All right, so let's check out what you have here. I think it's really cool. And I'm gonna look at your left guy first. And I'll leave the sound on just for myself here. Let's go again here. I love everything, it's so cool. And I love that little impact there. Just that little, it's not too big, not too crazy. If I would be super picky, I would say the both shoulders go at the same time. So I would reduce one by 30-40% and also make it a frame or two later, probably two. Maybe one, suppose it's so small, one might be enough. You just want it to twin, but... And that way it doesn't make it that big, it's great. Once you loop it, it feels a bit big. But the first time around, I was more looking at his face. So that would be my picky note there. And then on anything, while this happens, that little moment, they could be like, the thumb could come down a bit, the fingers could just kind of clench a tiny, tiny bit.  Again, not to do it, oh, I don't see my camera. This is nostril view today. So something where his hands, they do like a little, just a slight adjustment and the knees can just kind of come in. Just something where you can see that it is moving. Like I'm going through here, as I'm scrubbing, you can see how it ends here. So just for your next pass, but this is so cool. I really like it. As you look down, the different looks after that are great. Let's watch this again. Yeah, it's really cool. One more time. It feels like, watch his chin, I mean super big here. He goes back, forward, stops a bit, and forward again. And right now it feels like a little pop. But I like that he stops like that. It's not all spliny up and down. So if it looks like your curve is doing something and you get like a double key or something, maybe going up a bit and down, I would just make it so that it flattens out of it and it continues. So you actually have that pause, but it still kind of drifts down and it continues. So it's a bit more deliberate. And again, by like two frames, three maybe, something super small. But just to get a bit more than that little stop there. All right, so look at her. Not sure about that blink. Like I love all this. I love the timing of this Mac. You got good stuff there in the legs. The breathing's great. Yeah, to me the big things are her blinks and the way she stops her hands. It comes to a fairly quick stop. You can track her fingers. It goes back, goes back, goes back, boom. And then pretty much nothing. There might be like one more thing here. No, not even. So it feels like that goes back, boop. And give this like something. And then she can go back. Like, and she can go back and then have kind of a stop, a soft and then adjust her hands like that, potentially. If you want to get away from that super flat pose. All right, drawing mode here. Let me get out. There you go. So she goes back and then she has which could still work. So that way her hand, the fingers could be here when the hand goes up. She can kind of lower those other fingers. So it feels like, right, back. And then a little grip. Again, I'm talking tiny, but just to get away from, and then that stickiness and then nothing going on in the fingers. I feel like if I go back, it might be one more, like one more pause and a little clenching of the fingers. So like because of it being tight, just kind of a little adjustment. I think that would be really cool. It's great. Love the breathing. Stuff on the hips that goes back. It's great. I'm going here.  This guy's done. And for her, I think the biggest thing are her blinks. It feels like she's doing, where my camera is. So it feels that deliberate. She was, you could have, you know, it starts off like that. And then you got that ease into halfway down closed. And then hold that probably for the same amount. And then you want to open up where it's just really easing out of that.  And my eyes get smaller and smaller in this regard this guy. And then you get to, you know, around here. And then it will go all the way up to here but over like five frames. It's kind of like, they get that close, slowly opening, opening, but then soft with the ending. Otherwise it feels again a bit too mechanical.  So when my top of the eye was like this, just adjust accordingly. It's the same principle of easing out of that, close in a soft, old, soft opening, open with the soft ending. That makes sense. If that makes no sense, by the way, you can send me the Maya file and I can go in there. And then I feel like just for contrast, the understand could be bigger. Only because right now, the mouth is kind of the same value in terms of how big it gets. And the stand is right here. And I feel just, she's kind of looking straight here. She's shaking. We wouldn't quite see all the shakes because there's a lot of movement. And here she relaxes and gets kind of profiling.  She's opening the jaw a lot more on stand. I don't understand. And it's kind of also an emphasize for her. I don't understand. And that will give us a nice little contrast, good little silhouette there.  