professor of journalism at the University of Rhode Island where I'm also a documentary filmmaker. My recent film series called Can We Talk takes a look at the problem of low representation of underrepresented people of color and STEM. And so I'm really thrilled to be able to hear insights from the humanities and the arts on how to discuss this issue and explore different ways of understanding the scientific world and scientific problems. I will be joined today with Shalini Kattaya and I'm Sharon McGrane and Yo-Yo Ma. So we will begin with Shalini Kattaya. She is a film director, producer, and creative director from Seventh Empire Media. Her film Code Advias. Code Advias, if you haven't seen this film, please, please watch this film. It is such an important contribution to the topic of AI, but specifically around algorithmic oppression, as we call it. So I use this film in my classroom and I'm so really happy that there is this particular contribution on this topic. Also, the film premiered at the 2020 Sundance Film Festival to critical acclaim and was broadcast nationally on PBS. And such a fantastic and important contribution also was awarded the CIMA Award for Best Director. It has been nominated for Critics' Choice Cinema Eye Honors and NAACP Image Award. Shalini's debut film Catching the Sun, also a beautiful film, premiered at the LA Film Festival and was named a New York Critics pick. She directed the season finale of the National Geographic television series Breakthrough, which was executive produced by Ron Howard and broadcast globally in 2017. She's also directed for NOVA and YouTube Originals. Shalini is a TED Fellow of William J. Fulbright Scholar and an Associate of the UC Berkeley Graduate School of Journalism. So welcome, welcome Shalini. Thank you so much for having me. It's such an honor to be here. I want to thank the National Science Foundation and Kendall for that incredible contribution. And it's such an honor to be here in dialogue with the amazing Yo-Yo Ma. And so thanks so much for having me. I am delighted to talk just a little bit about my journey and the importance of sort of inclusion in STEM. I sort of came to this as an artist misfit. I came, I was born into a family of scientists who are immigrants to the United States and sort of science was thought of as sort of a very practical field. And so when I told my mother, I wanted to be a filmmaker. She said, movies, movies, how will you eat? And it was thought that, you know, the arts was something very sort of frivolous. And for a long time, I felt a lot of guilt coming from an immigrant family being raised by a single mom and taking up this very impractical passion, which was filmmaking. And I think because I, you know, wasn't raised with a good deal of privilege. And I think when you don't have entitlement, you have the opposite, you're always asking permission to do things. And I felt that I had to yield my abilities as a filmmaker. I was always sort of aware of the tremendous privilege of being a filmmaker. And I felt that I had to do something important with my work. And I think coming from a community that is underrepresented in film, I was always aware as a filmmaker that I didn't have the privilege of just representing me, that I was actually bringing a lot of other communities into the room. And I felt that as a tremendous responsibility. That being said, you know, just growing up around science, I love science and I love technology. I read Wired religiously. I believe in technology's power to transform the world. I'm sort of obsessed with the stuff. And very much interested in how technology, disruptive technology intersects equality between people, you know, whether it makes the world more fair or less fair, and for whom. And I think three years ago, I didn't even know what an algorithm was. Everything that I had known about artificial intelligence had sort of come through the imagination of Stanley Kubrick or Steven Spielberg. I'm definitely someone who's watched Blade Runner way too many times. And I stumbled down the rabbit hole when I read a book called Weapons of Math Destruction by Kathy O'Neill. And subsequently began to realize the ways in which artificial intelligence and algorithms, machine learning are increasingly becoming almost a gatekeeper of opportunity, deciding really important things like who gets hired, who gets healthcare, how long a prison sentence someone may serve. And at the same time as I was learning about the ways in which we as human beings are outsourcing our decision making to machines in ways that really change lives and shape human destinies, I came across a TED talk that Joy Bell and Weenie gave and was sort of shook out of my seat that these same technologies that we are putting our blind faith in have not been vetted for racial bias or for gender bias or even more broadly that they won't hurt people, that they won't discriminate, that they won't cause harm. And that's when I really began to understand that everything that I love as a free person living in a democracy, be it access to fair and accurate information or fair elections or civil rights is rapidly being transformed by artificial intelligence. And quite what so many of the scholars articulate in coded bias is what I began to realize that we could really set back 50 years of civil rights advances in the name of trusting these algorithms to be fair and to be just and to be neutral when in many cases that's just not a fact. And that sort of set me on the journey to make this film. And what I want to say is that as an artist without degrees, advanced degrees in mathematics or data science, I was terrified. And I had every bit of imposter syndrome. And I thought that I was, I feared that I would use terminology improperly and everyone would laugh at me. And that would be the end of that. And just the opposite happened. Every major technology company has screened coded bias. Since the making of the film, IBM and Microsoft have both changed their policy around selling racially biased facial recognition to law enforcement. Facebook has basically deleted a billion face prints. And so what I began to understand is that with an issue like algorithmic justice or climate change or the pandemic that we're living in, that there's a real crisis of science communication. And that science can't just be something that is for PhDs, but that we the people actually need science communication that translates to us so that we can make informed decisions. And as an artist, what I realized was my gift was the human heart. Roger Ebert says that films are empathy machines and they make you feel for someone, you know, a world away from you. And it's my belief as a filmmaker that it's through that radical empathy, through feeling something for someone that you don't know that that is the spark of social change that we act not because we do not know, but we act because we do not feel. And to me, that is the marriage of art and science that is so powerful. And so I learned that that my sort of gift was not just being able to understand and trying to sit with some of the most brilliant people I've ever met in my life, advanced degrees and PhDs from Harvard and MIT and the like, and being able to try to communicate that to the public, but also to connect that science communication to the so what, to the people down the street who have skin in the game, and whose lives are impacted by this science. And so what I began to realize is that all the things that made me feel insecure and like I didn't deserve a place at the table was exactly why I needed to be sitting at the table. And in the making of the film, I saw three black women, all of whom were graduate students at the time find racial bias in commercially available technology that was already being sold to the FBI, already being sold to law enforcement, that somehow Microsoft, IBM, and Amazon missed. And if that is not a rally cry for inclusion, I don't know what is. And one of the things that I've grown in compassion for in the making of coded bias is our own limitation and our own human bias. And that that is an inherent inherent condition. And that means diversity and inclusion isn't just something that looks good for the pictures. But it's actually absolutely essential in developing innovative technologies and solving problems in the 21st century. So I'll just leave it there. Thank you so much. And I'm so happy that you've introduced the conversation on science communication because I believe that the next speaker is also opening doors around science communication as well.


