 days out. So this is a typical cycle that we would enter a trade-in If you look at the at-the-money options, so at the 250 strike in this case the difference between the bid and the ask right now is about seven or eight cents, right and so That the reason I point that out is again because of the volatility that we're seeing right now That is a much wider spread Then you would typically see in a Should I I don't want to say normal, but should I call market in SPY sometimes you're gonna see those spreads penny-wide one or two or three cents wide Whereas right now you're seeing seven eight nine cents wide at the money even though there's all that open interest So the more more open interest the more volume the more spreads or the more options trading at that particular strike is Typically gonna represent a smaller bid ask spread But if implied volatility heightens, you'll see those bid ask spreads widen out as well So it's okay to also just put your order in and let it set to see if it gets hit The downside is that the price could just run away from you and you never get in the trade Of course it if it happens to be a winner then you're you're mad you didn't get in Of course, if you if you do get filled and it goes against you you wish you would have waited So it's just a game that that's trading and so it's just a game of doing a little bit of price discovery Kind of moving that price around on your trade tab Around that mid and natural price to see where you get filled because the fill price is The actual price that that spread is trading at so hopefully that helps. Let's let's jump into what's going on in the market today Again, we've got about 30 minutes till the market closes s&p's up over 40 Got the Dow up over 300 Just about an hour or two ago. They were they were falling down and and the Russell was negative It's just barely positive now, but they have bounced back up Pretty small range again. We've talked about two days ago the range being the smallest that we've seen in weeks having another fairly narrow range and If you look at this range over the last week and a half or so, you know from 2600 down to about 24 25 ish, I mean Based on what we've been seeing where we're have, you know, we've seen 200 point ranges in a day We've got about a 200 point range over the last week and a half or so. So a little bit of consolidation going on and I see that as the markets just kind of winding up for the for the next move So again, don't don't fall asleep at the wheel based on the somewhat lower Volatility moves that we're seeing intraday compared to what we've seen over the last few weeks Because keep in mind. I mean if we look at a one-year chart If you go back before all the craziness started happening, you know, I mean look at the look at the look how small those bars Look compared to what we're seeing now. And so You know, I think the volatility is going to continue and so when I say the markets consolidating It's kind of weird to say that the market is consolidating in a 200 point range in the S&P But that is in fact what it's doing So you've got to look at the current environment To kind of look at look at that consolidation Not just an average over the last year because then you'd be taking in the consideration these tiny moves that we were seeing Previously compared to what we're seeing over the last couple months The other thing oil big boy move in oil currently up 22% it was up Quite a bit more about 35% at one point. So much needed in our positions You know, we have been kind of managing and rolling these positions as it's been sliding and now we're getting a little bit of a bounce And so much welcome bounce in oil. Hopefully that continues a little bit big move in gold up over 3% Bonds pretty flat, you know, they're up about a quarter of a percent Natty gas down a couple percent. So still some decent moves going on around the around the marketplace Boeing looking at the stocks Boeing down six percent Some, you know, some big movers in in stocks as well CVS Delta Airlines, you know, the airlines still continue to get hammered banks pretty flat today Expedia down almost 10% if you look at some of the Casino stocks like wind down another 4% and then zoom, you know, we've talked about this a couple times if I go to a year to date You know, it's it's down over 11% today It's been down the last four days in a row pretty significantly at one point The stock was up 140 percent year to date now. It's up about 76 year to date And so what's going on in zoom? I mean people are still staying home. People are still using zoom conference calls Well, there has been some news I don't know if you heard the new terminology is Zoom bombing and and so what's happening is So there's a concern about the privacy and security of zoom But the main thing that's happening is, you know, if you've ever used zoom, you know, you can just send out a link There's no password protection or anything. You you can have a room password protected But you can also just send out a link that anybody can join so imagine a big company 3000 people on the call And then somebody randomly gets a hold of that link and jumps in your zoom call with you We've got all your employees in there and there's been there's been reports of big calls going on and random people to jump in there and start Uh posting pornography and racist videos and all kinds of stuff So people are you know, it's kind of like photo bombing when somebody jumps into your photo Well, this is zoom bombing somebody's jumping into your zoom call and uh and and playing videos and things that are Not really what you intended. So to me, it's kind of funny But so there's all this talk about the privacy and security, but in reality, it's it is The people who are creating these zoom calls creating and sending out these zoom links They're just they're not being careful with who who's getting them. So anyway, kind of funny stuff, but uh If you're using zoom be careful. Make sure you are securing your calls. So you don't get any crazy people jumping on Uh, so that's what's going on in the market. So what did we do today? Uh, we ended another entered another double calendar when implied volatility is contracting Those have been working really really well Uh, and then um, and then we rolled a couple positions. So That's all I got for you. Everybody have a great evening. I would be posting the uh Recap of all of our alerts to pro members tomorrow. So I'll see the rest of you next week. Take care


