Hello everyone and welcome to another episode of Code Emporium and today we're going to see if we can predict stock market data. Spoiler alert, we cannot very well, but I still wanted to make this video because there are certain outcomes that I wanted to kind of come across. One of them is this is a good example of another use case of time series analysis. The second is that we're going to be using a novel method for evaluation of a time series. This is just better than like some other typical like mean absolute error that you would typically use, especially in cases that we're going to see now and why we would need to use this kind of evaluation metric. And the third is mostly that machine learning isn't magic. Your model is only as good as your data is and I kind of wanted to hammer home that point through an example like the stock market where data can be really hard to come across and you don't have all the data that's enough to actually predict the next day's closing price, for example. So with that all in mind, let's just get started. So right here I'm going to be using data from Yahoo Finance, typically, well more specifically I should say that we're going to be using Tesla's stock price data.  And when you apply it, when you hit apply, and then you can hit download to just download all the data from 2010 of June 28 till till current date, right? So if you hit download, you'll get a CSV and that's a CSV that we'll be using here. Let's just go back to the main project and doing all that you get this little data frame over here, right? Now all what we are interested in for stock market data is we want to predict the closing price for the next day, given all of our data up till the current date, right? And adjusted closing price is just a more accurate metric. So we'll be using, it's a more accurate field, sorry. And we'll be using that as our main label. So right, and now just getting a very brief look at like some top level stats. We have data from 2010. There's 4,000 days of data points that we have. Well, technically that's the difference between the dates, but we only have 2,800 of them because the stock market is only open for the weekdays and not for the weekend. So this is like five sevenths of the total 4,000. Cool. Very cool. All right. And if you plot out just some nice visuals on like how your data looks for the stock over Tesla, at least for the last 50, 50 working days, it kind of looks like this. Not really observable seasonality, or yeah, anything of such, but we'll roll with it. This is our data. The stock market is particularly volatile around this time of life, I guess. So this is going to be fun to predict. Let's see. So next is going to be doing some, well, let's say feature engineering, but essentially if you think about like what, what from this data that we have so far, what would be useful in actually predicting the closing price for the next day? Now, because of such noise that I'm seeing right here in the charts, I'm just going to be using the lagged variables by one day. So the previous day's closing price is the only variable I'm mainly using to predict the next day's closing price just to keep things simple for now. And I'm also going to be throwing in like a day of week predictor just to capture any weekly seasonality if it does exist. And, and also I think we have volume data here too. Yes, we do. So we have like the total number of, a total number of buys and sells that actually happen on that day. So for every single day, we have sale volume, which we will be using for typical economics, demand supply economics just to cater to that. All right. And now let's just say if we kind of just like format our data set, it will look kind of something like this, where this is a sample, where this adjusted close is our label. And the previous adjusted close price is the previous day's close price with the previous volume and the, and the current day of week, right? So essentially what we kind of see here, and I'm going to be throwing this all into like stats models, right? And what we see is that there is a really high correlation and as we kind of expect from the last day's adjusted close price to today's close price. What's really surprising is that we're actually able to see 99.5 explainability with just these variables alone. Which kind of just like tells you, okay, we might actually be doing pretty darn well in predicting like the next day stocks, right? Something else that's interesting that I've seen is this day of week predictor because it shows that there is, so the 95% confidence interval is like in the negative region right here. And because the coefficient is negative too, it kind of suggests that the stock prices will fall as the week goes on. I'm using ordinal encoding here. So ordinal encoding essentially says, well, instead of one hot encoding where we have a bunch of dummy variables that are binary encoded, we're just encoding every variable as 0, 1, 2, 3, 4, 5 from Monday to Saturday or whatever it is. And essentially what it is saying is that as the days go on, that means as we go to Friday, the stock prices actually fall, whereas in Monday, it's much higher. But this is kind of against the typical scenario of how stocks typically behave because there's something called the weekend effect, right? And if you go to the very venerable source of investopedia, it defines the weekend effect as a phenomenon where Mondays are often significantly lower than Fridays, which is the opposite of what we're seeing. So this could just be, I mean, this could be a number of reasons, honestly, it could be that we are using a linear model. It's not exactly capturing that kind of data. Or that Tesla specifically, that stock is so, it's very different from all other stocks. It's like a magical stock and somehow it behaves differently. I don't know, but this is the data that we're seeing, so we'll just go with it. Another thing that we're also seeing is, well, for previous volume, you see that 0 falls within this confidence interval, which makes me believe that, well, previous volume is just not that strong of a predictor, at least when used with a linear model, with the linear regression model here. And previous adjusted close price is just as, well, it's super predictive of what we are actually looking for. So that's probably why we have such a huge explainability over here. I'm just going to be keeping, though, previous volume in our model, since I noticed that if you kind of just remove all of these other variables, previous model does have a significance, a significant impact, and it does have some explainability associated with it. So if you do replace a linear regression model with a slightly more complicated model, it might actually, you might see those effects a lot better. So we'll keep it in there. All right, so next is the model training phase, cool. So right now I've defined a function for evaluating a model. I'm using the mean absolute error, which is essentially like the mean value of the actual price of the adjusted closing price of the stock that day minus the price that a model predicted for the adjusted closing price that day. And just taking the absolute value and then just taking the means across all of our predictions and that will give us our evaluation metric. I'm also, in addition to that, considering mean absolute percentage error because there are cases, I just want to make sure that we're able to see it from every single lens because sometimes the mape might be lower than the absolute error and vice versa. So we just want to capture all those cases and get the best view of our data. Now, when passing this through a linear regression model, passing it through a pipeline over here where we first create, we do some preprocessing steps, which is standard scaling and then ordinal encoding of our numerical and categorical variables respectively. And then we just fit our model right here. And what's interesting is that we see a mean absolute error of $16 and 72 cents or a mape of 3%, which is very, very, very good. Actually, if we were to see this, I mean, it's almost too good to be true, right? But is it really as good as we're seeing this basically saying we're, well, this is not the best. Okay, it's not. Okay, let me not exaggerate this. We're not. It's not absolutely incredible. Like $16 off is still pretty off, right? From the ticker price. But it's actually not too bad. It doesn't look too bad. We're only 3% off too. It looks okay. But let's actually take a closer look at what these predictions look like. And if you do plot them out, the blue is the actual value of the stock. The orange is the predicted value from our model. And you can see that it kind of looks like our model is lagging exactly by one day from the actual price. And that is actually not good at all. Because if you kind of compare, let's say like this May time, right? May 10th, 2021, the stock price was somewhere down here, but we were way up here. And it's only until the next day that we saw that it kind of corrected its value. But the stock just kept fluctuating even more so. And it's like that for almost every data point. You can probably see that effect right here on the table, right? So on this first day, which is like July 2nd, the actual value is 678, which we predicted correctly. But then it fell down to 659. It fell down by quite a bit, almost like 20 bucks right there. But we're still predicting way up here, which was the same as yesterday's truth. And then when it comes to the next day, now we're at 650. Now we predict like 660 though, because we're still playing catch up with the truth. And so you can see that it's really not that great of a predictor if you kind of look at the numbers. And so what we think, well, what I think is actually better is we have an evaluation metric where we're kind of creating a stratification of sorts. So let me just scroll down to what I'm actually showing you here. All right. So here is a graph where the x-axis is the percentage change of the stock of the actual stock day over day. So let's say from today, let's say it was $100 today and yesterday the price was $90 for today, the percentage of increase in change, the percentage change would be 10%. And like that, I'm labeling every single sample in our data set with the actual percentage change as you see here. So it would be 663 minus whatever the previous was divided by whatever that previous was to get a percentage change. And that was like 0.29% apparently here for this specific case. And then what I'm doing is now I'm stratifying these samples. So basically here, the zero is saying that, well, let's consider only the days, the samples for which the day over day percentage was at least, I mean an absolute percentage change was at least zero, which is like everything, right? And that's where we get like somewhere around like 3%. That's what we're, or rather, yeah, it's a mean absolute. This is like, it's supposed to be around like 16 something like that or something like that. Yeah, but then let's say that we look somewhere up here. Yeah, let's say this, right? So what if we only consider the days where the change was 4%, right? That means that's a pretty big change over there. And it looks like we're getting worse at predicting them. We're $35 off. The mean absolute error would have been 35 if we considered only those points. And then how many days is that though? Well, I have it on this other Y2 axis over here. So 4% is like, yeah, it's like 75 days maybe or something. That's pretty big. That's a pretty large number of days that we're predicting wrong. And we're getting progressively, this line is showing that we are progressively getting worse and worse and worse if the change from day over day is more and more. And that is bad. That's just indicative of when I look at this graph, I see, okay, we're not that good. But when I'll just look at this number that we saw before, just that 3%, I'm like, oh my goodness, we're at 3%, that's so good. But I feel like this method of just using percentages and stratifying it, stratifying our test samples accordingly, is a much better representation of what our data truly is representing and what our performance really is. I did it for the main absolute error, and we also have it for the mean absolute percentage error too. This is supposed to be, you multiply each of these by 100 to get the actual percentage. And you can see the mapes are increasing, which is not good as the, if the fluctuation is, you know, if the change is higher, we do expect this for any model, but this is pretty drastic. It's well over linear, so pretty bad change anyways. So all in all, this data is really not that great at predicting big changes in the stock market, at least for now. Now what you could do is, well, a potential improvement is adding Google Trends data.  