And then once you get to here, it's kind of even could be, you know, one side is a bit more in that makes no sense. This drawing makes no sense. Basically, it's more like, like, you know, one corner out more. And the second blink, same thing. It was the opening. I will break it a bit faster. And it feels like your whole, this is kind of a, I will make the first one to hold. It's kind of like, and then it's just, it's a blink. And he's observing him waiting for his answer. So I will keep this to, let's see if I can cut that out. I will cut out the closed part. All right. It gets the close. And then that guy cut this, sorry about her. And now it's easy into the ending. It still feels a bit mechanical on the opening. Pretty, you know, kind of relax into it. But for this, I cut out, as you can see, it goes from 78 to 81 on the blink. I love her finger moves, by the way. She relaxes the fingers as I will move there. Yeah, just what I mentioned there. And this guy's done. It's really cool. Picky, one more thing. Watch out. Once she hits that back part here, I feel you rotate too much out of this point. You can do more in here and a bit less in this. It's like a crane coming down right there. This feels a bit big. She has such a big long neck. And then track her nose. Just during this section here, it feels a bit drifty. Like, and it feels less like a move that's on purpose. She goes back. That could just be a bit sharper and then ends with a roll down on the head. Yeah, just a bit drifty there.  So he goes back and that's a more deliberate look. Maybe not that deliberate, but something where she goes back and has a bit of a contrast at the end. Again, I'm saying that's a shop right here, but you could try it. There's a little bit of it in there and you can just make it a bit more deliberate. So go back and then it's a bit more deliberate. All right, come on. What's going on here? Let me cut this out. And then you mentioned you just hit spline on that guy. See, watch this. My frame's here. What did I do? There you go. You're looking down. See, watch out. She's looking. No, this is the, I guess, I guess. Yeah, she is looking around here. Where, yeah, I was slightly reading it. I was still here because she goes back and goes, and she's watching him.  I just told you all that stuff and so what do you think? And I think you could start like that where she is higher and has a moment of looking down and then back up. See, you know, she could be looking at him and go, friends are supposed to feel good and back up. Like looking really, I think, but you know, you know where she looks at him, waits for the reaction, goes down, has that realization and looks back up to finish it. So it could be, on the field, she could go back up to look at him. Just a suggestion. So right now it's not massive. I see a sideways with a tiny up. And I think that would be cool because that way, it doesn't look like she's just delivering a line. There's more of a thought process in there, like really, oh man, well, ah, and then explains that. I think in terms of the way you got spine, it's already in a good position. I think what you have with the head accents is good. I would just tighten it up, especially having them. Just having them and then it's kind of drifting down. But there's nothing major that I would change. That, you know, technical things like those adjustments, which is doing with her fingers, poppy moment. This might be a bit big, how you go up with this shoulder and down with this shoulder. It's a bit like, okay, okay, okay. It's a technical term. Just happening at the same time, it feels more like a massive, massive rotation in both guys. And at the same time, you have this drag in her hands. I feel like it pivots off those fingers. If you scrub through, she kind of freezes in there and it's kind of pivoting off that. So you would have to change the speed of this hand to not make it stick as much. Now, in terms of overall gesture, I think, yeah, there's maybe two on the notes. I was thinking that on point, she makes the bigger gesture. So she goes, yeah, no, I guess, it's probably good on the point of having them. It's having them could have a, that's right, let's see. I think you could shrug more on having them. Yeah, I mean, like what you have, probably just on having them, like ha, to drive also that sound, but having them, so I think that I would add. Yeah. And then, you know, face-wise, as you, and this, this is a very inquisitive, like so, like what's going on here. She's a bit wall-eyed, like the Simpsons, bring your eyes in a bit, and think about how you want to end this. With that, so right now, she's like a tight little hint of a smile there.  I'll give it some sort of, it's a concerned look. You know, it's a mother, so she's going to be worried. That's kind of direction I would go. Anything away from this curve, like a smiley face, because you are ending on that pose, and it's a long, held pose as well, right, how I'm scrubbing through, like that's the whole time there. Just think about how you want the audience to perceive her. How much is she worrying? How much is this, like an interrogation? A little exhale, like you have this great breathing at the beginning, in the first shot, you bring that into this guy as well. I think the lower lip in this current stage is a bit too poppy. I feel there's a lot of in and out and lower lip stuff that you can soften or take out. It feels a bit too like you're hitting every note. Friends are supposed to make you feel, friends are supposed to make you feel good. Like I would go with these three big ones. Friends are supposed to make you feel good. Yeah, she does a lot of stuff here, but friends are supposed to make you feel good. Make it Muppet style, keep it simple. She'll be looking and then friends, friends are supposed to make you feel good. Keep it broad, and then having them, like that I would really enunciate. Point of having, like one favor, one side, having them, and hold them, that pose, having them.  Something like that. Again, those are all notes and suggestions that I would try out. All right, thank you. All right, there's an email. You can sign up, you can start whenever you want. You can submit whatever you want. You get 16 submissions. Either way, a like and subscribe would be awesome. All right, thank you.