Hi friends, welcome back to my channel and welcome back to another weekly WW grocery haul. This week, I hit up Trader Joe's and Fred Meyer, which is a Kroger store. I had quite a bit to get. I am so excited about what is on my meal plan and what I'm having for breakfast lunch. Everything fun is coming your way, including the first episode in my fall baking series. So if you wanna see what groceries I picked up for the upcoming week, then just stay tuned. So here is my haul from Fred Meyer, which is also a Kroger store. Next up will be what I picked up from Trader Joe's. So first I grabbed some of the Fresh Gourmet Wonton Strips. This is a for and upcoming lunch for meal prep. I'm really excited about my lunch this week and I needed the Wonton Strips for that. Surprisingly, these are better in points than I thought. So really excited to add those for that crunch factor to my salad. Some sourdough bread, just regular full-size slices of bread. Primarily for my husband, he's gonna be doing egg salad for lunches this next week.  And then of course I picked up my very favorite happy eggs for myself for the week and then also for him if he wanted eggs throughout the week as well. Sesame seeds, this is for my upcoming lunch recipe. A can of cream of chicken soup, this is for a dinner recipe. Again, the healthy request is better tasting than the 98% fat free and is the same points if not less. So highly recommend the healthy request versus the 98% fat free. Also, I picked this banana extract up and the only reason that I grabbed this is because look, 50 cents on clearance. So I've been thinking about maybe making a banana protein shake and I thought that this would add some delicious flavor with my devotion powder. So grab that just to have on hand. I had to buy one, get one free coupon for the Talking Rain. This is just your unsweetened flavored seltzer water. So I grabbed grapefruit and peach nectar. I just like to take these with me when I'm out on the road, if I'm traveling to houses or to show clients homes, this is a huge bottle so it lasts me the entire day and it's really, really good. Also some Kraft Parmesan and Romano, what I call grated shaky cheese. We have this for an upcoming recipe. We were completely out. So I really like the Kraft. I think it's the best brand. It's a little more expensive than your generic but I think the flavor is a lot better. So I picked that up. Also some mandarin oranges, no sugar added. I need this also for my lunch meal prep. I seriously cannot wait for my lunches. So I just grabbed this instead of a can only because the canned ones are generally in juice and this is not. So I'll just use these little cups as my mandarin oranges. Also some 90 calorie Pillsbury Crescent Rolls. This is for my breakfast meal prep. Again, can't wait, this is gonna be a great, another great week of meal prep. So I needed the 90 calorie Crescent Rolls for that. Jimmie Dean fully cooked turkey sausage crumbles. This is also for my breakfast meal prep. So I'll be sharing my meal plan and prep. Ideas with you guys at the end of the video. So definitely stay tuned for that. Per the use, a can of the fat free ready whip. I still have about a half a can but I don't ever wanna run out. So I went ahead and picked up another can. Also for my almond milk this week, I grabbed the unsweetened vanilla almond breeze almond cashew creamy. This milk is so thick and creamy and delicious. I generally will add this to my coffee. Sometimes I use it in protein shakes. So I really like this one. So I grabbed that and then I was so excited. I could hardly contain myself. That Fred Meyer now is carrying the sugar-free peppermint mocha creamer. I bought every single one they had, which is four. Tip on this, you guys, you can freeze these. So I plan on loading up my freezer with these to make it through at least half of this next year because this is my all time favorite coffee creamer in the entire world. I love it so much, it's so good. So I picked up four of those and then of course next week, if they have more, I'll be grabbing some more of those. And again, throw them in your freezer. That way you have them on hand for the entire year. Also some apple juice. Primarily this is for my husband, but I do need this for an upcoming recipe. Just a small amount of it. So he'll drink the rest of it. And then the rest is produce or vegetables. So I have three bags of the Kroger California style veggies. So this is broccoli, carrots and cauliflower. I need two bags for a recipe and then I picked up a third bag just to have on hand for a side dish. And also the Kroger mixed veggies. Again, just for a side dish for a vegetable. They have their peaches and nectarines on sale. Oh, that one doesn't look that great, does it? But anyways, they have their peaches and nectarines on sale for 99 cents a pound. So I picked up six that way I can have one every single day. Two green apples for my lunch prep and an orange.  My very first episode will be posted shortly. And actually I forgot, I didn't need these green apples for that and the orange. So I can't wait to share my first fall baking series recipe with you guys. So that is everything other than two cases of Diet Coke and a case of water that I picked up at Fred Meyer. So let's move into Trader Joe's. And here is what I picked up for the week at Trader Joe's. Mainly went there for produce because I love their produce, their prices are outstanding. So here's what I grabbed. So of course, hence fall, I picked up some fall colored mums. You get this entire bouquet for 3.99. You cannot beat the price on flowers at Trader Joe's. So I'm gonna swap out my summary ones that are kind of on their last leg for these fall colored mums. Really excited. Also, I grabbed another bag of the Trader Joe's Light Mexican Blend. I need this for several recipes for the upcoming week. Really good points. Only two smart points per one ounce or quarter cup versus three of other light cheeses. So really good deal if you have a Trader Joe's near you. Same with the Trader Joe's Organic Shredded Mozzarella. This cheese is pricey. It is $5 for an eight ounce bag, but it is a great dupe for the light shredded mozzarella because it is only two points per one ounce. And I know the light shredded was one, but this cheese is really, really good. And again, it's only two points per ounce or quarter of a cup. Also some light string cheese. I need this for an upcoming air fryer recipe as well as for my lunches and just as a snack. Really, really good. And only one smart point per stick. Two more pounds of my Extra Lean 96.4 Trader Joe's Ground Beef, my favorite ground beef. Hands down, great price, $5.49 a pound, which in my area is a fantastic price for 96.4 Extra Lean Ground Beef. Also for my lunches this week, I picked up another package of the Trader Joe's Just Chicken Strips. Literally, this is just frozen grilled chicken. It tastes really good. So I'm gonna be using this on my salads for my lunches for the upcoming week. Speaking of salads, I did grab quite a bit of lettuce. So I picked up this champs. I don't know how you pronounce that, but it is green leaf lettuce, frise, radicchio, and carrots. And I'm gonna mix that with the organic power greens, which is organic baby kale, chard, and spinach. And then I also picked up a pack of the organic's romaine. So I'm gonna be mixing all three of these together for my salads for lunches.  This is a three pound bag. I need potatoes in a couple of recipes this next week. So great deal, like less than $3 for this bag at Trader Joe's. My favorite Brussels sprouts. From the frozen section, 99 cents a bag. This is a one pound package for a dollar. And these baby Brussels sprouts are so good. So I grabbed two of those. Unsalted, dry toasted sliced almonds. This is for my salads. I'm really excited. So I was wavering between these or the regular sliced almonds. And I thought, why not get them already toasted? Save me a step, same price, little bit better flavor, same points. So I grabbed that at Trader Joe's. Also some asparagus. I need this for an upcoming recipe. Asparagus is about out of season, I feel like. So it's looking pretty good still. So wanted to do this recipe while the asparagus was still good.  So this is just a big bag of lemons. I've been really enjoying lemon water. Kind of a new thing for me, but I've been loving it. I use one lemon a day basically in several cups of water. So I grabbed that. Also, I picked up this Anderson Organics Sweet Onions.  To be honest, this was a better price than regular onions. So if I were to just grab onions bulk, it would have been a lot more expensive. This entire bag of organic onions was only $2.79. So this will definitely last me a while. And honestly, I prefer the smaller onions anyways. I feel like I can just use a whole onion in a recipe instead of trying to preserve it and put it in the fridge and hope it doesn't go bad for a second recipe. So excited about that. And the last hurrah in watermelon. I think this is about the end of watermelon season. So I picked up this big watermelon $3.99 at Trader Joe's. So that's everything that I grabbed at Trader Joe's for the week. So let's get into my meal plan and what's on the plan for prep. So let's go over what is on the plan for meals for the upcoming week. This is my absolutely beautiful Carrie L meal planner. As you know, I love, love, love it. So it is the eight and a half by 11 size of the meal planner. It's gorgeous. It's comprehensive. You have several tabs to help you stay on track and plan your meals. So first there is the resources tab. This is where you can jot down your favorite meal planning blogs and recipe sites just to kind of have everything in one spot for you. There's also the inventory section. I'm excited about this. I actually just reorganized my pantry fridge and freezer yesterday. So next on my list is to inventory those items. So there is a spot for freezer inventory, pantry inventory, and oops, I skipped past it here and spices inventory. And this is nice because spices expire sometimes before we have the opportunity to use them. And this is gonna be a great tool for you to track the expiration date. Also we'll come back to the meal plans tab. There is the gatherings tab. That is where you can plan out your gatherings. So if you have some sort of an event, especially with the holidays coming up, Thanksgiving, Christmas, New Year's, you can plan out what is on your meal plan for that event. And then there's also a detachable grocery list that you can take with you to the grocery store. So it just makes planning your life a lot easier. And last before we hit the meal plans tab