This is an example of like interest over time, which is a scale from 0 to 100 of, well, interest over time, which is I think like a function of the total number of search terms that search times like Tesla has been made. So yeah, we can actually fetch that through an API here, which is pie trends. And when I'm using that, I just append that to every single sample that we have. So we have a trend score, which would range from 0 to 100 for every single date. And now if we, if we kind of use this, well, I'm just looking at the search trend score in isolation, just alone in a, you know, through stats models. And well, I see that it's, it can be, it can be pretty significant in usage, maybe not in a linear model with all of those other features that we have. We might be dwarfed by that. But like, you know, the volume, it might be useful in a more complex model. And so that's, that's kind of all I'm going to show right here. But a cool thing that I've kind of like put together a little template for y'all to try out is, see, you know, what happens if you use a more complex model here, does it help, does it actually help improve performance overall? And by performance, does it help not only improve the, that 3% overall performance, but also with the stratified evaluation that we were talking about too, does it improve that? Maybe there are other things you'd like to try out too. Like I just use one day of previous data to, for, for every single sample. But maybe you would want to, you know, look at, you know, the last weeks or the last months of data to see if that would add an additional like predictiveness or predictive power.  I don't really need or whatever, five or 10 years of data. I don't need so much data potentially. I could have probably just gotten away with a much smaller time frame. Maybe try to see if that affects anything too. I have a template over here. So would be willing to, would be interested to see what, what you guys come up with. Anyways, I have some cool like blog posts to here for, you know, just Google trends and some code as well. All of this will be linked in the description down below, but I hope you kind of got the overarching principle of what this video was about, a video to introduce, well, another video on time series analysis and how it can be applied, how you can evaluate time series models in a different light.  Your, if your data is bad, your model will also be bad. If your data is very noisy and up and down and all around, you can't expect too much from your model. You will need to either redefine the way that you structure your problem or well, something else entirely. So I hope that helps you get a good sense of what's going on here. I've kind of explained a lot about like the code and the pipelines in previous videos. I like to structure my, my models in this way. And I hope you do too. It seems very clean to me. Do let me know what your thoughts are in the description down below and we will see you in the next video. Take care. Bye.


Aviation Story, DCS-253-1 Charlie-67 Vietnam, AFBR-MACV-5-2-67 Army, recorded at 3 and 3 quarters. Scene 9, ARAs wild. Try to aim towards the... Just a little right of the finger as we go in. 1, 2, 3, last round, slash. Over. Before the Vietnam conflict, infantry soldiers have always been called upon to march into war. But now with the new concept of air mobility, units of the 1st Cavalry Division Air Mobile at An K and other infantry units in Vietnam are airlifted into battle by helicopters. The coordination involved in this new concept is more complex than meets the eye. Putting this 1st Cav infantry unit into a landing zone or LZ involves various elements. Among these are artillery, armed Chinooks, and aerial rocket artillery or ARA. The first step is to find enemy activity. This is the job of the aerial scouts. They fly at low level in a Bell OH-13, searching areas for Viet Cong activities. After the selection of the landing zone, the coordination and planning is completed. When the operation is to begin, the LZ is prepared by a combination of ordnance. Each phase of the assault is coordinated by the battalion commander and the S-3 officer and artillery liaison officer from a command and control helicopter. According to the timetable set up by the S-3 and battalion commander, if needed there will be an airstrike by the armed Chinooks. The Chinooks strike lasts approximately 5 minutes. Due to its nondiscriminatory nature, the armed Chinook is rarely used when Allied forces or friendly Vietnamese are in the area. Immediately following the Chinook comes the artillery barrage. Artillery is fired for about 5 minutes, but the most important phase of the operation commences just prior to the lifting of the artillery. The lift ships carrying the infantry units are preparing to move into the area. Moving in front of the flight leader of the lift ships are two aerial rocket artillery ships or ARAs. Each ship is equipped with 48 rockets. The ARAs move into the landing zone immediately after the last round. The ARAs move into the landing zone immediately after the last artillery round has exploded. The ARAs are in constant communication with the artillery so that they know the last round has exploded before they make their assault. In their strike, the ARAs use only half of their rockets and then fly support in case of any heavy enemy resistance around the LZ. Now that the LZ has been severely pounded and softened by the artillery and air strikes of many different kinds, the infantry units enter the LZ with the secure knowledge that the initial landing will be secure from the sights of the elusive enemy's weapons. Such a concept of air mobility tactics would fail without the coordination, the planning, and timing, all of which play an important role in securing landing zones for the men of the 1st Cavalry Division Air Mobile. Reporting for the MACB Office of Information, this is Army Specialist Mike Baker. Reporting for the U.S. Army Special Photographic Detachment Pacific, this is Army Specialist Mike Baker.


Hi, welcome everyone to this online edition of Open Belgium. I want to quickly thank our three main sponsors, Mono Design Microsoft and Agentschab Binalands Besture. But without further ado, I want to give the floor to the speakers of today. Great. Thank you, Astrid. I'm going to pull up our presentation and we can get started. So hi everyone. Thanks Astrid. Thank you Open Belgium for having us here. And welcome to you all. Thank you for joining us. We have some new ideas and we're really excited to engage with you all today. And some of the websites we actually will share at the end are quite literally hot off the press. So this is very exciting. A few reminders first on this new platform, Big Blue Button. It's new for us, maybe for you. First, we have the video and microphone turned off for all participants right now. So you don't have to worry about it. But we will allow you, give you the option to turn it on later for discussion.  This main presentation will remain steady if you choose to drag and drop the boxes of the presenters. That's me and Gulshan and the slides that we're sharing. So let's get into it. So before we jump into these real juicy topics of open data and social justice, we can introduce ourselves. We're here with Civic Software Foundation, which is a U.S.-based nonprofit with a global network of collaborators. That's why we're here with you all today also. We create technology and applied practices that reinforce equity and democratic values. So my name is Anjali Mehta. I'm our Chief of Staff. And my name is Gulshan Kudar and I'm the Director of Research and Learning. Great. And hopefully we can spark some new ideas today and we'll have full contact information at the end. But in the meantime, please use the general chat amongst yourselves. We unfortunately don't have another type of moderator, but we will try to track the questions and keep time at the end for them. So let's get to it. All right. Well, let's today start here. We hope that everyone joining us today came to open Belgium 2021 based on some fundamental shared understanding and something of our event description, spark some ideas and interest. So we hope that you have some understanding of the larger open data movement. It's good intentions and it's a fake ineffectiveness. The growing body of scholarship and debate over and debate over criticisms and solution oriented innovations. So what do we intend to propose as a remediation and why? Let's get into it. So what is open watching? Open watching is actually the main problem we'll be tackling today and build our discussion around it. So we define open watching as publishing data for the sake of having evidence for data driven decision making or creating a transparency agenda with no context around data or planned use cases. And with little to no consideration on implications and embedded biases. To start, let's examine how open watching is mainly the effect of two well intentioned but ineffective motivations for open data. First, transparency. So the idea of transparency within open data movement has more often be presented as a one stop shop fix. You can imagine the logic. Look, our data is published on our public website. So we are transparent and we are accountable. Sometimes this even leads to the measurement of accountability based on the amount of public data sets that are openly shared. We know for a host of other reasons that even if all the data sets were published and made open, it still won't be a clear picture of what happens inside of an organization throughout data's life cycle, decisions around data, and how their practices served. So we tackle we understand there's the first line of intention transparency and the second one is data and technology determinism. I believe everyone is familiar with phrases such as the numbers speak for themselves or all you need is data or data has a better idea. So these all signals the wider cultural connotation data that cultural connotation that data is objective and truthful and inherently valuable. So data driven decision making is actually an outcome of this idea that data driven decision making. So that's why it's called. So data is something accurate and is something accurate than anything qualitative and that because of this, the resulting actions are just the way things have to be. Understanding and the notion of data driven decision making also feeds a real and palpable excitement to quantify every aspect of human life and experiences. Over reliance on and deep trust in data to understand issues to expect data to show us what's wrong and make decisions is a pernicious and cynical problem. So you see on one hand we have transparency and on the other hand we have data determinism and these two strongly connects to social justice and in actually circular problem. So let me explain what we mean by this circular problem. The first the cycle starts with data sets published and made open with no context around the decisions motivations or processes along the way. This is what we call open washing. Second step then comes this process leaves data constituents, people who are presented in or impacted by a given data set. So these people are left out of this data life cycle or any decisions around it. And then what happens is they turn into resources for data. Then this brings the next step, which is giving no context around data sets obscures the bias nature of them, which then leads to data sets being used as piece of evidence in defining the problem and agenda setting. Well, no context around data then leads to defining the wrong problem with bias data sets, which then causes unjust and biased resource allocation, causing real