So I think I'll take this time to, you know, talk about what Niharika is going to talk about. Her topics title is predicting the traffic jam, condition of air routing. I'll let her tell you about whether it is about network traffic or actual traffic. But right now she is pursuing her BTEC and information technology at triple IT Alhabad. And she's also a software engineer at Gojik. She has presented talks in multiple conferences such as DevCon, Open Source Summit at Europe, Flock to Fedora at Budapest. And she is the chapter title, chapter lead of Pi Ladies Mumbai. So if there is anyone who wishes to join Pi Ladies Mumbai, you should definitely reach out to her after this. She's been a finalist for the Red Hat Women in Open Source Academy Award 2020. And she was also an outreach intern at Fedora Project. So if there are students here who want to know more about outreach, you should talk to her about that. And she's also mentored students in Google code in for the Fedora Project. That's a lot of, you know, awesome stuff to have done as a student. So I'm sure there'll just be more to come. Also, yeah, Niharika, you can share your screen and we'll have you on the stage. I don't think three-minute delay should be much because we are at 3.18 and we'll start at 3.20 exactly. By the way, Wesley has provided the link to his serverless stuff. We'll be making it available as well, but you're hearing it directly from him on the chat so you can bookmark that right away. It is 3.19, so Niharika, please take the stage. Am I live? Yes, you are. All right. Hello, everyone. I am Niharika Srivastav and currently I am a software engineer at Gojek. I was also a research assistant at SUTD Singapore where my mentor and I, you know, asked me to compose the framework that can potentially predict the traffic jam by providing intelligent congestion aware routes to every passenger in the network so that you can basically avoid situations like these. So let's go to the talk outline. I would first start by addressing the problem of congestion and why it should be addressed and solved. Then I will introduce a concept of multi-class fleets that is potentially going to help us to solve this problem of congestion. Then we would go over the architectural overview and what our final framework should look like. I would also go through Python GIS libraries that I have used extensively in this framework and how they are helpful in analyzing transportation networks. Then we would talk briefly about the implementation of this framework. And finally, we would be comparing this framework with real life use cases. Alright, so let's start with some background. Well, the problem of congestion arises because every passenger wants to take the shortest distance route from their source to destination, thereby causing a traffic jam like this on road intersections, which results in underutilization of certain other edges in the network. Also, on top of that, the fact that over the years the number of vehicles has risen exponentially and the capacity of roads has always been approximately constant. So let's introduce a concept of multi-class fleets and how we're going to use it. Well, multi-class fleets allow breaking an entire customer journey into three classes. We have the first mile, middle mile and the last mile. So the first and last mile use micromobility options such as walking, scooters or bicycle and the middle mile uses fast speed cars, taxis or public transports. And the middle mile is supposed to cover at least about 90% of the entire customer's journey. So for example, a person would start walking from their source for about 200 meters and then they would alight a bus and take it for 15 minutes after which they would further bike away to their destination. Alright, so what are we finally going to do? We essentially want to route all customers in a congestion aware manner using that previously explained multi-class fleet of vehicles. And we also want to provide optimal transit points for each trip. So what that means is that if you see in this video. Sorry, apparently the mouse pointer is a bit distracting. Can it be changed or removed? Is it fine? No, it's distracting apparently. I won't use it. Okay, thank you so much. So we have to find optimal transit points for each trip. So if you see in this figure what this means is that let's say a person wants to go from A to B and they have two route options ACB and ADB. So they could either walk to their nearest bus station at sea and then take a bus from C to B having their total trip time as 34 minutes. So usually people like to opt working less and go to the nearest bus station and then just start. Or they could have actually worked a little bit further to their next nearest bus station at D and then take in a bus from D to B having their total trip time as 30 minutes thereby saving four minutes of travel time. So in this case you see that actually the optimal route from A to B is ADB and the optimal transit point is D and not C. So transit point means a point in the network from where you stop taking one mode of transport and start taking another. You're transitioning between modes of transport. We also want to create a social model out of this. So what that means is that so usually UBC implementations of congestion on let's say Google Maps and what they do is they try to route a customer on the most on the least congested path in a very user centric selfish greedy manner. But we don't want to do that. We actually want to route every passenger in the network in a congestion aware manner. So we want to take into account all the requests in the entire system and then scale this framework to work otherwise. So the final architecture of this would be as follows that we would be processing customer requests in a batch of N. They would have origin destination requests. We would use these requests and analyze our network which has congestion information which is in the form of travel time duration. And then we would be finding optimal transit points for each customer. We would do this by by using a proposed hybrid search that we have that we will come to later and after finding these optimal transit points for the middle mile journey. This is where you start taking your fast speed cars. We will dedicate routes to every passenger in the network using that multi class fleet of setup. So let me introduce a concept called geographic information system or just what is just just is actually a software framework that helps us to extract and analyze geographical elements in for analysis for research purposes. So it helps us to layer spatial data of Earth and view it in a 3D way so that we can extract patterns and then do foreign analysis. So as you might have noticed the research work of transportation or path