And thank you for the invitation to speak here today. Let's see. The only thing I need is... This is a pointer, right? So let me figure this out. And then the other thing I need, yeah, there we go, is a sense of how time passes. Is that a clock you have there? It's behind my computer. Ah, great. Okay. All right. Well, I'm going to talk today about a project that we have at DARPA on having machines try to understand cancer pathways. I should tell you I'm not a biologist. I'm not a clinician. I'm not an omics researcher of any kind. Last course I took in college in life sciences was a long time ago. But I do think that the universal ideas of mechanism, causality, modularity, hierarchy, that underlie computer science also underlie systems biology. I've never found two fields that are as closely... have such a close affinity as computer science and biology. And so I'm thoroughly enjoying this project. And on behalf of all of the participants and you'll see some of them are computer scientists and some of them are biologists, I'd like to thank you for the opportunity to speak. I also have to put this slide up. You'll forgive me. All right. Last October, government back treasury bills lost seven standard deviations in their value in about two minutes. And two minutes later they recovered their value. This was caused by a flash crash. And flash crashes are caused by automated trading systems that we don't really understand. In a lot of important domains we have what we might call component level understanding, but not system level understanding. We understand, for example, that iron will cause algae to bloom and that when the algae die they sink to the ocean floor and they take carbon dioxide with them. So with this component level understanding of how algae work, we're proposing to dump a lot of iron filings into the ocean to control the climate. What will happen? We don't know. We lack a system level understanding. For these reasons to try to develop technology to provide system level understanding we created the big mechanism program, DARPA, the goal of which is technology to understand complicated systems. And we're surrounded by complicated systems that we don't understand well. They include not only the climate and ecosystems and socio-economic systems and energy systems and food systems, but cancer. So fortuitously we decided to work on signaling pathways in cancer and we bumped into Frank McCormack. We were introduced by a mutual friend and he said 10 months ago now, why not work on RAS? And we said, what's RAS? And we got started. RAS is now the focus of the big mechanism program and what I'll do in the next few minutes is tell you about some of our progress. Pretty much all of the systems that are being developed in big mechanism have this architecture. The systems read journal articles. Build up models of what's being described in the journal article. They suggest revisions to models. Those revisions are processed and revised models are produced. There's some model management happening. And then downstream we do some reasoning. And the reasoning may be about what causes what, where to apply pressure to a system to make it change its behavior. So everyone's trying to build this kind of system. But in 10 months we've sort of gone from naivete to realism. I'm going to tell you about that. Not because I think we're on the wrong track, but because in any program like this any time we try and do something like this the challenges are much more interesting than the successes. Your cancer biologist, you certainly understand that. Let me start with reading. You don't recognize how difficult reading is because you do it all the time and you do it really without thinking about it. We all do. But for computers, reading is really a problem. Consider the sentence. RAS acts as a molecular switch that's activated upon GTP load and deactivated upon hydrolysis of GTP to GDP. You read it really without giving it a second thought. But a machine has to figure out what the entities are. RAS, GDP, GDP. It has to associate those entities with the standard ontologies. They recognize activation loading and hydrolysis as events. But importantly it has to understand that some events and entities are actually classes of things or abstractions. They're not particular precise things. They may not resolve to a gene ontology identifier. They're abstract classes. Then, if you get that far, a system has to understand what are called co-references. Look at the second dimension of GTP here. Is this the same thing as this thing? Well, you know it is. But how's a machine supposed to know that? And what if it were called something different? Then if you know what the entities are and you can sort of track them through the sentence, you have to put them in their appropriate roles in events. You have to recognize that the subject of activation is this molecular switch which itself refers to RAS. And you have to recognize that loading here of RAS, not the loading of GTP. It's the loading of GTP into RAS. Again, you do this without thinking about it. It's hard for computers. If you can do it for one sentence, then you have to read a whole paragraph or a whole paper and you have to be able to tell a causal story about what happened. I thought naively that everything that biologists talk about is represented in a biological database or ontology somewhere. Gene ontology and pathway commons collectively contain about 2 million proteins. So our team at USC looked at 755 sentences from papers, found 541 proteins, looked them up in gene ontology and pathway commons and only found half of them. We thought, well, okay, maybe the problem is that there are sort of spelling errors. So they built a fancy pattern matching algorithm and only found quarters of them. Many of the things that appear in papers simply