is the notes tab. And that is just where you can jot down notes. You could even make lists and things on this section as well. But I generally use this as an area to write recipes down that I don't have printed or screen shot in. I do wanna show you really quickly in the back of your meal planner. You have these great little folders. There's one on each side. And that allows you just to store printables and things like that in your meal planner. So let's go back to the meal plan tab because that's actually where we're gonna spend a little bit of time. Let's fast forward to the current month. I cannot believe you guys, it's September already, crazy to me. So September, you have by a glance the monthly meal planner. This is so nice. It allows you to jot down what you are having for breakfast, lunch, dinner, however you wanna use it. You can see it at a glance. You'll know what's on your plan for the following day. You can make sure that you not only have the ingredients on hand, but you also have the meat pulled out of the freezer. So I love the monthly planner, love it. So for the week, you have what's called a weekly planner. So you have a weekly menu day by day where you can plan out your meals. I generally just use this for dinner because as you know, I meal prep so I don't prepare separate breakfast and lunches each day. And then the detachable grocery list, that is something that I always, always, always use. Each week is the grocery list. So let's move into this week. So my week goes Saturday through Friday. You can see the grocery list is gone because I did use it this morning at the grocery store. So Saturday is the day that I use my weekly. So I always go out to some sort of a meal, breakfast, lunch, or dinner. So I don't actually plan to prepare a dinner for that night. Tomorrow, I'm gonna be making sheet pan steakhouse dinner. I'm really excited to share this with you guys. You will see this in my upcoming What I Eat in a Day video. Monday is a rollover recipe from last week that I didn't quite get to. Just had a kind of a crazy week. If you haven't checked out my weigh-in video that I posted last Friday, definitely check that out. Or yesterday, I should say. Definitely check that out because I talk about how crazy my week was. So I'm gonna be coming back at the chicken enchiladas skillet with vegetables. Tuesday, I'm making meatloaf, potatoes, and veggies, kind of that home-cooked, hearty meal on Tuesday. Wednesday is a one-pot chicken dinner. This is a 30-minute meal, you guys. Sounds amazing, can't wait. Thursday, I'm gonna be in my air fryer making homemade mozzarella sticks. Yes, please. And we're gonna air fry some chicken as well. And then Friday is going to be leftovers. So on the back of the actual weekly menu is where I will plan my meal prep. So I prep every week breakfast, lunch, and some sort of a snack. So really excited for this week. So for breakfast this week, I'm going to be having comfort food breakfast bake. The weather is changing. I'm definitely feeling a little bit more the comfort food vibe. So I can't wait to share that with you guys. For lunches, I'm gonna be doing the Copycat Panera Asian Chicken Salad. I cannot wait for this. I've been craving salads again. I'm in the salad mood. So I'm gonna be doing this and I'm gonna top it with some chicken. So it's going to be a full comprehensive, low-point, delicious, healthy lunch. And I'm gonna be making homemade pumpkin pudding. Just to have as a dessert one night, I believe it makes four servings. So we actually can have it for a couple of nights. Super low points and we're getting into pumpkin season. And also I am planning on doing my first fall baking series. I'm not gonna share with you the recipe, it's a surprise. But that is what's on my meal plan for the week. Again, this is my Carrie L. Meal Planner. If you are interested in picking up this meal planner, my 10% off discount code is here on the screen. Click the link down in the description box. Enter the code. You'll get 10% off. Highly recommend. It is definitely a way to stay on track to plan your meals and it just helps you in your weight loss journey in general. So definitely recommend the Carrie L. Thank you for joining me on this week's WW grocery haul. I hope you enjoyed seeing all of the good stuff that I picked up for the upcoming week, my meal plan, what I'm doing for meal prep. And of course I can't wait to share with you my first ever 2019 fall baking series video. I am not gonna give away what we're making, but I can't wait, it sounds amazing. The smart points are great. So I can't wait to share that with you coming shortly. You'll be seeing tons of fall baking, Halloween baking, Christmas baking. We do it all around here. So if you are new to my channel, make sure that you hit that subscribe button and that little bell. That way you'll be notified every time I upload. You don't wanna miss a single video, especially with the holidays and vlog miss right around the corner. I'd love it if you thumbs up this one. Comment down below. Let me know what you thought of this week's grocery haul and what on my meal plan or meal prep are you most excited about? And I'll see you guys all in my next video. Bye.


So, we started with the Slater determinant as a basis for the n electron systems because Slater determinants by definitions are anti-symmetric. So, we said that in the last class that Slater determinant are anti-symmetric by definition. So, they are valid wave function for the n electron problem. The question is whether they are exact or not. They may not be exact wave function but they are valid representation of at least an approximate wave function. And we discussed of course the case A where the Hamiltonian was non-interacting which means it is sum of just some one particle operator. In that case, a Slater determinant is an exact wave function and this is something that we discussed in the last class that the product if it is a non-interacting product of one electron operators is a valid Eigen function. However, the product is not an acceptable Eigen function so we made it determinant. Please remember that determinant is anti-symmetric. With anti-symmetry is the most important rule. The first rule is anti-symmetric. So, anti-symmetry has to be followed then all other things can come. So, if h is non-interacting of course Slater determinant is exact function. What we will discuss today is what happens when h is interacting and that is the case for our atoms and molecules and we have discussed it. Why? Because that Coulomb operator which is sum over 1 by Rij that cannot be written as a sum of one electron operators. So, we discussed that mathematically it is not possible to write. So, that is the reason. So, essentially interacting Hamiltonians are of the form which are of this form. So, we have a sum of one electron operators and sum of two electron operators for the Coulomb problem, for the atomic and molecular problem that we are discussing. This Hamiltonian contains of course kinetic energy of the electrons and all other external potentials which means electron nuclear attraction energy in particular for atoms and for molecules also electron with various nuclei not one nuclei for molecule. So, that there of course we invoke what is called the Born-Oppenheimer approximation. We will come back to that, but let us assume we have already done those we have already done Born-Oppenheimer we will know. So, for molecule of course we always discuss this under the Born-Oppenheimer approximation and we have the form of this Hamiltonian. So, then the question is what do you do? So, S-Later determinant is of course not an exact wave function there because I cannot write this as a product because of this curve. So, we have to find a way to write this. So, this is where I will again go back to a simple two electron wave function and show what I now call the form of the exact wave function and we will actually tell that an exact wave function form is certainly possible. However, it is in principle or in practice never implemented and that is the reason we say that approximate methods have to be developed, but there is a form of an exact wave function for interacting problem. So, we are talking of this case B, we are talking of the case B where Hamiltonian is interacting and we would like to show what would be the form of the exact wave function in that case. So, that is what we will try to do now. So, let us take a two electron problem it is always good to have a two electron problem. So psi is 1, 2 the Hamiltonian is H of 1 plus H of 2 plus 1 by R 1 a convenient example is a hydrogen molecule in Born-Penheimer approximation or helium atom. So, depending on what is your H, so external potential is only going to be different. So, this is my Hamiltonian and this is the wave function the wave function has of course coordinates which are I have repeated again not just space coordinates, but space and spin coordinates. So, I repeat that we have defined what is called the space and spin coordinate x 1 which is equal to R 1 and the spin coordinate x 2 which is R 2 and spin coordinate. So, omega 1 and omega 2 are spin coordinate and our spin functions are alpha omega and beta omega. So, I think it makes it very clear just like our space orbital functions are phi of R our spin functions are only 2 alpha and beta, but they also depend on coordinates those coordinates are what I am calling omega just to give a name. So, x is a composite coordinate of R and omega and I repeat that this is four dimensional because R is three dimensional. So, if you want to be more correct then you should write this as R vector and the x 1 and x 2 each of them is four dimensional. So, whenever I am writing 1 and 2 please remember they are actually x 1 and x 2. So, this is actually an eight dimensional function 4 for 1, 4 for 2. So, because the anti-symmetry must always include the spin. So, now we define what would be my psi of 1, 2 in principle without even solving this Schrodinger equation without even trying to solve the Schrodinger equation. We first note that any function for example, if it is a function of phi of x can always be written as a linear combination of a complete set of functions, this is a complete set of functions f of i. I can always write any one electron function as a linear combination of a complete set of one electron function. I hope this is a mathematics that everybody knows. For example, if you have sin x, cos x, exponential x, everything can be written in powers of x. Powers of x, x to the power 0, x to the power 1, x to the power 2 etc for a linearly independent complete basis. So, you can always