life harm to oppressed and marginalized groups in society. So there is no real accountability around these decisions and even no requirements to be accountable because it's data driven decision making in the end. So this leaves the questions that rise from unfair data implications unanswered. The logic of more data set equal more data equals better understanding is being used as a metric of success for these so called decisions. So what happens then in order to measure the success of these decisions more data is being collected and then published again with no context around it, which then starts the cycle back. It's hard to imagine how deep this cycle is because it feels like it's how we've been working for the past many years. But what Gulshan has been talking about is just a bird's eye view of a large body of scholarship and research on this topic, or certainly not the first ones to call this out. But with this foundation, what we really want to get into is what actions we can take. How can we address the heart of this problem? How can we sustain the movement and really create a bedrock for lasting change? Across leaders in the field, all research, academia, research, think tanks, there is a resounding consensus, in theory at least, for a purpose driven approach through new collaboration networks. Specifically when we're looking at these problems of open washing, of transparency, of data determinism. You have think tanks like the GovLab in New York University and the Associated Open Data Policy Lab, expounding the idea of a third wave for open data. There's a lot on this third wave and I encourage you in another place to go research that. But all that they have to say, they identify the needs to address these problems in three main points. One, that publications need to publish with purpose. We need to foster new partnerships and we need to prioritize data rights. And they call out repeatedly across their publications, this focus for context, and I'll quote from one. This latest iteration of the open data movement concerns itself with a technical, social, political and economic context in which data is produced and consumed. This is quite resonant with arguments in data feminism, a book published about exactly a year ago from Catherine Dignacio at MIT and Lauren Klein at Emory to major inspirations for us. But in this book, they also have a call, a specific call for action for context. But they also acknowledge that not having the right incentives or networks make it really difficult for this context to be created and to really happen. So I'll quote from their chapter literally titled The Numbers Don't Speak for Themselves. They say, governments and data providers have not invested as much time and resources in providing context to end users as they have in providing data. And data sheets for data sets are great, but can we expect individual and small teams to conduct an in-depth background research project while on a deadline and budget? So we have come to conclude that this purpose-driven approach that is meant for creating context in the collaborative network that will create it needs to be established outside the current data lifecycle. The question is now, how do we do that? Who should act and how? Now we're really getting into why we brought you all here today. Based on what we heard from Anjali, then we can define context as the situated knowledge in which a data set was produced and abstracted from through collection. In order to break the open-washing cycle which I explained earlier with specific regard to creating space for social justice, we need context. And as civic, we offer a methodology, structured context. Structured context is an applied methodology to remediate open-washing effects with inclusionary practices. Social structured context itself is a technical metadata schema designed to address these two main critiques. To address the pseudo-transparency, structured context focuses on commitment and trust. And to counteract data determinism, we put lived experiences of data constituents at the center throughout the data lifecycle, capture what has fallen through the cracks of data categorization. This quote from Howard Zinn reminds us once again why we need something more than data and why data is not answered to all of our questions. It reads, statistical historians have tried to assess slavery by estimating how much money was spent on slaves for food and medical care. But can this describe the reality of slavery as it is to a human being who lived inside it? Can statistics record what it meant for families to be torn apart when a master for profit sold a husband or a wife, a son or a daughter? So, given that we have this definition of structured context that we now want to apply with a methodology, we can talk about how to really build this. So the collaboration that builds these inclusionary practices within structured context is recognized through iterative cycles, engaging stakeholders and constituents that are both inside and outside the normal data lifecycle. Pictured here is a simplified form of civics infinity loop collaboration model where we highlight that energy continuously flows through the system. We'll get into the different actors and stakeholders that lie along this path. But first, let me explain exactly what type of context we mean, what specific questions make up structured context. But based on the calls to action from all the previous scholarship and research we are building that we're building from. We've identified and created two main buckets of context to form structured context. And that's where our collaboration model is focused on. The right hand side of the loop is what we call foundational context. This is how we understand and capture the production of the data set. Essentially, this context is answering who collected the data, why they collected it and how. The left side of the loop is transformational context, alluding to the cultural shift that can occur once this practice of structured context is in wider use. This context goes beyond the data lifecycle to capture the resonance of a particular data set. This is where we break ground on the deeper situated knowledge of lived experience. This means engaging with data constituents, as Gulshan defined as those who are represented in or by a data set, as well as other domain experts to explore the social justice context, historical context, and limitations and use cases in regard to ethical governance. Specifically, this can mean things like questioning the potential harm caused by a data set, the historical connotations for certain categories, and for which uses applications of this data set is best used or not used for. So to really explore how this ecosystem will function, we want to hear from all of you. Our collective discussion in this next few minutes will then lead us to a fuller group discussion that will lead to further engagement. So we will be splitting into breakout rooms in a minute, and we'll all be contributing to the same matrix on a shared board. So the purpose of this is to first understand the different types of people involved in that infinity loop collaboration model we just described. So we think of actors as those who will be involved in creating or contributing to context in some way, either foundational or transformational. Stakeholders are those individuals or groups who will benefit from this type of context being created and distributed. And to be clear, we are not advocating for the actual data to be shared any further or more publicly, just this resulting metadata that's created from capturing context. We want to understand how these different people relate to the different levels of context, and the stickies that you'll see in the shared board are placed to brainstorm who, what, and why. So the way this will work, we get off to the races. Through smaller group discussions of five to six people, we want to imagine a better world where we are each civic agents connected to this larger community. We hope we can allow yourself to brainstorm and dive in here without holding back on constraints like funding or resources or scale. We have some ideas there and we'll be talking about that later. So let yourself imagine. The goal for your group is to collectively complete the template, at least once per quadrant. And don't worry if your notes and the stickies match the grammar of the template sentence. Other just logistics for these breakout rooms in this new platform, you will be automatically placed into a room. You don't have to worry about that. You might be prompted with that echo test again that you saw coming into this main room. And again, when you come back, when the breakout room ends and you come back to this main presentation. But other notes, the general chat will stay open to communicate amongst yourselves, as well as private chat to meet a cushion for assistance. You'll probably saw more buttons at the bottom of your screen. So your video and microphone can be turned on once you're in the breakout room. And last, the cushion shared the link to the mural board in the chat. This is a shared brainstorming space. And this is a direct link. So you will not have to sign up for anything. It will open up in your browser directly sending you to the board where you can edit as a guest. So any quick questions on logistics? You can type them or ask. All right, then. Well, I'll have Gulshan send us off. We'll be jumping into some of the breakout rooms and keep the conversation going. So looking forward to it. Let's go, Gulshan. I see there are people joining in the breakout rooms. Oh, great. We have three rooms. So we have one people in two breakout rooms. Astrid, are you still on to help? Okay, now more people are joining. And if you want me to join a room, I can also join now. If you want to join a discussion. Yeah, sure. I'll wait a bit to see who here seems to be alone at the moment. So we can move him to room two. Yeah, I would do that. And maybe then I can join room one. How do I do it? I think for me, I can't see who's in which room. Or if I join room three and tell here to go back to the main presentation and then be reassigned. Okay. Let's try that. Okay, I will join his room now. Okay. People are in the mural board, so hopefully it worked. Our timing is really good. Yes, I can invite people who are not in a breakout room. I'll add to a sticky make sure people are going. Okay. I'm back. Yeah. He left the audio, but okay, he's in room two. Okay. Now I see it. Is it okay if I join room one? Yeah. Okay, see you later. It's nice I can send invitations to people. Maybe we can join. Maybe, okay. One of us can stay here and then one of us can visit rooms. So I can go for five, ten minutes, then I come back and then you can go. I'm just on the mural. There are adding and I added a few. I added two just so it looks like. That's good. Okay, I'll go see what they're doing. Okay, have fun. Perhaps you're not actually in a breakout room. You mean you have to be in a breakout room here as well? Yeah, so let's see. This is the main room and they're in breakout rooms. Let me see if I can help get you into one. Thank you. Because I clicked on the app from mural. Oh, that's good. On your left side, do you see like where the users are listed? I see users, shared notes and messages. Oh, okay. So we don't see the breakout room. I see Gulson, her name. I think I can get you into the room. This is a new platform for us but we should be able to get you into the room. Thank you. I understand that it's hard. Complicated a little bit sometimes. It's an adventure. Yes. How did everybody on board in the second room? Maybe by explanation. I'm not sure. I'm back. Okay. Do you want to join room one? Astrid says they're a bit stuck. Oh, okay. Or if also trying to get me into a room. Hey, Gulson. There's only five minutes left but it can still be fun. I'll just wait until everybody returns and I'll see the results. It's fine. Okay. Thank you. Gulson, do you want to join room one though? Because Astrid asked. Okay. All right. Are we all back? I don't know how long we have left. I think we'll show the rooms ended. Yeah. Okay. No warning but I'm glad that worked. Great. Well, welcome back everyone. The mural board seemed very active. So thank you all for participating in our little activity. But let's come back together and see where this has brought us so we can So before we get into civic software sharing want want to hear from you all. So if you're comfortable still using your microphone and we welcome video to if you want. Let's just start off with some top of mind thoughts or reactions. How did you see your these actors and stakeholders interacting around the what we call the infinity loop model or any anything that comes to mind from what it felt like to go through this exercise, whether it was difficult or easy. You are all unlocked by the way.  