planning strategies come under GIS. So there are several Python GIS libraries that are provided. It's all open source. I use many of them. The first one I used was OSM NX. What it does is that it helps us to extract entire street networks from the open source street map database and then further analyze it. One thing to note is that this framework that we built was verified and validated on Singapore's street network. So we extracted it and on the left we see all the nodes that are present in Singapore's graph and on the right we see all the edges that are present. The different colors of edges represent the different kind of roads. So some are highways, some are residential roads, some are service roads, etc. So a graph has been broken into nodes and edges. Geopandas. So Geopandas is nothing but a slight extension of the Data Science Library Pandas. And what it does is that it helps you to convert the previously gotten nodes and edges into separate data frames. So node is one data frame and edge is another. In this figure we see Singapore's edge data frame. So for example the columns U and V refer to the starting and ending node of a certain edge. We have the name of the edge, what kind of a road it is if it's tertiary primary secondary service. The maximum speed allowed on that particular edge which is regulated by government rules. And then we have the length of the edge and location corresponds to the exact latitude and longitude points. So after we have gotten our nodes and edges in a data frame separately, we combine them to create a network X graph.  Such as Dijkstra or Bellman's Ford or A Star Search. So that you can go from one node to another and plan your route trip. Awesome. And lastly we have ArchDree. So ArchDree is actually very cool. What it does is it isolates every geographical element and it's like it creates a bound for over every geographical element. Every geographical element is one single element. And then you can perform set operations such as union or intersection and see what other elements fall under the same radius. So for example, if I were to search that what are the nearest neighbors for my search for a certain node in the graph, I would take a union of all the nodes for a certain radius and understand, okay, this is the nearest neighbor. These are the number of nearest neighbors. So that's what ArchDree does. And finally something I used which was for speed up is project RSMS contraction hierarchy. So one thing to note is that project RSMS is not a Python library. It's actually written in C++ but it enables us to integrate it with Python by using the rest API. It provides something called contraction hierarchies and it is a routing algorithm just like Dijkstra's algorithm. So what it does is that it helps us to route between two nodes in the graph using the shortest distance path or the shortest travel time path. Now the reason why we're using contraction hierarchy and not Dijkstra is because when we're dealing with large networks of cities and countries, in this case Singapore is an entire city country, so Dijkstra becomes really inefficient to compute. So for an example over here, we see that if we take an 80 mile Euclidean distance route from A to B, Dijkstra would have to compute 500,000 nodes, whereas contraction hierarchy would have to compute only 600 nodes to compute the shortest distance route from A to B. And the reason why this is happening is because many of these stages are pre-computed and that is a major boost up for our library. Alright, so let's finally start with implementing this framework. The first and foremost thing we need to do is compute congestion for the entire network. So after we have gathered our data from OpenStreetMap using all the Python just libraries, there is something called as traffic speedpan data set that is provided by Singapore's government in an open manner. It's open sourced. So what the speedpan data set provides is that it provides you the link ID, which is a primary key for what edge it represents in the entire Singapore's graph. We have the location which is latitude and longitude points. And something important is it gives you the maximum speed observed at that edge on the road at a particular time and the minimum speed observed at that edge at a particular time. So if you average it out, you can see that for a particular slice in time, you can understand the observed speeds at every edge in the network. So down below on I figure 8, we see a heuristic called Bureau of Public Roads Heuristics. So heuristics are basically estimates we use in order to make our computation easier. So the estimate over here is that congestion is being calculated in terms of travel time. So estimated travel time, hence the heuristic is used. And the main thing to note is that your estimated travel time for an edge should be proportional, directly proportional to the number of cars that are already present on the road. And it should be inversely proportional to the capacity of the road. So let's say that if you're, okay, so let's say there is an edge on the road and it has a maximum speed limit of 60 km per hour. But the observed speed is just 30 km per hour on an average. So your intuitive thinking is that the cars are not able to reach the maximum speed limit. Hence they are facing some kind of congestion. Hence their estimated travel time is going to increase. So this is how we calculate congestion for the entire network. The next step is to divide one entire customer journey from source to destination into three miles because we want to eventually give them a multi-class setup. So let's say a person wants to go from source to destination. The first thing we do is that we take a radius of 500 meters from both source and destination and then we check how many potential transit points fall under that radius. So transit points could be bus stops or a pickup taxi, whatever is suitable. In this example, we have three bus stops within 500 meter radius for source and destination respectively. So the reason why the radius is 500 meters is thinking that a person wouldn't want to walk for more than one kilometer max in an entire trip journey because we also want to take into account the flexibility for a customer. So now, given this, the person has the options of going from all these three bus stops from source to any of the three bus stops at destination. So there are possibly nine routes the person can take. Now which route do we actually select? So we select that route which will give us the overall travel time from source to destination. So essentially, if you see, we only need to calculate which bus stop