don't appear in the databases. When you do find something in the database you typically find too many. So I looked up RAS yesterday in gene ontology. There are 91 entries. There are almost 17,000 entries of things that are associated with RAS. How is a machine supposed to know who are the players in a story? It's not a trivial problem by any means. Another problem is that we use different words to talk about the same thing. Here we see the volume and weight of a tumor right here. Volume and weight is in orange. That's the second reference to the volume and weight of that tumor. Right at the end of the paragraph and it's the word size. They mean the same thing. How is a machine supposed to know that? And you can go through this entire paragraph and you realize that we refer to this thing here four different ways. One, two, three, four different ways within one paragraph. Continue with the example. Occasionally the same thing will be referred to in exactly the same way. PI3K is referred to in exactly the same way. But this is really the exception, not the rule. This is called the co-reference problem knowing that one thing has to be identified by different names. I was really astonished by the scale of the co-reference problem. Larry Hunter at University of Colorado Medical Center hand annotated 97 papers. What he found was that there were over 20,000 what we'll call identity chains in those paper. And an identity chain is successive references to the same entity as I just showed you. These are four successive references to the same entity. In 84 papers out of that corpus, the mean number of identity chains per paper was 245. This is worse than Tolstoy. You have 245 characters in your novel and they are referred to on average 12 times. How are you supposed to reconstruct the story? It's difficult with Tolstoy. Imagine a machine trying to do it. In fact, named entities and co-references are only two of the problems involved in reading. What you've seen happening in the reading community is the development of many, many technologies for machine reading. They're sort of always put into a great big pipeline. We have a document reader, a sentence splitter, a JNIA tagger named Entity Recognition. Down here we refer to Kebby. Here we do some more tagging. What have we got here? Function words. Lots and lots of things that find function words. Here we find overlapping annotations. We do some parsing. This is from the University of Manchester from the National Centre for Text Mining in the UK. So we build these huge systems that do a lot of different kind of processing because reading is hard. But the vast majority of reading systems do not read the way that you and I do. When you read Angry Whopper All Fountain Drinks 98 Busses Welcome, you actually have something in mind. Humans read with something in mind. You can call it context if you like. You can call it a model. Roughly the same thing. Which of these two pictures do you think this is about? This one or this one? You have something in mind. When you see this, it evokes some context or some model. It's the fast food model. It's not like you just evoke that model and then the knowledge pours into your head. You actually have to think about what's being said. Are buses really welcome? No. People on buses are welcome.  So when you process language you do an enormous amount of inferential work and you do it with a model in mind. And that's the kind of thing that we want reading in the big mechanism program to do. So here's how we're doing it. We are providing the readers with a huge model in the Biopax representation language and with a stack of texts. And then they're going to read the text and they're going to answer two questions. Just two. What does the text claim about this seems to have lost my microphone. What does the text claim about the prior model? Particularly does it corroborate, contradict, or extend the model in some way? And where in the text is the evidence for that claim? Those are the two questions that we want to ask about this model given these texts. Now, let me just get myself organized. So for example this is a paper from Nucleic Acids Research it claims something that we didn't know before that is it wasn't in the prior model. It claims an extension to the prior model. Namely that the protein I don't know how to pronounce this HUR translocates from the Nucleus to the cytoplasm. That's something in the paper that wasn't in the model. And this index card, we call these index cards was constructed by hand by scientists at the MITRE Corporation as a gold standard for an evaluation this summer in which machines will work with a model that has about 20,000 entities in it and will read about a thousand papers. So it can read a thousand papers, the machines are going to read a thousand papers and they're going to construct index cards like this by reading against this model. Now the question of course is whether that's going to work. And so let me show you some prior evidence. This is what we were doing in December. In December we asked whether the machines could recognize the entities in biological texts. And this is precision and recall. For those of you who aren't familiar with precision and recall, recall is sensitivity and precision is one minus specificity. So this is an error rate. Anyway, so we asked two questions. We said first can you identify that something is a biological entity that's these blue triangles and then we said can you identify which biological entity it is by pointing into one of these ontologies. And we asked Harvard trained biologists the same question. And the machines are doing the best of the machines. It's sort of doing almost as well as a Harvard trained biologist. That was in December. We also asked whether the machines could identify events in the text. So here's an