Hi, Lucas here. We had like the great help of Gulson. She came in and explained to us. We had like some yeah doubts if we understood correctly. And it is interesting to see that you're using this picture with this like loops. So it seems like everything is interconnected and depends on each other. And in this graph that we worked on on the my row, there is like this hard lines. And it seemed like, okay, there is the different shape, right? It's like, if I'm here that I'm not there. And I would like to raise a question that is it possible that basically you have to go through. There is the need of a foundational existence before you can even consider to get to a transformational one. Is there this dependency? I think it's interesting question. So when we can get into how we envision this program really working. And from a mental model, I think it's easier to think about if we have the foundational context and we understand where the data came from, then we can think about its historical context or previous connotations. But when we actually get working, if once you kind of are in conversation, I don't think that's literally necessary to have one and then the other. For example, engaging with, you know, we have term data constituents. Those represented in or by a data set might not be an individual. It might be a community organization or someone who has studied these trends before as an anthropologist or historian. And so engaging in that conversation first, or not first but simultaneously, can also help the foundational context workers, whether that's a data steward or a researcher kind of answering these questions, think about it more critically or open up new conversations that they hadn't previously thought about. And then answering questions that are quote unquote simple about how they collected data. But I hear your point that when we put the graph together to help make a simple brainstorm, how it also conflates the interactions and the collaboration in real life, humans talk to each other and don't have clear lines between them. I don't know, Gulshan, do you want to add more to that? We talk about this a lot. Yes, I think the concept that just popped in my mind is intersectionality because we do we as humans, we have multiple identities and multiple roles in the society. So for instance, you can be a data scientist, but on the other hand, a transgender person who is being influenced by the data decisions. So for instance, if data collection only has two genders, and then just a box saying other, then for instance, who decided on these categories, why there was this need for these categories can be a part of foundational context. But then when you dive deeper into this binary understanding and kind of surfacing this mentality around it, then it can, you know, slowly moves into transformational context. But first you have to identify the categories and the way data was structured and decisions. And then having that foundational information, foundational context, then we can move into transformational one. Yeah, I'll also add just one more and maybe other people have questions from it. And kind of that linear thinking we need foundational for transformational. But there's also like once you have that transformational context, you can answer even more questions about the foundational context. And so that's how the loop kind of keeps itself in perpetual motion. For example, we talk about foundational context as Gulshan mentioned understanding these categories and what it really means. That also alludes to privacy and security or ethical use in terms of limitations. And when you start seeding those conversations, when you get back into foundational context about how or who collected this methodology, it might open other places where you didn't think to document this before, but now that this question has been brought to the table, we can add even more context to our foundational side. Once questions and conversations have been opened by these transformational questions and conversations. So thank you, Lucas, for that dynamic question. Any other top of mind thoughts where these type of actors and stakeholders had you had an opportunity to brainstorm their interactions together before or emerging things that you may have seen when trying to think of stickies in each of the four boxes because we made you. Yeah, I wanted to share because I'm a data scientist and usually my work can be very isolated depending on who I'm consulting with. And it was really nice, even though we had the grids, it really helped me see like the whole picture very succinctly. Like, maybe I don't know exactly what the project manager in the company does, but being able to see what they, right, like their motivation and how they're part of each step in the process was really useful. Because when I saw what other people added, I could go and reflect back to what my role can contribute to what they're saying in the, right, what they say, oh, I want to help with this context. And I hadn't thought of that context at all. It's really nice to just see kind of the layout on the land and then go back and think how I can contribute to all those spaces. I can't, right? Maybe I can't, but like, it was really nice to, I could see the infinity sign at the beginning when I saw the grid. I wouldn't have thought of the infinity sign, but that's what happened to me at least that I kept going back and forward because I wrote something in the first block and when I got to the end I was like, wait, this relates back to the first block so then I would go back and forward. So I thought that was really cool to see. Thank you for sharing, Vanessa. And thank you again for joining if you have to believe. But I think that brings up a lot of good points. We have these roles and that's, you know, how so many of our businesses and society have been brought up how the open data movement functions. But it's almost hard to get this step back to look at what we're really doing. And so I wonder, kind of Vanessa, you helped me bring up in going through this grid system, even though we had this infinity loop model on the side. Any thoughts about the power dynamics between these different actors or types of context? We didn't ask specifically in the grid, but I wonder if having gone through that exercise, if it's bringing up any thoughts there. And I also would like would even augment that question how power dynamics allow or don't allow someone to exist in any of these spaces if any thoughts are there. Or no answers, but does this make you think of any other questions? Yeah, I guess like I hadn't even thought about that right because when I was in the grid I went rapidly to the foundational first grid. So I was like, well, this is where I have quote unquote power. But then I didn't really question, right? Is there other places that I could be contributed and I don't because I don't feel empowered to do so. And I bet it happens, you know, in combination with all the grid components too, right? Like maybe a private manager doesn't feel like they can contribute to the data acquisition process. But in my experience, that's not true. It depends on like the conversations you want to have. And not even nice. Like if we think about individuals and data owners and, you know, where is your input? And I don't know. It's a very great question, but I'm just kind of not sure what that would look like differently. I'm just stuck in the grid, I guess. Well, that's why we're here experimenting with a new model and proposing a new methodology. It's because these questions haven't been asked or really dealt with before. I think the whole point for us, for this even new methodology is to realize that there are questions we can and we should ask. Because of most often when we are really getting into this concept of predefined or this trends or this culture around data, we often just adapt to that thinking system. But with this methodology, a new way of thinking, we really want people to question and to think about what questions even can be asked. And we, of course, acknowledge not everything can be answered, but even the very act of asking questions is something amazing itself. Great. Well, thank you for your thoughts. We have a few other things we want to share with you. So we'll go through that and then bring it back just to open questions. So we discussed and throughout this session, we first recognized the problem and then identified two main factors that was feeding into the notion of open washing. And then this was important to acknowledge and identify this process so that we can have a clear understanding on what needs to be done, what causes this problem and how we can think about solutions that really addresses the core of it. And which then this grounded understanding would bring visibility around this topic, like places like this, when we get to talk about the possibilities or problems, starting this discussion and then having the visibility on these topics, that would allow us to collaborate and get action and move on to this active part of actually changing the things. So not only complaining, not only addressing the shortcomings and pitfalls of the current systems, but then moving to the active part and thinking, what can I do? What can we do to change it? What other possibilities, imaginaries we can think about? So we offered an intervention, a structured context as civic, but we acknowledge that this method itself will not change the world. And for it to work, we need to create this ecosystem, including many actors and stakeholders, active participation, commitment, and open to be questioned and to ask questions and get comfortable from time to time and think about really our own positionality in this process as well. And so what we offer today is a place to start and an invitation to have a conversation. The connections seems to be lost. Yes. She should come back in a second. What she was saying is that we have this model to start a conversation and we call it the flower of hope because we're very optimistic and we think if you plant the right seeds, we can bloom into a beautiful garden together. But the point of this visual and this model is to have a clear vision of what structured context can do for us and how we can build it together so that you can bring in different levels of trust and commitment through visibility. So that's one of our pedals here and the agreement of this visibility. It's really bringing to life and shining a light where it's often hidden the structured nature of, constructed nature of data. And so we have this model to highlight the collaboration of what it would take to bring visibility to these different points in an open environment that allows for questioning without shame. Gusion, is there anything else you want to add to this flower of hope? I think I picked up where you left on and off. Sorry, I dropped out. But yes, the point is we really try to stay away from this mentality that here is one thing we created and it's going to fix everything. But we do have to think in a systemic level. And we do have to approach this issue of open washing and in the end, social justice as an active action and not just in silent efforts. Yeah. So with that, and all these new ideas of what we're really trying to, you know, engage everyone on, want to share this very new thing. So first, again, thank you for participating in our quick but lively brainstorming activity. As we mentioned a few times, you know, we're not the first ones to call out this need for attention remediation. But we do have an actual practical application to implement this methodology for structured