to take from source and destination. So these are transit points for the middle mile and this is what we need to calculate. How this is calculated is using a proposed algorithm called Modified Hybrid Search. Now our main objective is to find the minimum travel time given a set of travel times. So to understand this, our first thing to note is that imagine we have, in the previous example, we had nine possible routes. In this, let's say we have 10 possible routes. So we have 10 source destination pairs. So we have from A1, B1, so on to A10, B10. And what we do is we calculate the Euclidean distances between these load pairs. And why Euclidean distance? So this is another heuristic because we essentially want to estimate travel times. And our main objective is to compute our minimum travel time in a very cost effective way. So Euclidean distances serve as a lower bound for distance. Euclidean distance is the least distance you can possibly get from point A to point B. If I were to divide this distance by constant speed, I would get Euclidean time. And again Euclidean time would serve as a lower bound for travel time between point A and B. So what we do is that we have 10 possible node pairs and we calculate the Euclidean times for all of these 10 pairs. And we sort them in increasing order. Now if I were to give you an array of 10 elements and I would say that you need to find the minimum travel time pair. But you do not know what the actual values of those 10 elements are. You essentially need to go through every element in the array and then you would output a minimum pair. So that is like an exhaustive search that you are doing. And that is computationally inefficient because it is the order of O of N. And it wouldn't scale well when we are talking about huge networks of countries. So that is why we are trying to do modified hybrid search. So we are going to start doing that. So let's say we have 10 possible roots over here. Whose Euclidean travel times have been sorted in increasing order. We introduce a variable called cutoff which is equal to C of N by E. And so N being 10, C of N by E is approximately 4. And 4 over here represents the fourth element or index 4. We have a decider which is equal to cutoff plus 1 which is equal to 5 in this case. So fifth index or the fifth element in our array. And index is starting from 1 for this case. Sorry about that. So what our main aim is that our idea is to stop our search the moment we reach a cutoff and then we do a base comparison. We do not want to go through all elements to return our least travel time because that is inefficient for us. So for case 1 we go to the first element. We see that the Euclidean travel time for pair A1, B1 is 10 minutes let's say. We see that the actual travel time at A1, B1 taking into account the congestion information and how many vehicles there are and so on and so forth. The actual travel time is 29 minutes. We then compare this 29 with the second Euclidean travel time which is 20 minutes. And we see that 29 minutes is greater than 20 minutes. So we continue. We go on to open up our second element which is 25 minutes. This is the actual travel time for pair A2, B2. And then again we reiterate the same process. We see that 25 minutes this time is lesser than its next Euclidean travel time which is 30 minutes. So we stop and we return 25 as our minimum travel time. And why we're doing that is because we see that we first established that Euclidean travel times are serving as a lower bound. So it is obvious that 25 being lesser than a Euclidean travel time is going to be lesser than every other element that is going to come after that. Hence we stop and we return with 25 saying that this was our minimum travel time for the entire array we are done. In an alternate case what can happen is we again start with opening up our first element. This time it is 35 minutes and it is lesser, it is greater than the next Euclidean time which is 20. So we open up our next element. This is 32 minutes. It is again greater than 30 minutes. So we open the third element. But at the same time 32 has been minimum up till now. So between 35 and 32, 32 has been our minimum travel time up till now. So we just want to make sure that we remember that in a variable somewhere. We've opened the third element which is 45 minutes and it is still greater than the fourth element which is 40 minutes. So we open that up and it turns out to be 55 minutes. Still greater than the fifth which is 50 minutes. And finally we open the fifth element which comes out to be 65 minutes. Now you see that the red line is showing that we have reached our cut off. We need to stop our search. This is what, this is kind of a constraint we have set. We need to stop our search. Till now we have all these five elements and we need to see which one is the best answer till now, the minimum answer till now. And we see that 32 happens to be the least travel time that we have seen till now. Hence we stop our search. We say okay we're going to go with 32 as our least travel time for all 10 possible routes and we return. There could be an alternate case where we keep opening up our list the same way as before. Till element 4 we see that 55 has been the minimum element up till now. And then on the fifth we see that we get 52 minutes which is actually a decider element. We compare our minimum up till now to the decider and we see that 52, our decider element is lesser than minimum up till now. Hence 52 is going to be returned as a minimum for the entire array. And this is how modified hybrid search works. So one thing to note is that in all three cases we did not go through every all the 10 elements. There was a reduction in the number of queries. In the first case we had about 80% reduction in the queries. We just queried the database for two elements and in case two and the alternate one we had a reduction of 50%. So we are trying to make our search algorithm more computationally efficient. And we will see what the tradeoff is but this is how we're doing it right now. So this is where we started to validate our network framework with other state of the art current frameworks. The first thing that we did was that we see that our network is using a multi-class setup wherein a person is breaking their entire trip journey into three parts. And every journey, every customer trip is being system optimally getting every customer trip is being allocated a system optimal route. We are going to compare it with the current framework that we use intuitively which is you start with just a single vehicle from your start to destination. And you want to choose