event. The expected increase in IRC activity in the NRAS mutant cells. So we want to see whether the machine can recognize that this is actually an event and what its participants are. Again, here's the biologist. Here's the best of the systems. Doing almost as well. Where machines didn't do anywhere near as well as biologists is on this new task of saying what the text says about a prior model. Machines have never been asked to do that before. And so as you can see performance isn't very good. Interestingly the biologists also couldn't agree. There's about 50% overlap between those two biologists on what they think the text says about the model. But that's a different story. So in the meantime in the last few months the reading systems have gotten a lot better. Go back to that paper I was telling you about the translocation of the protein HUR. This is the output of one of the more sophisticated semantically deep parsers developed by James Allen and his team at Rochester. By the way there's a URL here. Tiny URL Alan Trips. You should try it out. It's really fun. It does a good job parsing sentences. Anyway, here's the parse tree for the first part of that sentence. HUR is normally a nuclear protein. And it appears to be correct. That is to say it's telling me something. It's about something being that's the is here. It recognizes that it's about something being normally happening. It recognizes that it's about a protein. That the protein is the subject of the be. But here's where it starts to get troublesome. It says that being nuclear is a property of the protein. That's actually not what the sentence says. What the sentence says is the location of the protein is normally the nucleus. And so in a sense it's got it wrong. In another sense it's got it right. There are such things called nuclear proteins. It just doesn't know that nuclear proteins live in the nucleus. Also keep track of this. It understands that this is your protein. This is the thing we're talking about. Now we go on to the second part of the sentence. And the second part of the sentence is but it can translocate to the cytoplasm on stress. What's it? The parser fails to recognize that it's that protein. It's now it not referring back to HUR. And something else interesting happens. It says let's see what it's got right. First of all it does recognize that it's about translocation. It does recognize that a lot of parsers cannot. It recognizes that there's a modality to this sentence. It doesn't say it does translocate. It says it can translocate and that can make all the difference when interpreting experimental results. So it does those things right but look down here. This is sort of interesting. Translocate to a location which is the cytoplasm Wait a minute. We have a modifier for cytoplasm. This phrase is parsed as the cytoplasm on stress. As if to say the cytoplasm on your slide or my brain on drugs. It's not right. It's not saying that stress is the cause of the translocation of the cytoplasm. So it gets some things right and it gets some things wrong. And the reason I'm telling you this is that I see this going on pretty much indefinitely. Machines will do an okay job reading but to do a really good job they have to know something about the domain. And that can be quite taxing. Alright. That brings me to my penultimate topic which is how to represent biological models and the evidence so the machines can reason about these models and update them when the evidence becomes available. There are certainly differences between the representation languages we use for biological models. We use Bell, Biopax, PiSB, Kappa, variety of things, pathway logic. But all of them are basically if-then rules. All of them encode basically if-then rules. Here's in Bell the statement in Bell here's the if part, here's the then part. This really says RAS ASPB 2 complex increases transcriptional activity of P53. There's another if-then rule in Kappa. It basically says if you have a molecule that looks like this and a molecule that looks like this then they can bind to one another. That's what this rule says. So they're all sort of if-then rules of one kind or another. I will skip over that. So then the question is if models are just collections of if-then rules how does reading update this? And I'm going to sketch this very quickly. Here is if you like an extremely reduced model of how H-RAS is activated by these things up here. Now imagine that this is your prior model. This is what the machine knows and it reads H-RAS association with GTP was undetectable when PIK3 was substituted with mutant variant. Clearly PI3K is having some effect. It needs to play a role in this model. The question is where? Should you update this rule here? Should you update this one? This one? You've got a model. What is the text telling you about the model? Well the algorithm for doing this is actually pretty complicated and it's Bayesian but I can sketch the intuition pretty easily. The intuition is that you want to modify as few rules as possible and you want to modify them so that they look more like other rules that have the same sorts of antecedents as the rules you're looking at modifying. So that's a bit of a mouthful. So let me give you an example. Rule 1 has EGF and EGFR as antecedents. There are very few such rules in the database that also have PI3K as an antecedent. Whereas rule 529-1 has antecedents, GAB1, GAF2 and many other rules that have that as an antecedent also have PI3K as an antecedent. So this is a more likely rule to modify.  