write in terms of such a function. If you know this, then let us try to analyze this function first as a one electron function. How can I do that? It is a two electron function. It will be one electron function provided one of the coordinates is constant. So, I am only looking at it mathematically. Let us first look at x1 as a constant. For a given value of x1, the psi now depends only on x2. Then of course I will change x1. It is like writing an f of xy in curves as a function of y for different values of x. You have done that in chemistry or mathematics many times. I will try to do that. I actually say that for a given value of x1, it is only a function of 2. Let us assume that I have a basis of 2 which is the basis of chi i of x which is a basis of spin orbital. Of course, it must be a basis of spin orbital. So, this is my basis. So, do not forget this and this is the basis that we had introduced. What is this basis? This requires an orbital times alpha and beta omega. So, again I repeat the chi i of x would be some phi k of r times alpha omega or beta omega. So, that is my chi i of x because x is four dimensional function. I have a space part which is like hydrogenic atom maybe and alpha and beta part. I assume that I have a set of functions which is complete. I have a basis which is complete which is the basis of chi i. Then if I want to write psi of 1, 2, how will I write? I will write exactly as a function of 2 keeping x1 constant. So, this now becomes chi i of x2, multiplied by some coefficient. Let us assume that this is now a one electron function depending only on the coordinate of the electron 2 which means x1 is constant. So, for a given value of x1, this is no longer a function of x1, x2, it is only a function of x2. So, I am writing that as a linear combination of chi i of x2. However, if I want to write the full function, I have to change x1. Every time x1 changes, the form remains the same, the coefficients will only change. For another value of x1, it is again the same form. But what will happen? The coefficients will change because it is a different function, is it clear? Like sin x cos x, everything I can write in terms of x to the power n but the coefficients will be different. So, similarly for another value of x1, I will still have the same form but the coefficients will be different. Hence, I can say that I can write this as a chi i which is a function of x1 times chi i of x2 because now chi i will change as a x1. So, chi i will have a functional form of x1. I hope you understand. So, basically I am writing, so let us say I have a two dimensional function x1 and x2. So, this is my x2, this is my psi of x1, x2. I write this as a function for a given value of x1. Then another value of x11, let us say this is x10, x11 and so on, so functions keep on changing. Each of the function can be expanded in this form. So, in doing this, what is the difference? The difference will be only linear combination coefficient. When I expand two different functions in the same basis, only thing that will change is this. So, this is changing because x1 is changing. So eventually I can say that this chi is a function of x1. Since now chi is a function of x1, what I am going to do is to expand the chi of x1 again in the same basis. So, I am trying to write for a given chi of x1, I am going to write an expansion in the same basis, let us say dji chi j of x1. Note that for each i I am expanding, so I have to bring another dummy index which cannot be i, which has to be some other dummy index j, so this is the specific i and the dummy index is j. So, dji, actually it is dji chi j but dji depends on i, so I am keeping track of which i. So, I am writing dji of dji, so this becomes a two dimensional matrix and that is now a number. This is now a number. So if I put this here, then this will become sum over j sum over i dji chi j of x1 into chi i of x2, chi i of x2 is as it is there, the dji is now another constant. This constant depends on of course j is expansion of ci, so it is like writing c of x1, how would you write dji chi j, right? So instead of dji, since it is for a different i, I am keeping track of which i, so for every i dji will be different, ci is a number but it is a function of x1. I told you, number means what, for a given value of x1 it is a number. What is the number and a function? f of x for a given value of x is a number, correct? So I told you that I am expanding this for a first for a given value of x1, so then it was a number, but if I keep changing x1, this number keeps on changing, so that is a function. So that again I can expand like this by the same basis but now my chi j will depend on x1 because I am expanding ci of x1. So eventually my psi x1 x2 will be a double summation of j and i dji and a product of chi j and chi i. Is it clear? This is a very important part to understand because you can easily generalize this for n electron now, for n function. So if you understand this, then I can write x1, x2, etc, xn. How many now summation will be there? I have two summations here, n. Is it clear to everybody? Because first what I will do, I will expand in terms of xn keeping x1 to xn minus 1 free fix, then I will expand that in terms of xn minus 1 keeping up to xn minus 2 free and keep doing it recursively. If you see for every expansion, I will have one dummy index. So I have a dummy index which will be like i, j, k, l, etc, etc, n times and I will have a coefficient d, l, k, etc, j, i, whatever and then you can write this as chi, l, xn, chi, k, xn minus 1 and so on up to chi, i of x1. It depends on how you write it, does not matter. So I can keep expanding this as you can see the way I have written it here. If you want to keep the same i, then this should be i, j. So I can change this around. So this should be i, this should be j and so on, this should be whatever is the last one chi, l of x1. It does not matter. Those are all dummy indices. So it is a immaterial. The point that I am trying to say that for the n function, it should have product of these basis functions n times. There should be n number here and these coefficients will have, these are all numbers but this is now not a number, a number which depends on all these n, n indices just like here dji. So if you call these two dimensional matrix, this will be a much larger dimension matrix. So it will have all different numbers. So eventually what is important is that this is a product of n basis and if you remember one of this product was what I called an exact wave function for a non-interacting Hamiltonian. In fact, for a non-interacting Hamiltonian any one product would be exact function because the wave function is a product of the spin orbitals which are Eigen function of the H. I can take this as a basis for example. So it could be made as a exact wave function but for interacting Hamiltonian, I have to take all combinations of this product. So that is the main difference, not one product. Here one product was an exact wave function, here I have to take a combination of products. I hope you are under linear combination of products. So I have a basis functions chi i. So from this chi i, I continue to multiply. Remember number of basis function is much more than capital N. So I can keep on multiplying. It is not that I have the only one product. If of course you have n functions and n electron problem, you will ask me what can I do? Chi 1, chi 2, chi 3 etc chi n. But also that this wave function must be anti-symmetric. So what do I do? Every product I make a determinant just like here. If every product is a determinant from our discussion, please note that no two spin orbitals can be identical. So if I have n basis and n electrons, how many product I can write? 31. Is it clear? Because each of them has to be different and I have to write n product and I have only n basis functions. Let us say I have only chi 1, chi 2, chi 3 to chi n. I can only write a product which is chi 1 times chi 2 times chi 3 times chi n. And then any interchange does not make a new one because it is a determinant. It will only change sign. However, if I have a large number of basis which is capital M which is much greater than n, then how many products can I write? So let us say I write as a chi 1 into chi 2, one function, two electron problem. So chi 1, 1, chi 2, 2, forget about some plus, plus or minus, chi 2, 2, chi 2, 1, chi 3, 2 and so on. You can write but each of them is not a symmetry, how can I anti-symmetrize? You cannot anti-symmetrize between them because each product contains difference between orbitals. One case it has chi 1, chi 2, one case it has chi 2, chi 3, then it is not a separate function. Then it is the same function you are talking about. See, initially I have n product out of M. So this product can be any one of them. Each of them can go up to 1 to M. So it is m into m into m into m, so m to the power n. How many terms will be there? If each of them goes 1 to m, 1, 2 to m, 3 to m, it will be m to the power n but that many number is not allowed because that violates symmetry. So to make it anti-symmetric, we have to make determinant. So now I am asking you how many determinants you can make if you have a basis of m for an n electron problem, mcn, is it clear to everybody? Because all you have to take is a different sets of n from a basis of m. So you pick up any n out of m which are different. So none of them can be same because it is a determinant. Without determinant I will not proceed because I am repeating anti-symmetric governing principle. So my basis must be an anti-symmetric basis. So my basis is no longer just the product but a determinant. So I will have to write the wave function in terms of all possible determinants that I can generate, n electron determinants that I can generate out of an m basis which is mcn. So I will write them now. This is just the combination coefficient for each determinant and I am just keeping track it does not matter. So eventually let me write down if h is interacting and I have a basis of m spin orbitals and let us assume that it is a complete basis, then exact wave function for interacting Hamiltonian can also be written. I was always telling that it is not possible to write. It can be mathematically written as a linear combination of all n electron determinants that can be formed from the m basis. So this is the statement that we are making that I am given a basis of m spin orbitals. An exact wave function can be written as a linear combination just as I am writing as a linear combination of a determinant. Of course the combinations will not be all i, all j, all k, all l because I know that I cannot be equal to