a path which is either the shortest distance for you or if you check on Google Maps and you see okay this is the least congestion travel time for me so I will go on this. So the blue one corresponds to our framework and the orange is the one the single class user framework. And in all three cases over here we have three peak times called off peak moderate peak and high peak so that we can properly understand every pattern of congestion throughout the day. And in all three cases we see that our framework is drastically less than the orange bar. What that actually translates to is that we are utilizing our entire network there is by 74.26% more. So there is greater network utilization using our framework it means that more people are being allotted a congestion free route from their source to destination at the same time. If we opt for a multi-class system equilibrium framework and something to note is that since there is a system equilibrium and every trip request has been taken into consideration. The congestion faced by every customer now who is following this dedicated multi-class setup route is facing zero congestion at all times. So I also wanted to see how this is working and scaling for a real case scenario. So we compared it with Google Maps. So over here we see that let's say a person wants to go from the upper left red mark to the down red mark on the right. So that's your source and destination. Google Maps also provides multi-class recommendations. But again they are user centric in greedy and we're trying to do a system centric one. So let's say how that compares the brown line that you see on the right corresponds to Google Maps framework. What they suggest is that you should walk for a total of 21 minutes. So it's like 10 minutes you start from your source and then you walk for 11 minutes from your transit point to your destination. And your travel time using a fast-speed car or vehicle is going to be 53 minus 21. So your total trip time from start to end is going to be 53 minutes which corresponds to walking plus your middle mile which is cars or bus. But our framework is recommending the red path on the left which says that you should walk for a total of 33 minutes. So it's like 15 minutes starting from your source till a certain point from where then you start taking a bus. And then you again walk for about 15 minutes more to your destination having a total trip time of 45 minutes. So we see that we have saved time over here by just increasing our pedestrian networks slightly more by just increasing our walking time slightly more. We have increased we have decreased our overall journey time. So this is our framework being used in a real life case and we've reached the end of our talk. So I'm just summarizing in case some of you lost track. We first started with collecting our data using open source Python just libraries and this helps us to analyze our spatial data. Then we implemented our algorithm wherein we are aim is to divide a customer's journey into three miles in a certain way. Then we tried to find the optimal transit point for a certain person in the trip. At what point should they be stop walking or should they stop walking and start taking a bus and then stop start walking again to finish their entire journey. So in order to save that overall travel time also while doing this in a computationally efficient way. And finally this is our framework that we get where essentially batch of end customers are allotted dedicated routes using this multi-class frame multi-class setup of fleets. So thank you everyone. Thank you Niharika. And again the chat section has been a buzz so you have a lot of question answers questions for which we hope you will be able to provide answers. If not, if you can also bring up the slide where people can contact you later on that can be there for reference but now I'll read out some of the questions that people can answer. So one of the questions that struck me as generally useful is looks like everything depends on whether there's data provided by government. Any alternative to implement in case such data isn't provided is what, let me see who is asking this, it is Akshay. Yeah awesome question something which I actually faced problem in myself. So in Singapore's case Singapore's government is very generous and they provide traffic data openly but I was also validating this framework on New York and Paris just to see how it scales. And New York and Paris governments don't provide this data freely at all. So what I had to do was that we had to model our estimated travel time and there are various research papers and citations would say that congestion travel times and traffic patterns are represented very accurately using Gaussian distributions.  So that works too. It is not ideal. It is not real life. But this is what you do when there is parts of data that is where data collection is a huge task in data science. I hope that answers the question. Akshay, you can ping if you have got your answer. Another question is, is the speed up of contraction hierarchy gained by sacrificing the optimal result? We know that Dijkstra produces optimal result. Can we say the same about contraction hierarchy? Yes, the question is really on. So contraction hierarchies also provides the optimal shortest path which is provided the same as Dijkstra. The speed up is because if you must have attended the keynote by Anand, he said that pre-computation is a very important thing in data science wherein you pre-compute common data points and then you store them. So contraction hierarchy has two processes. One is pre-computing data and the second is what we use, which is after the pre-computation is done, all we need to do is query what is the path from A to B. So in transportation or in graphs, nodes are constant. They will never change over time. Hence, we can pre-compute the shortest and even roads never change over years. So you can pre-compute the shortest distance from A to B by using the road length and then store it. And all you need to do is while you are querying your contraction hierarchy server, you need to divide every edge using the speed limit at that particular edge, which can obviously change. So the pre-computation efficiency is gained because of the pre-computation step and contraction hierarchy provides optimal results just like Dijkstra. I hope that answers. I can chat more later. I think we will take one more question. So Sravya asks, this was during the 70% mark, by the time offsets between A1 and B1 are estimated, what if A1, B2 has become the optimal counting all parameters, optimal counting all parameters? Yeah, okay. So