Now will this work forever? I mean is this simple way of doing updating going to space for us to build really complicated models of cancer and what about when we propose such an update, don't you want to go back to the lab to see whether it's right? Well yes, exactly you do and so all modifications to models will be provisional. And there will be lots of modifications to models because we're going to be reading thousands of papers. So that leads to a really interesting sort of new area of science which is how to manage thousands of models. Each of which has some sort of family resemblance to each other but is in some ways different. To my knowledge nobody has actually tackled this problem before. If you want to have millions of models of nature out there being maintained by machines on behalf of human researchers how do you organize them all? How do you search them? Who owns them? So this is a pretty interesting challenge and it's one of a number of sort of high level challenges that we face as Big Mechanism Project goes forward. Because I think that the real contribution of Big Mechanism is to change how knowledge is organized and communicated. Today we work under a thoroughly medieval model. You do and I do.  It's a medieval model in which we go into our monastic cells nowadays they have a computer in them but we go into our monastic cells and we pull knowledge into our heads and we try to synthesize that knowledge in our heads. Well there's a bit of a problem with that. One is that you don't read any faster now than you did when you were an undergraduate or the monks did 400 years ago and yet the amount of knowledge is increasing exponentially. Which means you're getting less and less of what's relevant. This is an unsustainable model. There are 20,000 proteins within two hops of rats. There's no way you can understand everything that's going on even if you read all day all the time. You need help, I need help. But the medieval model is also enormously wasteful. Because it's so hard to read at the scale that we need to, the vast majority of things that are published aren't read. And yet we pay for them. What we need instead is what I'm going to call a push model. In the push model everything that's published is immediately consumed by a computer and its implications that is what it tells us about what we know are immediately calculated. When somebody publishes a paper it goes into a big mechanism that's maintained by machines. And in that big mechanism we calculate the ramifications. And that's what human scholars study. Not the papers but the mechanisms that are built by machine. And if this idea works it's going to be the first demonstration of a completely new kind of scholarship where instead of pulling things into our heads we push them out to the machines and I think that could change scholarship profoundly. Thank you very much. Do I have time for a couple of questions? Thank you. Are there any questions? Yes, in the back. How often did we find overt contradictions in papers? At the moment we only know this from human annotators and we know that there are enough to be worrisome. I can't give you precise numbers. By the end of the summer I'll be able to tell you that for a thousand papers. Now I also have to point out that those are contradictions that are recognized by machine. And what that means is that some contradictions won't be detected and other things that are detected won't actually be contradictions because the machine doesn't do a perfect job. But I think we're going to be surprised. How do you deal with the rate of false positive and false negative results or conversely true positives or true negatives? There were some reproducibility studies that said that biological mechanism studies from academic labs reproduced in industry had about a 10-20% validity rate. And probably negative results or those results that aren't found have an even lower rate. How do you account for that? That's a good question. The science isn't perfect and sometimes people screw up. It's not overt. Sometimes people overstate the case. They give you impression that this is a general result and actually it's a very tightly preserved result. There are all sorts of ways that the knowledge can be wrong in one way or another. How do you do it? You compare what's asserted to what you know. And the more you know the better you are able to judge the veracity of what's asserted. That's one of the reasons that we think big mechanism is a valuable program. The bigger these models are the more the more knowledge there is which to compare any given new result. Thank you for the very illuminating talk. Second time I hear you. Every single time I hear you I think we're so wrong on just doing it by hand basically. Using human heads only. So I have one comment and one question. I do believe that you're right that we are reaching to the level of knowledge that no single human no matter how intelligent they are is going to be able to tease out things. Even in this program that we are in this symposium we generated a ton of data and we're finding we're finding some new stuff because they're very obvious. They're basically hitting us on the face. But I'm pretty sure that we're missing a bunch of stuff because we have models in our head that don't contemplate a new information that is more subtle. So this will be very welcome. So my question now is like I'm so glad that you're working with Frank because we don't have to be cleaning up the mess. When do you think this system is going to be able to ingest more than just Russ? And how do you intend to give this information out so we can use it? Good question. So it gets back to what I was saying earlier about how much you have to know about something in order to read well. There's really some stuff in the system that's specific to RASP, but not very much. So it's not a RASP specific system and if it were then this program wouldn't really be a success because it's supposed to be about building big mechanisms generally. So when can we give it out? I think by the end of the summer. I mean actually a lot of the software is already