j. So you have to order them and there is a way to write how to order them. I will write for a specific 2 electron, 3 electron and so on how to write this running coefficients, running indices but that is very easy. In physics it means you have to find out all possible n electron determinants that you can form out of the m basis and that number, this number is mcn. The number of n electron determinants that you can form out of m basis is mcn and finally your wave function is just a linear combination of this mcn number of determinants. I will give examples to further elaborate this. So this is an exact form. However, before I go ahead with the example let me tell you that this is not really in principle or in practice it can be implemented simply because you will never get a complete basis first of all. A complete basis is usually an infinite dimensional basis. So the problem will simply become infinite by infinite problem. The number of determinants that you will require is infinite in number and it will never reach the exact wave function. So that is the real problem of writing this but mathematically a form exists which however is really useless but I think it is a good starting point to understand because now we can make approximations from here to cut down you know how to choose a basis and lots of questions will come up. If we choose a good basis maybe you will at least get closer to the exact very quickly. So let me just to elaborate this let me take example of another again a 2 electron problem and write a determinant. So let us say that I have 3 spin orbitals so I am making it as simple as possible. I have 3 spin orbitals which is my basis chi 1, chi 2, chi 3 obviously it is not a complete basis. But let us assume that how can I write the determinant chi 1, chi 2, chi 3. So to show the problem that you are generating. So what will be chi 1, chi 1, 2? Now chi 1, 2 will be determinant of chi 1, chi 2. I hope once I write this everybody can write this letter determinant. I have already shown this in last class row and column so it is a full form with a normalization constant but that is not really important.  Then I have another one which is d 1, 3 chi 1, chi 3 right and one more will be there what is it d 2, 3 chi 2, chi 3 is it clear that this is all that I will have. If I have a 3 basis it is like taking 2 out of 3, any 2 out of 3 from my basis of chi 1, chi 2, chi 3. If I have chi 1, chi 2 I cannot have chi 2, chi 1 because it is negative of itself. I cannot have chi 1, chi 3, chi 1 I cannot have chi 3, chi 2 of course each one is not there. So instead of 9 if you had all summation 1, 2, 3 instead of 3 square 9 I have only 3. And that is your MCN 3C2. If you have 4 orbital you will have 6 so it is very easy to do this calculation and these are the coefficients which I am just keeping track in some way. So in general if you look at these coefficients however written I have written orbitals which are ordered chi first one is less than the second one or greater than one of the order you follow. So I can write this as I less than j same thing I can write the d ij chi i chi j so that is a very convenient way of writing. So this is now exact the same thing you understand the same thing I have now written at d ij chi i chi j but d ij this ij can be changed to some other index also. I can give an index for example I can call this 1, 2, 3 it does not matter right now I am just keeping track of the orbitals but later on I need not because all it all it just for a minute all it means these are numbers and eventually these numbers have to be found out.  Slater determinant is the only basis yeah the point that I was trying to tell you if you have a non interacting Hamiltonian then each of them is an exact wave function one of them will be ground state one will be an excited state but each of them is an exact wave function because it is a product chi 1, chi 2, chi 3 if I choose chi 1, chi 2, chi 3 as an eigen function of the h and if my Hamiltonian is only h of i and this was not there then each of this would be a exact function. So I can trivially do an exact function by single determinant by ensuring however that the basis is an eigen function of this one electron operator that is important if the basis is something else then you will still not get then you still have to expand yes because each of them is anti symmetry this become minus it is not required it is not required no no 1 and 2 means you are you are so now what what you are doing that you make this chi 2, chi 3 this you make chi 1, chi 3 I did not understand no no no they have a they have an anti symmetry in the wave function in this in this d. So the coefficients will make sure when you do a full full mapping see eventually I have to write this into the Hamiltonian right so the coefficients will make sure that there is an anti symmetry built into the coefficients when I interchange them so that will make make it anti symmetric finally right now we are only looking at a at a basis which is anti symmetric the actual anti symmetry that you are talking of will come because of the coefficients the coefficients when they you interchange 1 3 and 2 3 they will all change sign so so I am so I am not I am still not understanding of problem you are saying psi 2 1 you are not in change talking of chi 1 to chi 2 right the spin orbitals and coordinates are different you are talking of changing of spin orbitals or coordinates which one you are talking about coordinates if you coordinate then everything will change chi 1 chi 3 is also what is chi 1 chi 3 let me write it down it is a coordinate of 1 and 2 so it is chi 1 of 1 chi 3 of 2 so I am not understanding what you are talking of no I was talking thinking of you are talking of entire determinant there is a symmetry of the determinant that will come out from the negative side if you are talking of this is trivial because everything is 1 and 2 coordinate I think you are again confused with spin orbital index and coordinate index because the entire wave function is a function of 2 electrons it is just that I am also using 1 2 3 for here I can use a b c I mean that would have been better probably to you are confusion I should use chi a chi b chi c the interchange is in the coordinates but there is a another anti symmetry that comes later that we will talk later when you do chi there is another anti symmetry I thought you are asking already that that is the how do you if you interchange inherently 2 levels spin orbital level then there is a relation between a set of coordinate coefficient to another set of coefficient so that is a much more complicated thing no this is a simplest example for a 2 electron problem if I take 2 spin orbital that is trivial because you will have only one determinant so that so this is the simplest example that I can think if you want complicated I can do it you can take n m it will be m c 2 I mean after that it is it is just trivial if you understand if you understand this is trivial right in general you can have 1000 you will have 1000 c 2 for 2 electron for 3 electron 1000 c 3 for n electron 1000 c n I mean this mathematics is trivial right you are only picking up that many out of this set so if you do for example 3 electron problem so let us do a 3 electron problem then I have to of course increase the basis set otherwise it becomes again trivial so let us say I take a simpler example chi 1 chi 2 chi 3 chi 4 then I can again write psi 1 2 3 so now I will have a how many determinant I will have 4 c 3 which is nothing but 4 right so what are those 4 determinant so let us say d 1 2 3 chi 1 chi 2 chi 3 plus this this number is not important d 2 3 4 chi 2 chi 3 chi 4 how I index them is not important d 1 3 4 chi 1 chi 3 chi 4 plus 1 more will be there 1 4 1 2 4 over and it is a trivial exercise anybody will I mean any mathematics people will do it can keep doing it I am I am just you know showing it is not really necessary to show once I made the point that it is a combination of all n electron determinant it means all m c n you write this coefficients in some way so I will again write exactly in the form that I wrote I less than j less than k d i j k and chi i chi j chi k so all i j k I am summing but I must be less than j must be less than k so I am so I am ordering them this ordering ensures that I get only m c 3 in fact if you look at this ordering this ordering ensures actually that I only get this those numbers that I want where I am never allowing any one of them to be to change interchange so I am always fixing the first index to be less than second index to be less than third index you can do the reverse the first can be greater than the second second can be greater than the third okay so this is just a way of writing and you can keep writing now 3 electron 4 electron so your determinant will be an n electron determinant that is all so that many or spin orbitals will be there okay so there is something else that will come up in the d that is what I thought he is asking but anyway that is a later part we will see later that is trivial because the coordinates are always 1 2 3 each one okay so each determinant is anti-symmetric that is the reason we call it a basis so these are anti-symmetric basis okay alright


On Wednesday, we found out that the U.S. homebuyer mortgage applications to purchase a home rose 5% for the week and were a remarkable 33% higher than a year ago. Trump threatened to cut funding for schools and slammed CD-series opening guidelines as too tough and expensive. Meanwhile, the Swiss unemployment rate fell from 3.4% in May 2020 to 3.2% in June. Welcome to the Tick-Me-Lap data. I'm Canada Nell, the founder of the Investiva movement. Make sure to subscribe to the Tick-Me-Lap YouTube channel and support us by liking and sharing this video with your forex trading friends. On Thursday, we'll be eyeing the Eurogroup meetings and the U.S. weekly jobs report. Today, I'm looking at the Aussie dollar pair which is aiming once again to reach the key resistance level of 0.70 after failing to break above it over five times in the past year. So far, it looks like the bullish sentiment might already be dying down and the pair could go down to form a double top bearish reversal chart pattern. Do you think the Aussie dollar pair has enough bulls behind it to finally break above 0.70? Head over to the comment section and let me know. Of course, trading in the financial markets involves a risk of loss and you should only trade the money that you can afford to lose. If you liked this video, give it a thumbs up and subscribe to the Tick-Me-Lap YouTube channel. I'll get back to you with more updates tomorrow.