I want to point this out, which I kind of didn't add in my presentation, was that modified hybrid search that was proposed is not providing an optimal minimum travel point obviously because we are not going through all the 10 possible routes. But it is providing us something very close to optimal. And what happens is that in a use case like traffic network congestion, let's say if the minimum travel point due to changing times and changing congestion patterns happens to be for example 20 minutes. So from A to B, let's say your minimum travel time taking all parameters into consideration is 20 minutes and modified hybrid search, what it tends to do is it gains, it gives you accuracy up to 70% with the deviation of on an average less than five seconds. So you would get your estimated travel time as 20 minutes five seconds. So that is that trade off is very negligent when you're talking about a use case of transportation, right? Five seconds make no sense. Even a threshold of two minutes or a delay of two minutes doesn't matter too much to an individual customer. So that was the kind of speed off slash trade off we gained by using modified hybrid search. Shavya, does that answer your question? I'm not sure if she's gotten to the chat section, but I'm sure she will see it. Yeah, you can always contact me and then we can discuss this offline. Yep, just I think we'll take one last question. Why is the cut off chosen as N by E? Perfect question. Yeah, so N by E is approximately 37% of E. This concept is called stopping criteria of hiring secretary problem, hiring secretary problem stopping criteria. This concept says that there is a 37% win probability of finding the minimum or the most optimal value in given N number of data points where you don't know what the actual values of the end data points are. You just know that the size of data points is N and you want to find the minimum without having to look at all. So I'll again repeat it's called hiring secretary stopping criteria. You can check that on Wiki. What we used is a modified version of that where we didn't go through all N again. We only went through just there was a decider if you saw so the decider is just cut off plus one. So again, we didn't go through we wanted to basically stop our search as soon as possible and see what the tradeoff was and how computationally efficient this was. So we try to modify that further by saying there is N by E cut off and then just one more comparison there and that's it. So yeah. Awesome. So on a related point, there's a book called algorithms to live by where it also talks about when to stop in an algorithm. So I think it will be worthy to check out if people haven't. Anyway, Niharika, thank you so much. You've been working on time. So thank you. And thanks to the audience for, you know, like asking valuable questions and participating in the talk because as difficult interactivity is to maintain in a session, I think we're pulling it up really nicely. So thank you again. Yeah, I think I'm going to bring you down from the stage, but thanks again. And you haven't put up your contact information. Or maybe I missed that part. All right, I'll put it up on Hopin. No, you can put it up right here or you can put up on Hopin as well. Yeah, people are all there. I will copy it and put it up on Zulip chat and it will be available there for taking questions later or you have a Twitter handle or email ID. That will be great. Sure, I'll definitely do that. Thank you. Your slides will be made available, I suppose. All right, yeah. Thanks for having me. This is my first Picon, awesome experience till now. I'm not sure at all. It was brilliant. Thank you so much.


One of the greatest threats to patient safety during an operation is surgical site infection, an infection in the part of the body where a surgery takes place. Healthcare professionals try their best to prevent this type of infection by scrubbing their hands and arms up to their elbows before surgery, maintaining a normal and constant temperature during surgery, and thoroughly cleaning the skin at the site of surgery. But the increasing mortality caused by surgical site infections has forced healthcare professionals to consider every possible way in which the surgical environment can be disturbed. That includes minimizing traffic into and out of the operating room. Studies show that the opening and closing of doors during surgery allow for the entry of bacteria-carrying particles, which pose the risk of infection. While a closed-door policy would be unfeasible for the dynamic environment of today's operating room, it is undoubtable that decreasing traffic into and out of the operating room during surgery would go a long way toward reducing the risk of infection. A recent study suggests that one effective way to reduce traffic during surgery is through a collaborative effort among healthcare professionals to identify the root causes of that traffic. For an orthopedic operating room in a university medical center, researchers showed that a five-step problem-solving exercise could reduce door movements by up to 78%. In this study, researchers implemented an intervention called the A3 report among a team of four healthcare professionals working in an orthopedic operating room. An orthopedic surgeon, an anesthesiologist, a surgical coordinator, and a quality coordinator. The A3 report is a problem-solving tool that originated in the field of lean manufacturing, a field concerned with maintaining customer value while minimizing work and costs. The report involves five main steps. Define the problem, specify the current situation, determine the ideal situation, analyze the gap between the two situations, and take action for improvement. By adapting this process to their operating room environment, the surgical team identified three main causes of traffic during surgery, an unclear policy among team members about entering and leaving during surgery, the lack of visibility of the operating room's telephone number, and an ambiguous sign warning against entry during surgery. After proposing and implementing improvements to eliminate these root causes, the number of door movements decreased dramatically from approximately 24 per hour to four per hour over a six-month follow-up period, a decrease of 78%. While the researchers admit that their results are specific to one operating room at one institution and make no direct link between door movements and surgical site infections, they highlight the potential of adapting lean practices such as the A3 report to improve quality of care and patient safety in a healthcare setting.