available. The Manchester folks I believe make their stuff available. The University of Arizona folks. James Allen's parser is available. Putting them together into a system that's easy to use is a bit more challenging. I'm about to start a project on that basically making it easy for biologists to construct their own pipelines. But I think it's better than six months. Okay, well let us know. We'll let everybody know. Yeah, and we would like that because that's the best way to test the stuff. Thank you. In literature there are two experiments done earlier like Deep Blue in IBM learned to play chess. Then Watson project where they learned to play Jeopardy. In both of them there was a huge learning effort was there. Years of very comprehensive learning and learning what made the rule predictable and responsive. So in your project what is the learning component and what's the outlook there? Well I spoke mostly about reading. I think that there's going to be a lot of learning in assembly. Because we have to learn for example what are plausible rules. I gave a sort of a very preliminary example of that. Rules that mention these sorts of things also mention those sorts of things. I think there's going to be a lot more learning there but right now on the machine reading side it's learning, learning, learning all the time. I mean we're going through here's an example. Learning paraphrases. So if you go to gene ontology for any given thing it will give you sort of three or four paraphrases. We're learning hundreds of paraphrases for anything in gene ontology. We're learning of hundreds and thousands of different ways of referring to the same thing to try and handle the co-reference problem. So learning is absolutely essential to this project as is understandably big data. Thank you. Hello. This is very exciting. We curated a database called GeneSigDB and we've curated about 7000 papers as part of that. One of the things that we've run against when we've tried to do much, much simpler text mining is we're restricted to PubMed Central. So I'm very excited to see large projects like this will there be a push towards open source publishing? Absolutely. And how is... Well I mean we DARPA can't steal people's papers. So we're the same thing. So we're working only with open source papers right now. It is however a real limitation. So what do we do about all of the papers that are not open source right now? We have no plan. We just don't know what to do about it. Is this going to produce a new way of writing papers? Well I certainly hope so. Because if we understand language technology well enough and if we understand what a machine needs to know about what a paper says, then we can start imagining easy to use markup languages that go far beyond keywords to talk about the content of the paper. And I skipped over a slide that talks about a little bit about that, but if you wanted a break we can talk more about it. Yeah, no it's very exciting. There's a lot of restrictions with card gene ontology. Thank you all very much. Thank you. So we'll move on to our first session of the morning and we're going to hear about an update from the popular renal cell carcinoma by Dr. Marston Linahan.


Well, this past weekend in Gull Lake events put on its annual Gull Lake Frozen 4 in Niswa, which included events all weekend for the whole family. And as our Clayton Castle tells us in this week's Northwoods adventure, it's all for a good cause. It may have been cold and snowy this weekend, but that didn't stop people from going to the Gull Lake Frozen 4 in Niswa for a weekend full of fun activities and events all to benefit two local organizations. And it benefits Confidence Learning Center and Kids Against Hunger in the Brainerd Lakes area and it's just a phenomenal weekend of fun that raises money for two great causes. The event usually takes place at Hole in the Bay Lake, but due to the warm weather conditions, events were moved to a tent on land in Niswa. The frozen flop was moved to the tent with people jumping in ice cold water instead of a frozen lake.  Saturday saw the traveling art pub where the locals showed off their art skills and the golf event where people toured golf holes all across the area around Gull Lake in order to win prizes. Each hole had a special game attached to it like this one where players had to beat the dealer in a game of golf blackjack as the theme of the weekend was Vegas Tour. Later in the night, everyone gathered back at the tent for a night of food, drink and live music performed by the bands The Why Not Band and Steel River Band. And finally, the last day of the event saw the food and craft beer fest where hundreds of people came out to sample some of the finest food and beer that the Brainerd Lakes area has to offer. With so many events spread over the weekend, sometimes it's hard to pick a favorite. Just ask Matias from Chile. I'm a chef so I do like this event in the morning, but I like, I'm Latin, I like the party and I like the party on the tent on Friday night. And for some, it's easy to pick. The frozen plop, it's growing, it's so much fun and to see how people really get into it and dress up for costumes and the whole weekend is just kind of this euphoric weekend really with everybody getting so involved with that plop is really cool. Favorites aside, the entire weekend was filled with fun, laughter and most importantly food and drink all helping a great cause. Reporting for Northwood's Adventure in Niswa, Clayton Castle, Lakeland News. Looks like a lot of fun and while numbers for this year aren't in yet, last year's Gull Lake Frozen 4 raised more than $45,000 for the Confidence Learning Center and Kids Against Hunger. If you've enjoyed this segment of Lakeland News, please consider making a tax-deductible contribution to Lakeland Public Television.