Okay, well, thank you very much. I'll go ahead and begin. I'm going to be talking tonight about discovering extra-solar planets, or as we refer to them in the field, exoplanets, particularly with the nephesis on the Kepler space telescope. Kepler was launched four years ago this week, and has been on a sole mission to detect an Earth-sized planet orbiting a star like our sun in its habitable zone. I'm going to describe that in a second. But first, I want to back our story up to 1995. When 51 Pegasi B was discovered, this is the first planet ever discovered orbiting what's known as a main-sequence star. That is to say a star that's burning hydrogen and helium like our own sun is. The planet, as you can see, is about half the mass of Jupiter, and it is orbiting so close it is boiling at 1800 degrees. Meanwhile, in 2004, the Hubble Space Telescope imaged, directly imaged for the first time, a planet orbiting a star called Formahalt. This planet is far, far away from its parent star, but even so, we still have to use extensive techniques just to block out its light. So observing a planet directly near a star is like trying to catch a match held next to a flood lamp. It's very difficult to detect, and what we're interested in knowing is whether or not this planet can exist in that magical distance called the habitable zone. So hotter stars have habitable zones farther away. Cooler stars have habitable zones that are much closer to their stars. We want to use Kepler to look at a single point in the sky, a very large, actually, area of the sky in the constellation Cygnus, looking at 150,000 stars all at the same time. This is accomplished by using 42 large CCD detectors, and as you can see, even though it's a large part of the sky, Kepler is peering into a relatively small portion of our galaxy. So the idea here is a numbers game, we're trying to see if we can find planets around a nice selection of stars and use that to extrapolate, to create statistics about the likelihood of Earth-sized planets elsewhere in our galaxy. But again, finding a planet next to a star is extremely difficult. So rather than trying to take a picture, what Kepler does is it tries to measure the tiny difference in its brightness as this planet transits in front of the star. So we take a light curve. So we're watching for that star to just flicker out. It's like turning off one light bulb out of 10,000. And what we have at the top is a light curve of one particular planet. This is Kepler-22B. And as you can see, there's just that tiny little drop-off in light measured once every 290 days. Here we have some phased light curves below. And you can infer things such as the relative size of the planet to a star. You can infer its inclination. And by the way, there at the upper right is an image of Jupiter what it would look like transiting our sun, comparing that to some of the planets that we've seen transiting stars. Again, these are not photos of stars. These are artist's impressions. But they give you an idea of how we can characterize the size of planets relative to the stars. And of course we can characterize the size of the planets themselves. Most of the planets that were found early on were these massive so-called hot Jupiters that orbit very close to their star. But then we began to find planets about the size and even slightly smaller than Neptune. And we've even found a planet orbiting binary stars, which is very exciting because when I was studying astronomy we weren't sure if a binary system could have planets. Of course it does. However, this particular planet is a gas giant, so there wouldn't be Mark Hamill looking up and seeing double sunsets. But if you were on a moon around this planet, whoa! Now, things got really exciting just this year because we discovered a planetary system orbiting a star that's a bit cooler than our sun. But as you can see, Kepler-62e and F are right there in the habitable zone. They're larger than Earth. But they're right there. This is what we're looking for. And Kepler-69 is even more interesting. Here we're finding this planet only a little bit larger, about one and a half times the size of Earth. It's almost certainly a rocky world like our own planet. It's right there in the habitable zone, right there where the temperature is just right that maybe liquid water could exist and maybe life could exist. So we're narrowing down the statistics. We're taking Kepler's field of view and we're showing you all the different sizes of planets that we have detected. And there's quite a lot detected. We have 132 confirmed of these planets. Several of them are Earth-sized, but we have nearly 3,000 planets left yet unconfirmed that we're almost certain are there. So this is what astronomers really do. We take data and we plot it and we generate these correlation diagrams. You could see on the far right the relative sizes of planets, the planet radii. You can see Earth down there. And we're looking for that one planet, the size of Earth that has the same orbital period as Earth. And here is a beautiful plot. You'll see that most of the planets discovered to date are about the size of Neptune, but we're beginning to find Earth-sized planets. And yes, some of them are in their habitable zone. We haven't found exactly that one planet yet, but we're getting closer and closer. But we have so much data. Let's make apps out of them. This is exoplanet. It's taking data that's on GitHub. I can't believe someone hasn't written an app by now. I've been speaking for three minutes. This is a cold vision conference. Come on. Anyway, get this app. It's free. It's fantastic. And it alerts you when a new planet is discovered. And you can look at the light curve. You can also visit Ewingo. And you can nominate a name for one of these exoplanets. I nominated two planet names named after my mother. My mother-in-law gave them that certificate from Mother's Day. You can go to kepler.nasa.gov.index.cfm and you can check out the latest on the Kepler mission. Unfortunately, and this is the part where I hope I don't lose it here. Just yesterday, they announced that Kepler suffered a failure in one of its three reaction wheels. It is presently unable to maintain the pointing necessary to take the data that is required. I have some good friends of mine who work on the Kepler mission. I know they're a little bit sad, but they're also very happy because there's so much data. And it's only a matter of time before that tiny signal of a Earth-sized planet around a star like our own sun is teased out and is found to be right there in the habitable zone. There are 100 billion suns in our galaxy. Our Earth twins exist. Thank you so much for your time.


It's time to get started. Thanks for joining us. I'm Cliff Lynch, the Director of the Coalition for Networked Information. You have reached one of the project briefings that is part of the Spring CNI 2020 virtual meeting, and that will be running till the end of May, so there is a good deal more still to come. Today we have two speakers, and I'll introduce the presentation in a minute, but just practically, we'll hear our two presentations, and then we'll take some Q&A at the end. Diane Goldenberg Hart from CNI will moderate the Q&A. There's a Q&A button down at the bottom of your screen, and I'd encourage you to key in questions using that at any point during the presentation, as they occur to you. We'll deal with them all at the end, but there's no reason to delay putting them in till the end, so please, as questions occur, drop them in. Our presenters today are John Dunn from Indiana University, and I know many of you are familiar with his work. He's a frequent presenter at CNI and has been one of the serious participants in our community for years, and he'll be joined by Sean Africamp from AVP, who AVP is one of the partner organizations that's working in this. Now, just by way of context for a little bit, Indiana has had an enormous project for digitizing audio visual material that's been going on for a number of years and has done a great deal of pioneering software work in this area and has built that out into a consortium, essentially, of like-minded institutions. If you go back through the archives of CNI's presentations, you can find an awful lot on the development of that work. The topic today is a particularly interesting one to me, and it's about how we can most effectively combine machine learning techniques and human expertise to address issues around the very challenging scaling up of access to large AV collections, where there's just no way we're ever going to get enough expert human time to do it. But where it's also really hard for machine learning to get it just right, and I absolutely believe that the path forward here is going to be understanding these alliances between the capabilities of machine learning and human expertise. So, I'm really eager to hear this presentation, and I'm extremely grateful to Sean and to John for bringing it to CNI. And with that, I'll just thank them again, and thank you for joining us and turn it over to John. Great. Thanks so much, Cliff, for that very kind introduction. So, as Cliff mentioned, I'm here with my colleague Sean from Digital Consulting from AVP. I'm from Indiana University, and we're going to talk about a project called AMP. AMP is the Audio Visual Metadata Platform. Chris Lasinic of AVP and I talked about this in a very preliminary way at CNI several years ago, and the goal of this presentation is to provide the community with an update of where we are and where we're going. And I should note this project is a collaboration of Indiana University Libraries, AVP, the University of Texas at Austin, and New York Public Library, generously funded with support from the Andrew W. Mellon Foundation over the past several years. So, to start, just a brief outline of what we're going to be talking about, I'll give some introduction and background and overview of the project. Then Sean will come in and talk about collections that are involved in the project at this point, evaluation and selection of machine learning-based tools within the project. I'll come back then and talk about the platform architecture workflows that we're building and next steps going forward. I should note that this work is the work of a large team spread out across a number of partners that I just mentioned, and give great credit to all of them for getting us to the point we're at now. And it's the work of all of these people that Sean and I are here talking about today. All right. So, some background. Cliff actually motivated this fairly well, but as he mentioned, we have a project at Indiana University called the Media Digitization and Preservation Initiative, or MDPI, that has been underway for about four years as an effort to kind of funded centrally within the University to first inventory and then digitize all rare and unique audio-visual resources from across the institution. And these are some screenshots from the MDPI website. At this point, we've digitized over 325,000 pieces of audio and video, nearly 18,000 film reels, creating several dozen petabytes of data to be preserved and in derivative form to be made accessible to the degree we can, both to the University community as well as the broader research