context. So big news, Civic Software just launched a new program and call for participation in building out what we call the Civic Data Library of Context. And this is going to be a novel digital public resource to host a full catalog of contextual metadata, all created through the inclusionary practices that we've been discussing around the infinity loop model. So if any piece of today was at all interesting to you or sparked new ideas or questions, I invite you to follow our website to learn more and find ways to further engage and contribute. This is a wide call for participation. We are looking for individuals who want to actively participate in grounding themselves in this new practice of structured context. This program called the Emergent Lab is a place for applied learning and talent development. We're also looking for different organizations who share the values embodied by structured context. Organizations whose mission in some way, shape or form fits in with everything Gulshan and I have been talking about today. And these organizations can become implementation partners with us and implementation partners means a few things. It could be any or all of getting training for internal competencies can mean going through the applied process of contextualization and publishing the resulting metadata in this new library product. This means studying a foundational framework designed to empower the full teams and illuminate at a wider breadth the invisible human work that goes into creating data ecosystems. So again, more details on this website. And I'll leave you here with all of our contact information. The website again, a feedback form if you want to do it now also email it out. We have a few minutes left. I know we're at the top of the hour and it's scheduled past and we want to open it up to more questions either about our program, anything we've discussed today. Any other questions that may have been sparked. Yeah. Any questions anywhere. Otherwise, again, big thank you for joining us. It was really exciting for us to be able to share and engage with you and you already brought up so many good points for us to keep thinking on both in your questions and in the mural board. I have a question about the library of context so you want to. What's the ultimate goal. The library is to create this new product in this new visual representation and actual resource that captures this new form of structured context. And so we're taking from the beginning those quotes and from data feminism recognizing that there's no data intermediary or, you know, current actor in our data lifecycle system that can take on the accountability and responsibility to creating this context. And so that's why civic software foundation is setting this seed so that other people can come in with a structure and infrastructure and funding setup. But creating this place in a repeatable way to create structured context. And so the goal of the library is to one make it easier to see what context can do. It will help share this metadata in new forms. It will get more people engaged in being able to ask these type of questions without hopefully, you know, feeling the shame because it's not yet trying to fix anything. It's just trying to make visible these new questions and conversations that we can ask. So it's both a product to share a catalog of metadata and the program that is going to create this product is a true inclusionary practice bringing in a lot of different people for experimental learning and emergent creation. Okay, cool. We'll definitely check it out. Yeah. And I think it's a good reminder here again that we don't, we don't, it is not a requirement to publish the whole data set to be participated in the library. And so that all that sense we understand there are some regulations and of course organizations are really concerned about, you know, crossing the line and afraid of doing something illegal. So in that sense, we, we advocate for context data and say that we don't even have to see the data itself so data points itself but just the categories and understanding the processes it went through would be a good start for creating context. Well, leave it here unless there's any more. I welcome everyone to this.


Hello everyone, welcome to the module on Intermolecular Forces and Potential Energy Surfaces. We are currently discussing about potential energy surfaces and the potential energy surfaces of different kinds of molecules, either triatomic or even other kinds of systems. Before we get into looking at potential energy surfaces in a little more detail in this lecture, let us recap our memories on what we discussed in the previous lecture. In the previous lecture, we had started out by looking at what are called as potential energy surfaces and we had said that these are nothing but 3 dimensional or a n dimensional hypersurface in which by playing around with the coordinates or different modes or different degrees of freedom of a given molecule or atoms or ions, one can actually look at the potential energy of the system and such a diagram of potential energy versus the coordinates is what one would call a potential energy surface. And in case of where we are looking at chemical reactions, these coordinates are typically the vibrational degrees of freedom such as the bond lengths, bond angles and various other degrees of freedom. So we had seen that based on the kinds of the molecule, one could have either a hypersurface of 3n minus 5 or 3n minus 6 where n is the number of atoms or ions in the given system. Further, we had also said that these 3 dimensional surfaces can also be mapped onto 2 dimensional picture which is called as a reaction coordinate diagram and here what one does is to look at along a particular coordinate of interest that is either a particular bond length or a bond angle or even a combination of them and then draw them on a 2 dimensional representation. This is typically what one would use in looking at the transition states of a given reaction or even the transition state theory of a particular species. So having said this, we had also learnt or discussed what is called as a saddle point which is a very important aspect when we talk about potential energy surfaces and reaction coordinate diagrams. So if we take a diagram such as shown here, the one which we had looked at in the previous class as well, what we said is that this point where going from the one valley to the other, the hill one crosses is what is called as a saddle point and we had seen that it is called saddle because of the way it looks like that is on the 2 sides the energy is actually going down and on the orthogonal sides the energy actually increases. So an important point to note is that a saddle point is invariably highest energy point on a 2 dimensional reaction coordinate diagram however when it comes to potential energy surface of 3 or n dimensions, it is not the highest energy point. It is always somewhere in between because there are points of highest energy such as you could see here on the green parts of the surface are the ones which are actually having a higher energy compared to the saddle point. So a key distinction is on the 2 dimensional picture that is the reaction coordinate diagram saddle point represents the point of highest energy whereas on a n dimensional or n dimensional hyper surface which is called as a potential energy surface saddle point does not represent the point of highest energy. This is a very important distinction to keep in mind. So having sort of refreshed our memories on what are called as reaction coordinate diagrams and potential energy surfaces now let us start looking at a very simple case of a H3 system. So for that I am going to try and write down the reaction which we are interested in first. So let us say I have a hydrogen atom HA which is interacting with a hydrogen molecule. Just to distinguish the hydrogens I am writing them as HA, B and C and this would give me HA, HB plus HC. So if I am interested in this particular kind of a system where hydrogen atom is reacting with hydrogen molecule to give rise to similar product but with a slightly different connectivity. In this particular scenario what can happen is the 2 entities that is the HA and HB, HC can actually approach in 2 different ways. One is they can approach in a collinear fashion that is HA is approaching the HBC bond or it can also approach in a non collinear fashion that is at a different angle or at a particular angle. For the sake of the convenience and what why and it also turns out that that is the most reasonable way we shall look at only the collinear case that is the HA and HB, HC are actually interacting in a more of a head on fashion. So if we now restrict ourselves to the collinear way of interaction then in order to represent this we can actually choose 2 coordinates or 2 degrees of freedom. One is the bonding between the HA, HB that is we are going to form a new bond between HA and HB. So we can look at the how does the potential energy varies as a function of this that is RAB and we can also look at how does the potential energy varies as this bond actually gets ruptured that is RB and C. So these are the 2 parameters which we can vary and look at how does the total energy or the potential energy of the system varies. So if you now agree that these 2 parameters are good enough to describe the particular system we are interested in that is H3 in this particular case then one can come up with the potential energy surface which is shown on the left hand side and invariably such potential energy surfaces are constructed by what are called as electronic structure calculations which are typically based on quantum mechanics based computations where both these parameters that is the distance between the HA, HB and the distance between the HB and the C are varied and at each of the coordinates that is each of these points the total energy of the system is or the potential energy of the system is calculated and that is plotted as a function of these 2 coordinates. And if one does it we would for the H3 system we would end up in a diagram which would look like this. So we will try and dissect this a bit more and try to understand this. So let us start by looking at this diagram in a bit more detail. Okay, here I am trying to look at HB plus HC, so HB, HC which is bonded and I do not yet have HA which is still interacting with it. So if I just take the reactant that is HB and HC then I would see that the one which is one we can see on the left hand side is the one you can look at it as a one dimensional potential energy surface of HB, HC correct. That is you have an equilibrium bond distance where the HB and HC are bonded and now in this particular case the HA is somewhere here, this is at an infinite distance and only once the HA starts approaching the HB, HC the energy of the system varies and then they interact and ultimately lead to the product where HA and HB are bonded and HC is left out free and that is what one would find if you look at this particular part of the potential energy surface here you have HA, HB which are bonded and HC is now free. So I hope you see or you can imagine this where I am going from a one two dimensional kind of a potential energy surface of one hydrogen molecule to another that is the product and what is interesting is how or what path does it take that is something which is very important and we shall look at it in a bit more detail. But to look at it in a more of a bird's eye view what we have is we have two degrees of freedom in the HC system that is the two bond lengths which can be varied that is RAB and RBC and if we take these two as the variables one can construct a three dimensional potential energy surface where two accesses are these two coordinates and the third access is the potential energy and that would look like what is shown here in the on the slide. I hope this is clear or at least understandable and if that is the case now let us go ahead and look at how does one actually take this and map it on to a two dimensional picture so that it is easier to understand these potential energy surfaces for further analysis. So again we start with to map this potential energy surfaces from a three dimensional to