King Henry VIII lived in the Grade 1 listed palace known as Hampton Court, located in the heart of Richmond, upon Tames, a truly majestic site to behold for anyone lucky enough to ever get the chance to visit it. This impressive, towering structure boasts an incredible number of amenities that rival the greatest palaces in the world. I'm talking 241 chimneys, nearly 1,400 rooms, 5 internal courtyards, and a mind-boggling floor area of more than 5 million square feet. Top that off with over 60 acres of formal gardens, as well as 750 acres of surrounding parkland. You've got yourself the very definition of extravagance. Also Michael and I have dropped our own house tour of our new home that we moved into this year, so go ahead and subscribe to our personal channel if you want to see where we're living and more of what we're up to. In these videos we don't reveal any addresses and even though I've done a house tour of my own place, please do not show up at any private residences because it's not safe for anyone. Its origins begin with the Cardinal Thomas Wolsey, King Henry VIII's Lord Chancellor, who undertook the transformation of this property from a relatively humble and ordinary country house into the magnificent specimen it is today. It was Wolsey's intention to create a grand building for himself in which he could host not only the king and the royal court, but also monarchs from all across Europe. And so after investing huge sums of money, he constructed a palace fit for a king. In fact, Wolsey was so successful at accomplishing his goals that the whole thing kind of backfired when King Henry fell so in love with the place that he decided to take Hampton Court for himself. By the 1530s, Henry's home had become many different things, a palace, a hotel, a theater, but largely it served as a huge leisure complex for the country's elite. In fact, the king used to demonstrate the palace's splendor by throwing lavish banquets with the country's most extravagant personalities and attendants, spawning over some fabulously expensive art. While living here, King Henry stayed in the private apartment quarters that were personally styled after his own taste. But even with that area taking up a sizable portion of the property, there was still enough room for 30 extra suites of lodging sprinkled throughout the Tudor Palace, in particular located inside the west front known as base court. For the most part, King Henry VIII adored the time he spent living here, but that's not to say that everything was perfect. After all, his third queen, Jane Seymour, died here while in the process of giving the king his much-longed-for-son Edward. In fact, it's been suggested that Jane's ghost appears each and every year walking the halls of this palace on the anniversary of her death, October 24th. And that's not the only ghost that haunts these halls. King Henry VIII's later wife Catherine Howard was also arrested while living here and later executed at the Tower of London for the crimes of adultery and treason. It's said that prior to her execution, she caught wind of what was about to happen and went running down the halls of what is now called the Haunted Gallery, where her ghosts can still occasionally be seen and heard begging for mercy. It's difficult to explain just how important this palace was during these early years of its existence and the role it played in running the country. To give you a bit of context, the kitchens would be a great indication. On different occasions during Henry's reign, he would sometimes have as many as 1,200 courtyards visiting him at any one time, all of whom needed meals prepared for them. To accomplish this, King Henry had the existing Great Kitchen enlarged and he created a space big enough to pump out as much as 1,600 meals a day, and that's without modern technology. Following the reign of King Henry VIII, Hampton Court would maintain its position as one of the kingdom's most prized and popular palaces. In fact, many of the 17th century's most dramatic events took place in its hallowed Great Hall, including William Shakespeare's original productions of Hamlet and Macbeth in 1603, which were put on for King James I. James was also responsible for organizing the 1604 Hampton Court Conference, which might sound like something of a bore, but resulted in the publication of the King James Bible in 1611, the first authorized version of the Holy Scripture to be printed in English. Years later, James' son King Charles I would utilize the palace to house much of his art collection, including paintings of Matanga's Triumph of Caesar. Unfortunately for Charles, soon enough, this palace would turn into his prison. In 1647, he was placed under house arrest after his defeat in the Civil War. The king would later escape through the privy garden, but was later recaptured and executed in 1649. I'm just thinking of all the ghosts that must be in this place. During the Commonwealth period of 1649-1660, Oliver Cromwell would save the palace from imminent destruction by making it his home. Despite his Holy Puritan ideals, Cromwell appreciated fine arts, mainly the tapestries that hung on the palace's walls, and he enjoyed his time living here like a king. After the restoration, King Charles II marked his return to Hampton Courts with the commission of the beautiful canal that borders the property in preparation for the arrival of his soon-to-be bride, Catherine of Braganza. The couple then honeymooned at the palace and spent their time lazing around in boats shaped like swans up and down the canal. Of course, just because he was married doesn't mean that the king didn't have something of a wandering eye. In fact, his main mistress, the Countess of Castle Main, along with all of their legitimate children, were allowed to live on the grounds of Hampton Courts. When William III and Mary II took to the throne in 1689, they asked Sir Christopher Wren to build them an elegant new baroque palace. And while Wren originally planned on destroying the entirety of Hampton Courts to make way for this new creation, he ultimately decided to leave much of this Tudor sensation as it stood, and added onto it instead, with a spectacular fountain court. The new wings around the fountain court contained fresh state apartments in private rooms, one set each for both the king and queen. These royal suites were of completely equal value in order to reflect William and Mary's unique status as joint sovereigns. William and Mary were also responsible for commissioning many of the most amazing areas of the Hampton court gardens to complement their new wing. This includes the great fountain garden and a brand new privy garden. It was also during this time that the palace's infamous maze was added to the grounds too, one that reportedly takes at least 20 minutes on average to make your way to the center of, and was originally constructed out of hornbeam. Another landmark here is the Royal Chapel, which once contained a great double window filled with stained glass depicting Henry VIII, Catherine of Aragon and Cardinal Wolsey. Unfortunately, when the Commonwealth took over, much of this space was destroyed. Then in 1710, Queen Anne commissioned Christopher Wren to remodel it, and he placed Grand Timber over the original brickwork. He also added