community. These collections represent a wide range of subject matter and genres, but also a wide range of existing metadata. And so, a couple of examples. This is an opera performance video recording from 2000, I think, unfortunately still on VHS at this point, of Stravinsky's Rakes Progress. And you can see on the right, there is quite detailed descriptive metadata for this work that came in as part of our digitization workflow pulled from the library catalog, very detailed information about performers, about dates, about other contributors to the performance and so forth. So this is kind of one extreme of quality of metadata we're dealing with within the digitization effort, mass digitization effort at IU. At the other end, this is an example of, I think, another, yeah, VHS in this case, where you can see on the right, the metadata, we have the, in the title field, tape B contains digital tapes, 7 through 11. It doesn't really mean much. If I look a bit closer, I can see some identifiers that might mean something to someone who's familiar with collection from which this came, but not to any average person, certainly. If I start to look at the video image itself, though, I can see a bit more. I can maybe kind of make out that the backdrop actually says University of Indianapolis. I can see a date, a time. If I can look at the face of the person there and see, well, that looks like it's probably our former Senator Richard Luger, but this is not information that is readily available in the metadata and so could not be discovered by someone looking for something in this time period or of this person or in this place. And so we have a lot of content that falls in this latter category. And the question is, for us was how do we, how do we make this more discoverable and usable by researchers, students, instructors, and the general public? Members of them would be interested in accessing this content. Going back to the opera example, I should note, even though we have very, very rich descriptive metadata, we don't have great navigational metadata or structural metadata to get me to particular acts, scenes, arias, and so forth within the opera. And so even though I can get to this two hour recording, two hour plus recording, I can't find what I want within it necessarily. And so working with AVP, we identified there's this challenge that we're facing, but others are facing as well. Growing digital AV collections created both through digitization, as in our case, but also just all of the born digital video and audio that many of us are taking in that's so easy to create now, so quickly compared to the past, obviously. At the same time, our users have increased expectations for access, both at the student level and at the faculty and research level, to be able to find things on the internet, to be able to find our collections easily, to have the same ease of use of working with our resources that they have with things like YouTube and so forth. But of course, we don't necessarily have the metadata for these collections. A lot of these collections have been somewhat neglected, in fact, due to staffing issues, due to technology issues. And so we don't necessarily have resources to do the full human sort of detailed human cataloging of every item that we may find in our collections. But working with AVP, we identified that there's really an opportunity here, we felt, given that machine learning based tools, now they're automated tools for continuing to emerge and improve that could potentially help with metadata creation for these types of resources. At the same time, there are new access tools that go well beyond the catalog to provide, to support various forms of time-based metadata, description of things happening within the timeline of a video or audio recording rather than just at the entire work level, tools such as Avalon, ABRY, standards such as DRIP-IF and its work for AV. And so really looking at how we can leverage the best of the automated tools together with human expertise, we're not trying to say at all that AI can replace the expert human cataloger or archivist. And to look at the fact that various kinds of collections, a music collection would be very different workflows than oral history collection, for example, in terms of the tools and steps to be applied. And so the goal of AMP is to build an open source software platform to support metadata creation for AV collections that supports building of these workflows that combine automated and human steps. Each step is what we refer to as an MGM or a metadata generation mechanism and that could deliver metadata ultimately to a variety of different target systems such as library catalogs, online access platforms and so forth, not tied to any particular delivery environment. Where we are in kind of the arc of the project is this, the middle piece on this slide. We are in what we call the AMP pilot development project or AMP for short. This was preceded by a planning project and would be followed by implementation of AMP into something that would be more production ready to be used by us, by our partners potentially such as MYPL and others as open source software. So with that, I'll turn it over to Sean to talk in more detail about the collections that we're working with and the tools that we're evaluating. Thanks, John. We could move to the next slide. So yeah, for this project, we needed a variety of sample audio visual materials that would allow us to test a wide range of MGMs. So materials from two major collections were chosen from MDPI content from the IU archives and from the IU music library that covered events in IU history and IU musical performances. We also needed an external partner to test less IU-centric types of content and also offer us some different use cases. So the New York Public Library, who also has a large AV digitization initiative, joined the project with the Gay Men's Health Crisis Collection, which includes interviews and footage of protests and speaker events documenting the organization's education advocacy efforts during the AIDS crisis in the 80s and 90s. Next slide. Amp requested 100 hours of content from each collection for a total of 300 hours. And we already had a long list of possible MGMs generated from a previous Amp planning project. So we met with each of the participating libraries collection managers to walk through some of their sample materials and talk about what metadata would be useful to extract and how they would want to use it. We then identified common needs so that we could prioritize the types of MGMs to incorporate into the project. For example, all collections had some use case where they wanted to transcribe speech from video or audio. And after that, each collection had needs that were more specific to their use cases. IU archives was interested in identifying people and locations. Certain people in particular are like Herman B. Wells, the former president of IU. The music library had an interest in identifying works performed, especially for segmentation and navigation purposes, as John just showed. And MYPL was interested in identifying content boundaries for locating multiple events captured on a single item or segments with no content in order to speed up archival processing and to support copyright review. Now in order to select the best MGMs for our collection manager's use cases, we needed to come up with some criteria for conducting evaluations and making informed recommendations. So we developed a set of criteria that we could use in selecting appropriate MGMs for the pilot, but also provide a general framework for any organization wanting to evaluate MGMs for their own use with the future AMP platform. So in addition to testing the accuracy of each MGM, we needed to consider factors such as cost, processing time, computing resources needed. It was also very important to everyone on the team to consider the more social risks of MGMs, like how algorithms might express hidden biases or what commercial services will do with the content you submit or the data they extract. So we included a criteria that we call social impact to assess what we found from each company's terms of service policies and potential risks that we saw in our accuracy testing. And we found the fairness, accountability, and transparency in machine learning principles for accountable algorithms to be a guiding light in developing these criteria, as well as an important resource in developing our testing strategies. Next, as a team, we developed a broad definition of metadata quality as it relates to our objectives for the project and we came up with the following. So note the emphasis here on good enough. The actual measure of good enough for each of these functions will differ depending on the collection and MGM involved. So constant conversation with our collection managers during the testing of each MGM has been important so we could have agreement on what good enough means. For each MGM category, we worked with the collection managers to select samples from their 100 hours of content that would hopefully represent a wide range of features to test for. As we tested and learned more about all the potential mistakes that an MGM could make, however, we realized that our sample sets could have been much more diverse in languages and dialects spoken and in ethnicities represented in order to push the limits of each tool and to tease out possible biases. So if you are trying this on your own, we recommend doing some experimentation with each MGM before you finalize your sample content set so that you can get a clear picture of just how poorly the MGM can perform. Next, we had to determine what metrics to use in testing each MGM's performance and this step made it very clear to us just how well we would need to define our use cases before testing MGMs for accuracy so that we would have a good measure for success. For instance, with video OCR, we wanted to find words and phrases that occur in the video that we could use as keywords or as input for named entity recognition to identify controlled access terms. We didn't care about where on the screen they occurred or at what time in the video. So knowing this allowed us to limit our testing to just verifying words found in the video without having to test the accuracy of the where and when. So once we had our samples and testing methodology, we created ground truth results for each of our samples. We ran our samples through each candidate MGM then we converted that MGM's output to our ground truth data format. We compared to and calculated precision recall NF1 scores and then shared our results and analysis with the collection managers for review. We also converted the data to more human comprehensible formats for qualitative review such as plain text for transcriptions or contact sheets of images for scene and shot detection. Overall, we found this whole process to be quite time consuming in that we had to write scripts not only to run the MGM's but also to convert our MGM output to a common data format and then compare and score MGM results against ground truth. So we've started sharing these scripts and GitHub for others to use so that they don't have to replicate the process that we did when they go to evaluate the same MGM's. So