two dimensional picture let us just start by looking at this 3D picture which we have of a H3 system which is what we just looked at in the previous slide and just another way of looking at it is that you can now forget about the pink parts and only look at the gray area if you just concentrate on the gray area all I have done is I have just scooped out the pink portions and I have just left with the gray area and if you just now look at the gray area what you would see is something like this and the most important point to note in this is that here you have the reactant which is this particular part and then you go to the product which is this on this side that is the this coordinate but what is important is while going from the reactant to the product you go through a slightly higher energy position which is actually hidden if you look at this three dimensional picture you do not see this higher energy point when you look at it when you look at a three dimensional picture so that is the reason why we have scooped out the pink portions and then hopefully this is a bit more clear that while going from the reactant to the product you have a slide you have a barrier which you cross and then fall into the product regime. So now the question is how do I map this into two dimensional picture so to do this what people typically do is what are called as slicing or a counter maps and that is what you see here on the right hand side this is what is called as a 2D counter map of H3 system and the way to obtain this is first what we need to realize is that if you actually look at this picture the three dimensional picture here you have the you have the RBC and the RAB actually both pointing at one particular direction and whereas if you look at the now the 2D picture they are actually not in the in the exactly same orientation that is this two dimensional picture is obtained or at least it is shown by just tilting it at a particular angle. So please realize that this diagram is actually not in exactly the same way as this three dimensional potential energy surface but it is just slightly tilted so that the 2D map becomes a bit more clearer. So just to make that clear that point so if you now take this okay so if you now look at this picture what you see is that RAB and RBC are pointing towards the top right hand corner that is this whereas if you now look at the three dimensional potential energy surface you have these two pointing at the down so you will have to actually take this three dimensional object or three dimensional potential energy surface and rotate in such a way that this the bottom corner actually turns to the top right corner so then you will be able to translate or at least you will be able to imagine the how one can go from a three dimensional surface to two dimensional contour maps. So this is point number one and the second point is what typically people do is once I have done that rotation that just in plane rotation then what people typically do is you take a slice or in other words you choose a particular potential energy for example I will let us say choose this I am just drawing a plane here so I have chosen a particular potential energy let us say I call it P1 so then at this point I will look at the at for both the coordinates how does the potential energy surface look like and I see that would typically correspond to this something like this because you have this shape which is going here this particular line or this particular curve is what one would get as this when you cut a when you cut through it you would get this particular line right so that is what I am trying to show here and to some extent you would also cut this that is here somewhere you would cut if I pass a plane through it and these are the two lines you would get for a given particular potential energy now I will come down a bit and I will again put again slice it or I will again go to a different potential edges let us say I call it P2 so then I can actually keep doing like this I can just keep slicing this or taking a slice at different potential energy surfaces and look at how does the 2D map look like so as you keep doing this till you come to this point or till you come to this point you only cut these two sides that is this side and on the other side is this right once you hit this particular point somewhere as you keep going down the potential energy you will hit this point at that point you are mostly at this this juncture okay and now if you actually go down further then what you see is that you actually see a this is this can be imagined like a small hill here so if you have a small hill then if you are cutting through this then you would get a you get these kind of let's say curved lines this is what one would get as you go below the top of this hill this is the top this is the hill top if you go below that then you would start getting curvatures like this and you can actually as you go as you go even down further down you would this we would become actually the the radius of this or the the radius would become lower and you would end up at the minimum of the particular bond that is either the reactant or the product so this is what typically is done to obtain a 2 dimensional contour from a 3 or n dimensional potential energy surface. So I will again repeat a few of the silent features which are used to attain a 2 dimensional contour that is first you take the 3 dimensional surface and you slice or you take a cut at different energies or different points and then look at how does the 2d plot look like and that is all that you are trying to plot here on the right hand side and I hope this is you can visualize this to at least some extent that the way we are able to construct and the most important point is that if you take a look at this the bottom left picture then you will realize that there is a small hill with a barrier which is actually hidden in this 3 dimensional complete 3 dimensional potential energy surface and only below the barrier you start seeing the corresponding contour maps which are which correspond to either the reactant or the product. Alright so I hope this gives you a feel of what are these 2d contour maps so having had some feel for this now let us go ahead and try to see the H3 system in a little more detail. So here again what what is done is the same picture which I showed you on the previous slide that is the RBC and the RAB and what I would want you to realize or appreciate is the following that if you now look at I am just going to draw a dotted line here so this is the equilibrium distance for AB which is the product and similarly I can draw something along this line and this is the R equilibrium mon length for the reactant that is B and C right and if I were to this is where the they would all the energies would go toward the infinity or toward the isolated atoms. So if I have this particular picture so then if let us say I have a hydrogen A which is coming towards HB so how should it come on the potential energy surface right so there are 3 possibilities and we shall look at all of them in little detail. So the possibility number 1 is or let us call it A because that is the notation we are using here. So I have HA and HBHC so this is coming approaching this so what I am trying to show is that the HA is approaching the HBHC progressively and at that moment the HBHC is kept constant or the equilibrium bond length of HBHC is kept constant it does not move and at beyond a certain point now after this what happens is that HA and HB bond and then you have HC which is left out okay. So this is one particular scenario where I have the HA which is approaching the HA is approaching the HBC progressively and in all this time the HB and the HC bond length or the equilibrium bond length is kept constant it does not change. This is one particular scenario you can think of if that is the case let us see how does the potential energies or the trajectory would look like on a 2D contour. So we said that RBC is the equilibrium distance for the BC so that is what we have kept constant here if you now look at the trajectory which is marked with A that is this particular line if you start taking look at this particular line the R equilibrium is more or less constant it does not change pretty much till you till you hit this line till you hit this curve right and and all through all of this what is changing is the hydrogen atom which was at the which was far away is actually slowly coming in slowly coming in and trying to go towards the HBHC bond that is what we have shown here on the right hand side. Once it is at almost this particular juncture let us let us call this this dotted point at this point what happens is then suddenly the HBHC bond would break apart and then the HA HB would bond would form and you have significant lowering of energy and that would lead to formation of the R equilibrium AB which is the product here which is what you would see right. So this is one particular trajectory the reaction can take place but if you already notice what you see is that this is along a slightly higher energy path because you are looking at it from a this path almost coincides with an outermost line which means the reaction is taking place along a higher energy surface right. So now the other option or the other possibility is what is called as B let us take a look at the B. So the HA is here and let us say HBHC is to begin with okay. So in this particular case what is happening is HA is approaching the HBHC but in this case the HBHC bond is actually getting loosened up far more quickly it is actually going from the equilibrium bond length the HBHC is slowly opening up or it is HBHC are dissociating much more rapidly than the approach of the HA this is another scenario right. So you can think of these two are the most classical ends of the spectrum right. So if this is what is happening then you would go along this path which is shown here B that is at even a small approach of HA which is along this direction you see that there is a significant lowering of the R equilibrium that is this angle this distance is actually now going is increasing and by the time it comes to the equilibrium it has increased so much and then suddenly little then it will the HBHC will break and it will form a HA HB. Then one can write HA HB and HC is lost right. So this is another example where the dissociation is much more pronounced compared to the approach of the incoming hydrogen atom. However the another possibility is that both these processes that is the approach of the hydrogen atom and the weakening of the HBHC bond can occur to the same extent or to a similar extent. So that is what we will call it as a KC. So in this case the approach and the breaking these two processes are occurring at the same distance or occurring at the occurring to a same extent. So in that case what would happen is you would take this particular path which is the C you go along the path C and then you ultimately end up in the product here right. So this path where you get the where you go through this particular pathway is when you actually cross that small hill which I told you in the previous slide where we had scooped out the pink part and only looked at the gray part. I hope you remember that there is a small hill and along the path C actually you cross the you cross the top of that hill which is the C and it turns out that C point C is the or the C dagger which is shown here is the least energy path among the ABC. So the reaction would invariably would like to go along C rather than A and B because A and B are higher energy or this is the low energy path the C. So this point or the C dagger is what is also called as a saddle point or a transition state and the geometries in and around that are what are called as activated complexes. So if a reaction is taking place along this particular trajectory or path then there is more likelihood of the reaction falling into the products rather than when it goes along A and B okay. So I hope this has given you at least some idea on how to look at the trajectories on a potential energy surfaces taking H3 as an example with this we shall stop now in the subsequent classes we shall look at a bit more of the final details. Thank you.