boxed in pews, an organ and a staircase leading down from the royal pew. During the reign of King George I, his primary contribution to Hampton court was to build an impressive suite for his son George, the Prince of Wales, and his wife, Princess Caroline. The King also commissioned a new kitchen at the time, which today is known as Georgian House, and can now be rented out if you're ever looking for a place to stay while visiting England. When King George II succeeded his father in 1727, the palace entered its final phase as a royal residence. He and his wife completed work on their own suite of royal apartments, but by 1737, he no longer felt inspired living here, and the royal family vacated the premises, allowing others with grace and favor to take over. Many of the individuals who would call Hampton court home during the subsequent years were aristocratic widows in tough circumstances, who were offered free accommodation in return for their husband's services to the monarch. And even though the apartments built over the centuries were very grand, they weren't necessarily all that comfortable to live in. Residents tended to complain about the palace being unbearably cold, damp, and access to hot water being difficult to come by. With so few people wanting to stay there, Queen Victoria opened the palace to the public in 1838. It has remained a magnet for visitors ever since, drawing millions from across the world to experience the grandeur, ghosts, and fabulous art hanging on the walls. By the 1920s, further leisure activities had been provided, including taking tea in the Tilt Yard Cafe, enjoying a game of mini-putt on the palace screens, and even a game of tennis on the courts. Today, the palace is also home to two famous annual festivals, the Hampton Court Music Festival and the RHS Flower Show. Apartments stopped being handed out to aristocrats on the premises sometime around 1960, but the rumor is that a few elderly residents are still living at Hampton Court today. Nowadays, the palace is still owned by the royal family and right of the crown, which means that it's held in trust for each new monarch and can't be sold. But for the most part, the palace and its grounds are cared for by an independent charity known as Historic Royal Palaces, which receives no funding from the government or the crown. If you can't quite make it to England, you can always catch some of the Hampton Court interiors when they pop up on TV series, such as Netflix's Bridgerton or even movies like The New World and Sherlock Holmes, A Game of Shadows. But either way, that's gonna bring this latest edition of House Tour to a close. Be sure to let me know what you thought by leaving some comments down below. I'm Kerri the Vampire Slayer and please take a second to like, subscribe and turn on your notifications to make sure that you never miss a video. If you want to learn more about England's many palaces, then check out our recent looks into the homes of Queen Elizabeth II, King Charles III and Prince Andrew. I'll see you all in another one. Bye!


Hey everyone welcome back in this video I want to show you how to trade a short call vertical in tasty works so we're looking at a chart of SPY the S&P 500 ETF and Looking at the chart You know not necessarily saying that this is a trade that I would make but just as an example Let's say you were looking at this chart and you decided that you were you had a short directional bias in SPY meaning you thought over the short term that you may see a little bit of a drawdown from where it is currently priced What you could do is you could sell a call vertical spread and to do that you simply go to the trade tab and Within the option chains you can see you'll have the different expiration cycles We like to stick with the monthlies. They're the most liquid and we like to stay between 30 and 60 days to expiration So we'd look at the one with 38 days in this case And you can click the little arrow to pop up the option chain Then what you're gonna do? There's a couple different ways to sell a call vertical or to implement a short call vertical One is to simply click on the bid and the ask of the desired strikes that you want to choose So let's say we wanted to you know, this is the call side. So say we wanted to Sell a slightly out of the money call vertical spread So here's the in the money in the money. So this would be out of the money on the call side So let's say we choose this one and you could simply click on the bid to sell that strike And then let's say we wanted to go five points wide So we go from 278 count five away, which is the 283 simply click on the ask to buy that one So we're selling the 278 and then we're buying the 283 to define our risk at that point Okay, so that's it's all set up. And so now you can check your stats. You see a probability of profit of 58% You see the probability of making 50% is this is you have a 76% chance You can see the Delta. Obviously, it's negative We are anticipating the stock to go down if we're right on the trade you can see your theta Max profit and max loss So so that's one way to set it up. The other way would be to let's clear this one off Is to go up to our strategy drop drop down Now one thing to notice is the first thing is short, you know You can click on that to toggle between short and long We obviously are wanting to do a short call vertical and the same thing here This is set at put so we click that to change it to call and then this is at vertical You've got the strategy drop down so you can do vertical and hit go and that automatically puts it on your chart Now you can see it all it all it automatically goes, you know very close to the money And so that's actually the same one that we chose for the short strike But our long call defaults to the very next strike So we want to click and drag that if we want to do the same five points away and just drag it and boom There you go. So you got the same stats 58% probability of profit You've got the 73% probability of making 50% the delta theta Max profit and max loss. Okay, so those are the two ways to set it up on the trade platform I'm a visual trader. So I like to take it over to the analysis tab just click on curve and And then make sure the analysis piece is highlighted as well and that brings up your visual graph So you can see on the green side where you're making profit That's a max profit of two hundred and six dollars and you can see the same thing in the flag up here PNL at expiration 206 and if you slide over here, you can see the pill PNL at expiration on The red piece to be 295, which is also your max loss and your buying power requirement So now that you see that now you could say, okay, maybe I don't like it Just five points wide. Maybe I want to widen it out and you can click and drag these and Adjust as needed. Now you can see the max PNL has changed to around 800 max loss to about 1300 and you can slide these back and forth To get an idea of exactly how you want the trade to look based on how it fits into your portfolio and based on your overall assumption of the direction of the underlying symbol that you're trading When you're ready to go just simply hit review and send Send it into the broker and wait to get filled Hope that was helpful. We will see you in the next lesson