to date, we've evaluated and recommended a mix of open source and commercial MGM's in five categories, speech, music and silence detection, speech to text transcription, speaker diarization, named entity recognition and video OCR and we're still working on at least five more and we've started posting our evaluations of the MGM's we've reviewed on our project wiki so you can learn more there about how we came to recommend these particular tools. In the initial planning for this pilot, we had identified a long wish list of metadata elements we hope to extract through MGM's but after we started testing them, we quickly realized that the outputs we got from applying MGM's out of the box without training to arbitrary audio visual content would require some human intervention to make that output useful to end users. So we revised our goals to extract data that could be efficiently refined through minimal human editing and to enhance the amp platform with tools for supporting that human review, which John will talk more about in a minute. So of the MGM's that we've tested and have yet to test, we've been able to extract keywords, dates, names, geographic locations and full text and we're also generating metadata to support enhanced navigation and more efficient human processing of collections and copyright review. As far as training MGM's to achieve better results, our goal for this phase of the project was to develop flexibility in the platform to swap out MGM adapters to suit the needs of any asset type domain or collection. We were not necessarily trying to train models to get the best possible results for our sample materials. But we did want to explore how we could add options to the amp MGM adapters to allow collection managers to easily include their own models or custom vocabularies. We experimented with using several controlled vocabularies with Spacey, the open source natural language processing MGM we've chosen for need entity recognition. So now the adapter allows for the addition of any vocabulary an amp user wants to include that uses Spacey's rule based entity matching in addition to the default model. We're currently working on similar customizations for structured OCR of supplemental paper based materials and facial recognition of select known individuals. Throughout the course of these MGM evaluations though, we've discovered many future opportunities for diving deeper into training models for amp, particularly in using library collections as training data. With the addition of human editing tools into the amp platform, we have the potential to develop workflows for creating training and testing data that libraries could eventually feed back into the system as models trained on more domain appropriate content, which we think is something that would be very exciting to explore. And finally, I just mentioned facial recognition and this has been a hot topic of discussion within the team throughout the course of the grant. Facial recognition has gotten a lot of attention for producing biased results based on bias training data for its abuse and surveillance and automated decision making and the privacy implications of using people's images without their consent. Because of what we already know about the limitations and dangers of existing facial recognition models, as well as what little we were able to find out about data use and reuse by the major commercial offerings. We decided to limit our explorations into facial recognition to locally hosted open source tools only, and to the very specific task of detecting a single well known individual, Herman B. Wells, in a collection of videos. We've tried to keep bias and privacy in mind when evaluating all of our MGMs by applying our social impact criteria. But we're always open to input from the library community and how we can do this better. And I'll pass it back to you, John, to talk about platforms. Thanks, John. So I will very briefly talk about how we've been building out the AMP platform. Obviously, there's not a lot of time to get into a great level of detail, but just wanted to highlight a few key things. So back in the planning phase of this project, we worked to develop a kind of high level conceptual architecture for this platform that would allow us to do what we want to do. And a couple of the key pieces of that are user interface application, obviously, for archivists, librarians, collection managers to work with, to submit content, to build and run workflows. We would need some type of workflow manager to support constructing and executing these workflows, combining MGMs, which might be running locally, might be running in the cloud, might be running within supercomputing environments and elsewhere. And one of the significant architectural decisions we made was to choose an existing tool called Galaxy to serve as this workflow manager and workflow execution environment Galaxy, if you're not familiar with it, is a workflow system originally developed out of the bioinformatics computational genomics world for doing genomics type of work, but it's been successfully taken in by other communities who need this type of building of composing of workflows from multiple tools. And so we took actually an example from a related project Clams at Brandeis and WGVH, which is after some of the same goals as AMP, and have adapted along with them this Galaxy platform that potentially allows us to collaborate more greatly with that other project, the Clams project as well. But Galaxy gives us this ability to relatively easily build adapters that connect in MGMs and compose them into workflows. And then we have focused on building out this front end user interface that's more appropriate to our use case to allow submission of files, selection and execution of workflows and retrieval and interpretation of results. Another thing I wanted to highlight in the architecture is, as Sean mentioned, in addition to the automated MGMs, the notion of a human MGM, a workflow step that requires human intervention, is a key part of our concept here. And so we've been working on implementing a number of human MGMs into AMP to support things like transcript correction, review and revision of named entity recognition output, review and revision of automated segmentation of audio and video. And we integrated a number of existing tools as noted here to support those functions, have integrated or are working on integrating those tools. In addition, we needed some environment to support task management by the kind of human workforce, in our case, mainly student workers who would be doing this work. And we used an existing tool JIRA, which many of you may be familiar with, to support the creation and assigning of tasks and taking of tasks by that staff. And that is going quite well at this point. So with that, to wrap up, I just want to talk a bit about our next steps on the project. So we've put a lot of work into selecting collections and to evaluating testing MGMs. That continues. We have started building out workflows to support the various types of collections we're working with. We're going to work on completing that design, integrating additional automated and human MGM steps as they're selected by Sean's team. Start executing and evaluating the outputs of these workflows against test collections, working very closely with the collection managers to identify workflows and evaluate outputs as to their viability. And then continue to refine the application user interface and functionality. Looking at the next phase of AMP work, the idea of this pilot phase is really trying to prove out is this a concept that's going to work? Is this an architecture that's going to work? Does this really help in terms of time and costs and so forth with this metadata creation need for AV resources? So we'll be evaluating all of that and hopefully feeding into a next phase that would focus on getting this more production ready, deployable in different environments. One area we might focus more on is in certain areas, rather than relying exclusively on off-the-shelf MGMs, looking more to training of models or additional training of existing models, transfer learning kind of approach for particular kinds of collections that have large volumes of similar needs. And that we would benefit from having some additional data science expertise within the project in this next round. And then really focusing on how the metadata gets into and is used most effectively within various discovery and access platforms to serve this ultimate goal of improved discovery and use by users. So that's what we have time for today. Here are some links with more information about the project. I think now we'd be happy to spend a few minutes answering questions. Thanks very much. Thank you. Thanks, John. Thank you, Sean, for that overview and very interesting details about this project. A lot of steps in there and detailed work. So I'd like to open the floor now. For questions, just you should find a Q&A box at the bottom of your screen. If you just click on that, you can type your questions in the Q&A box and I'll read them here for John and Sean. And let's start with Howard Besser. Hi, Howard. Howard asks, it looked like your human MGMs were all really human review slash correction of machine MGMs.  Sean can tackle this as well. I think, yes, so I think another one could consider human MGM, for example, taking all of the output and using this to help formulate a catalog record, for example, or there could be transcription processes where automation just simply is not going to work due to the languages involved due to the quality of the audio due to other issues that would be entirely human rather than correcting machine output. Sean may have other examples. Yeah, I mean, I think the way that the system works is you have pipelines of MGMs and the output of one becomes the input of another. So it seems feasible that you could have a human create data or, you know, hand transcribed transcript and then input that into a machine MGM. So I could be fitting in nicely. Okay. All right. Thank you. Thanks, Howard, for that question. Robin, Robin just chatted in. Great to hear what the project has accomplished. So thank you very much for sharing that. If you have questions or comments that you'd like to make live, please feel free to raise your hand and I can enable your mic. I can unmute you. So you should see a little option to raise your hand while we're waiting to see if we have any more questions. I just want to remind everybody that this webinar is part of CNI's ongoing spring 2020 virtual membership meeting, which will continue through the end of May. And I just chatted out there direct link to the schedule for the rest of the meeting. So check that out and we hope you'll join us for more offerings. And I also just want to remind everyone that we did offer, we did add a final plenary by Cliff Lynch, which will be on Friday, May 29th. So we hope you'll join us for that wrap up of this meeting, which has really been a great experience and a great opportunity for us to provide a lot of resources that have been recorded. And I'm going to thank our presenters again very much for coming to CNI to share your work with us. Thank our attendees for spending some time with us here today.