Hi everyone, Joe for jazby'scasebrakes.com with a really fun mixer here a Three box baseball mixer all definitive three years of definitive 2020 definitive 2021 definitive collection baseball and the 2022 edition Great great set by tops Looking forward to breaking this big. Thanks to this group for making it happen We had originally planned on three fillers for this You know, we only end up having to do one we were able to cancel some fillers add full spots back in so thanks Everybody who bought full spots. Hey, congrats to the winners in the filler as well All 30 teams are in let's roll it. Let's randomize names and teams Snake eyes two times easy one and two So after two we've got Logan up top mark in the 30 spot I'll put that in cell a1 And two times for the teams One and two. Good luck. We got pirates down to Yankees So remember if you're gonna trade there will be a trade one if you're gonna trade trade at your own risk Because there's three different years here Logan you got the Pittsburgh Pirates Dilo last Bob Mojo St. Louis Cardinals Scott with the nationals Nico with the Rangers Brian with the Jays Gary with the Guardians Gary Gordon Guardians Nice little alliteration there Alan Matsui with the Cubs Mark with the Royals Taylor with the Padres Charles He got my Dodgers Chris with the Red Sox Daniel with the Rockies Rob with the Astro Scott with the Giants Mark with the Marlins Daniel Braves and Mets Alan with the Tigers Daniel with the Mariners Charles with the Brew Crew Gary with the Halos Ed with the O's Mark with the Rays Taylor with the snakes Daniel with the shy socks Ed with the athletics Daniel with the twins Rob with the Phillies Ryan with the red legs and Mark with the Bronx bombers Let's alphabetize by team And we're gonna pause the video while we come back. We'll see if there's any any trades and then the break itself We'll see on the other side Welcome back everyone Here we are on the other side No deals are a little bit of trade chatter, but in the end no deals were done here on Wednesday the 8th 2020-2021-22 definitive baseball. It's a really nice three box mixer. Thanks everyone for making this happen. Unfortunately Unfortunately, these are the only box that we have so we can't run this back All right, so yeah, we're gonna just go in chronological order. We're gonna start with 2020 We'll go to 2021 and close with 2022 Love the love the big hit possibilities here Thanks everyone for for making this happen Maybe just maybe We'll make some dreams come true after there's like the little definitive Card right here that we're gonna use All right, first one is 43 out of 50 Chris sale Red Sox That's gonna be for Chris sale going to Chris Walker Now here's what I love about this If you go to MLB comm slash authentication I think it is if you punch in these numbers at really tiny JC 562791 these stickers are awesome because they'll tell you exactly what game this series came from And then you can look up his game log and then you'll see Then you'll see if he made a start both Charles get into both Here's Ahmed Rosario 23 out 35 Another relic with the with that sticker there. Love the authentication sticker that goes to Daniel and the Mets I'm hoping that that someday maybe Who knows how many years down the line But if this becomes a more regular thing, that'll be awesome. All right next up our first autograph is Joey Votto of auto auto 7 out of 10 That will be for Ryan and we're on since Natty We've got a Philly Reese Hoskins 26 out of 30 got that Philly's powder blue kind of like this shirt Philadelphia that'll go to Rob and we've got another red Barry Larkin game use Jersey and autograph 43 out of 50 That'll be for Ryan and Cincinnati Ron is Cincinnati the gold frame. It's always cool to see It's more reds one out of 30. There's he's a keynote rookie autograph And this guy was hobby hot when he first burst out into the scene a couple more here We've got Jordan Yamamoto 37 out of 50 rookie auto for the Marlins That's gonna go to Mark and the Marlins. This is Mark Marlin a NASCAR driver and the last one here is Jersey and auto rookie Jersey not no Brendan McKay three out of 30 Once a hot prospect now just Spend battling injuries But hopefully he can still make good Tampa Bay rays mark Is Jordan Yamamoto a Dodger? When did that happen? I know it's not in the Marlins and I think he went to New York New York Mets for a little bit 2021 definitive I think he's a I think he's from Hawaii Like a sponge right there as well Next box Good luck. We've got Xander Bogarts 49 out of 50. I Guess they went away from the authentic sticker this year Boston Chris. Oh, we got the sticker here Corey Seager 33 out of 50 That's vs 8 5 2 0 8 8 m o b comm slash Authentication I think compared to the pitchers. It's probably easier to see if the hitters had a The hitters were you know played that day Johnny Damon 9 out of 50 Jersey and autograph Chris and Boston Garrett Cole 5 out of 25 autograph for the Bronx that goes to mark mark s and the Yankees And we've got a Josh Bell 11 out of 30 Jersey and autograph That's gonna go to Logan in the Pirates with the spot that he won in the filler and the frame 8 out of 30 Rookie Otto Joey Bart That's gonna go to Scott and the Giants a couple more here. We've got a Davy Garcia rookie autograph for the Yankees mark s There's only one mark in this break for you mark And we've got a get well soon Walker Bueller 17 out of 25 on the road to recovery from another TJ Surge Dodgers that's gonna be for Charles and my Dodgers All right. That was 2021 Now let's move into 2022 The third and final box. Good luck everybody. I like the I mean maybe I'm partial because I'm a Dodgers fan But I do like the the blue tint that they put on these boxes is here All right final one. Good luck everybody. Thanks for the fill. Appreciate it First card is Carlos Correa 10 out of 30 three-color patch Stroze Rob, and we've got a mookie bets rally 40 out of 50 Dodgers edition going to Charles No, ma 10 out of that's a good shot of him doing his His his routine 10 out of 40 jersey an autograph no more Garcia para Chris and Boston no more is doing some TV work for the Dodgers network out here We'll do some color commentary every once in a while on TV or radio And studio work, there's the act Dennis Eckersley great penmanship 13 out of 25 great mustache Ed P with the with the a's Has come up with a lot more Catchphrases and you then you would think it's a whole whole dictionary 1950 40 that is Paul Molliter Piece of his lumber and autograph that is twins edition of Paul Molliter, and that's going to go to Daniel gold frame is laser show Dustin Padreau 19 out of 30 two-color patch and autograph Chris Walker in Boston nice break for the Red Sox. All right a couple more here. We have a Jake Myers rookie auto 14 out of 50 Rob of the Astros and our final hit In our definitive three box mixer is gonna be Whoa nice some good photography right there. It's Johnny Bench. That's Ryan with the Reds Defining images autograph. That's really cool Don't think it's I don't see a number on here, but it's still awesome, right? There you go There you go, ladies and gentlemen three boxes of definitive in the books Thanks for watching. Thanks for breaking with us I'm Joe for jazby's case breaks calm, and I'll baseball with you next time. Bye. Bye


All right, welcome back. So in this lecture, we are going to set up the logic for the fader. So the fader is gonna be an automatic way to fade between two screens, okay? And it also will allow you to set some artistic design choices as well. So let's jump over into Unity and get this started. Alrighty, so here we are inside of Unity. So the thing that we need to do is add another image. And in this case, we just want a single image. And again, I'm going to stretch it by holding down Ctrl Shift and Alt and hitting the anchor button up here and just expanding it so that it fills the entire screen. And for this particular case, I'm gonna just set it to a black, not totally black, but dark enough to feel like it's fading out to black kind of thing. I don't wanna go full dark, you know what I mean? It's always good to not make it fully black, all right? So I'm gonna rename that to fader, okay? So now in our UI system, the UI system is basically going to control this fader. So it's gonna fade it up and fade it down. And it's actually quite simple. Unity provides us some functions, some built-in functions that automatically do this for us. So I just want to implement that functionality into the UI system. All right, so we're gonna pop up in the UI system there. Okay, and what I need to do is create a property that is the fader. So I need to allow someone to assign the fader UI element to this particular script. Now we could automatically find it. Oops, I forgot to capitalize that. We could automatically find it, but let's just let someone assign it because I don't wanna do some more searching around in the start function, right? It'll be easier this way. So we'll just call this the fader properties. All right, and these headers just help you clean up your inspector editors, all right? Just helps kinda componentize the properties. It makes it easier for the end user or people who are using your component to figure out what the properties do, okay? So what I'm gonna do is just say public image. So I want an image. And in this case, I need to, in order to do that, I need to include the unity engine.ui namespace. So I'm gonna say public image mfader, okay, perfect. And with that, we're pretty good to go. The next two things is I wanna set in some values so that the end user can actually change the amount of time it takes to fade in and out. So I'm gonna say fade, duration, or actually we'll say fade in duration, like so. We'll initialize it to a value of one, so it takes one second to fade in. And we're gonna say float m fade out duration, okay? Is equal to one f. And I wanna name that properly. So float, not flay out. All right, so let's go and get this taken care of. So in the start function, what I wanna do is I first wanna make sure that we have the fader. So I'm gonna say if mfader, then we're cool. So we have it.  So I wanna say mfader.gameobject.setactive to true. Just so we know that it's gonna be turned back on, okay? So that basically initializes the fader and then we just need two public functions, okay? In here, so I'm just gonna insert them right here. So we're gonna say public void fade in, all right? And then we're gonna say public void fade out, like so, cool, all right? So in each of these, what we wanna do is we wanna say if mfader, because we don't wanna throw any sort of null reference exceptions. So we're gonna check to make sure we have it. It could have got deleted somehow at some point. So we need to check, okay? So if that's true, then we're gonna say mfader.crossfade alpha. All right, and you can see the arguments for this particular built-in function. We have an alpha value, a duration, and an ignore time scale, okay? So what I wanna do is I wanna say if I fade in, I wanna basically make this fader disappear. So I'm gonna set it to zero in the alpha. So it's fully transparent. And then I wanna pull in that fade in duration, and then I wanna say false for the ignore time scale, because I do want it to adhere to the current time scale of the game, okay? So let's copy this, and we're gonna say crossfade alpha, and when we fade out, I want this to be fully opaque, and I wanna use that mfade out duration, like so. All right, so there we go. We now have the ability to fade in and out between the screens, okay? And so basically, all we need to do now is we can just call fade in right here. So basically now the UI will just fade in, okay? Because we're gonna leave this set to true, and by default, the fader's gonna be fully black, so when this thing starts, it's gonna fade in, show you the screen, and then we're gonna utilize an animator to animate the transitions between all the screens. So this is, think of it as a global fader. When we leave, enter, or leave this particular level, it'll fade in, fade out. It's really nice, it's a nice effect, so. Let's actually assign it now, very cool. And the last thing we actually need to do, okay, is turn off raycast target, because I don't want this particular image to block the raycasts from the mouse when I'm hitting buttons. If this is, because currently, this is at the bottom, okay, of this hierarchy here, so it's the last child in this hierarchy. So that means that it is sitting on top of all the other UI, and that's why we can't see the other screens, right? And so if we have this raycast target on and I click, this particular fader image will block that raycast and it won't hit the buttons. So that's why we turn off the raycast, so now the raycast just goes through this particular fader. All right, so now we are ready to go. That was super easy. And now we have global fading working. Super awesome, okay? So I'm gonna leave you guys there. And in the next lesson, we are going to set up the animator and we're gonna start to get these guys to transition between one another. We're almost there, so hang in there. I'll see you guys in the next lecture. Thanks so much.


Hi, I hereby invite you to take part in our free, massive, open online course, Linguistics 201, The Structure of English. The class for everyone who is interested in English language studies, in linguistics or in both. The content of this class has been developed on the basis of the comprehensive grammar of the English language, written by Sir Randolph Quirk and his colleagues, Sydney Greenbaum, Jan Svartvik and Jeffrey Leach. It is thus a class equivalent to many traditional grammar classes offered within teacher and BA programs with a focus on present day English. If you want to become an English teacher. So if you are in any of these scenarios, the content of this class is an indispensable backbone of your teaching efforts, even if you are already on such a job. Well and if you are on a BA program, you need such a class to set the basis for further classes in syntax. Well and for both and for all other participants, we hope to have made the class as interesting as possible. With two steps, you can access your class. First you should create your free account on the Virtual Linguistics Compass. Well and then, and for many of you this may already apply, they have an account on the VLC, you can log in with your personal data and then you have two options. You can either register for the Massive Open Online Course Linguistics 201 via the Registration Office or you will, if you have already registered, you will straightforwardly see your class on your personal VLC start site. Well and here, a simple mouse click suffices in order to lead you to the interactive class start site. Here it is, this is what it looks like. On the class start site, you have access to some class related links and you will find a hyperlink to the message board, the forum where you can discuss problems with your classmates. The core of the class are the 13 units where the first one, the class preliminaries, informs you about some general aspects of the class and about the principles of studying online on the Virtual Linguistics Campus. In addition to this, you will find helpful routines that find out whether your computer is configured correctly and whether the submission of worksheets, the backbone of our testing principles is successful. For this reason, we recommend to go through the class preliminaries very carefully, especially if you're a first time user of a class on the Virtual Linguistics Campus. The remaining units exhibit the content of this class and if you wish, you can retrieve an overview of each unit by means of a click on one of these information symbols and this even works if you're not logged in to the Virtual Linguistics Campus. So you can actually see what's in a unit of a particular class. Let us now look at the content of the class traditionally referred to as the class syllabus. The class is subdivided into three parts. After an overview of the main principles of grammar and the analysis of English sentences with its main categories and functions, we will discuss the central syntactic categories and functions of present-day English in detail. That is, we will look at verbs and nouns, their integration into phrasal categories and their use within elements of clause structure. These units constitute the core of the class. The final unit is an extra, something which is often neglected in such a class, the orthography of present-day English. I'm sure you like it because it also includes a discussion of hyphenation and punctuation. Even though many of you might not be first time VLC users, let me now nevertheless explain what it means to study online on the Virtual Linguistics Campus. Well, it simply means go through each e-learning unit at your own pace. Study the virtual session. Use the guiding questions which you always find in the main menu of a virtual session. Watch the corresponding e-lecture videos which you can also independently access via our YouTube channel and optionally use the tests in the interactive tutor, which you also find in the main menu, to check your knowledge. Well, and if you wish, you can discuss further problems using the class-related forum, which is open for all registered users of the class. Finally, you might want to know what you have to do in order to get a qualified class certificate. Well, certification is simple. As in all our massive open online courses, all participants will receive a certificate for regular and active participation, the so-called statement of participation issued by the Virtual Linguistics Campus. You have to regularly participate and you have to submit a few worksheets. If you want more, you have to invest more work and maybe even a small amount of money. But what you will be given in such a case is something special and unique in the world of massive open online courses. For a small fee of 45 euros, you will receive this qualified statement of accomplishment issued by Marburg University in conjunction with our Virtual Linguistics Campus. This certificate will include your precise grade and a complete list of all the tasks you successfully submitted, including the workload of the class. To be given such a certificate, we want you to do the worksheets. One for each unit. We want you to regularly participate and if the overall score of all the worksheets is 60%, you will be given the option of either receiving a standard statement of accomplishment or if you want to pay this small amount of money, you will be given this specified statement of accomplishment, a graded statement of accomplishment and the choice can be taken on the class start side. It's a simple mouse click. And you can take this choice during the entire activation period of the class using, as I said, this option on the class start side. And if you change your mind, you can always reset your decision. So these are the three options. Statement of participation, statement of accomplishment and graded statement of accomplishment for a small amount of money. So we invite you to join us in our linguistics 201 massive open online course, the structure of English. As soon as the class has been activated, you will be contacted by the VLC team. The rest is up to you. So on behalf of my team, let me thank you for your attention and enjoy your class.