Good day. This is the third session on the Zambian tax benefit micro simulation model micro Zamod and in this session We're going to be looking at the tax and benefit policies in a lot more detail We're firstly going to start off by understanding how tax benefit policies are constructed and in this instance We're going to look at functions and parameters. We're then going to look at how to run the model and access the output So to give a brief summary Systems contain groups of policies policies in turn are constructed from functions and Functions perform calculations to create the model output So again, we can look at this diagrammatically So in micro Zamod version 1.4. We have two systems. We have the 2010 system and the 2015 system But as I've mentioned work is currently underway to incorporate the 2016 and the 2017 system We then have groups of policies. So we have policies on income tax. We have a policy on the social cash transfer We have a policy to policies on pension contributions, and then we have a policies on bats excise and other duties We then have functions which perform calculations to create the model output So for example in our social cash transfer that policies made up of a number of functions 60 and to be exact, but within within these functions, we have I mean within within this policy we have functions which perform eligibility test and a function that assigns an amount calculation so functions are really the building blocks of all policies in micro Zamod and the combination of different functions from the full set of The functions allow for the construction of almost any policy So functions can be classified in three categories. We can have policy functions for implementing tax and benefit policies We can have system functions for implementing the framework of the model And we can have special functions which are really used and are not used at all in micro Zamod So we have ten functions in micro Zamod. We have four policy functions We have and these are Elig Ben calc, which stands for benefit calculator Arrathop and shed calc We then have six system functions And these are upgrade deaf far deaf IL, which is income list deaf to you Which is tax unit deaf const which is constant and deaf output, which we've seen in our previous session So each function consists of a header displaying the name of the function and a switch Telling us whether the function is activated i.e. switched on or deactivated a switched off or not applicable Which is an NA the function is then divided into a series of parameters So we can have many parameters appearing within multiple functions and some parameters being specific to particular functions So most of the policy functions and some of the system and special functions provide common parameters They are compulsory compulsory and optional parameters For example the parameter tax underscore unit must be included in all of the policy functions Otherwise micro Zamod will issue an error message and this is because if we don't include a tax unit the model won't know who to assign the benefit to or who to Assign the liability of the tax for example So micro Zamod is told how to calculate the policy by setting the parameters of the functions to appropriate values So here we have an example of our social cash transfer in rule areas And again, I'll be going through the PowerPoint first and then moving over to the live version of the model to demonstrate the concepts So as I said earlier our social cash transfer for both For rule areas at least is made up of 16 functions It's made up of one depth bar function two LH functions, which stand for eligibility One arithop function which stands for arithmatic operator and 12 bencalc functions, which stand for benefit calculator So if we look at the first LH Function, which is number 11.2 on the spine We can see that this perhaps this function is made up of three parameters or there are three parameters in this LH function We can then see that there are switches Next to the function both the function and the policy and these are currently switched on so Model and look at our social cash transfer for rule areas We can see so we can make this a bit bigger so that it's a bit easier to read So if you click if you go to the bottom right hand side of the main user interface And where it says text size and click on the little plus sign there You could make the writing a bit bigger so it's easier to see So here we have our social cash transfer for rule areas We have as I said, it's made up of 16 functions 11.1 to 11.16 on the spine We have one depth bar function, which specifies our intermediate Intermediate variables that we're going to be using in the model in the in the policy rather We have two elige Functions which specify eligibility. We have 12 Ben calcs, which Actually calculate a living conditioned index, which is similar to a proxy means test in order to assign The benefit we then have one are thought which simply scales the score The scores generated from this calculation from these 12 Calculations scales a score to make it range between 0 and 1000 And we have two final two Ben calcs to assess eligibility so if we look at those briefly I will go through this in a lot more detail in the final run through of the model in session 5 So the final Ben calc assesses all the eligibility condition for the rural Areas and then allocates the standard monthly social cash transfer amount and Then we have our output variable, which is bsa underscore s and all of this is done at the level of the individual We then have a final Ben calc, which is an additional payment for households containing one or more disabled people so we have our Eligibility for for rural recipients of the social cash transfer and on top of the Criteria specified in the Ben calc above we have one more criteria, which is ddi 0 1 is equal to 1 which Indicates that there's one or more disabled people in the household so if Individuals in rural households fulfill this criteria. They are then get it. They are then allocated another amount Which is double the standard social cash transfer amount again, I'll be talking about that About this policy and the rest of them in a lot more detail in our final session So this is just to give you a brief overview So our function elige as I've mentioned already Elige determines eligibility And it sets a variable to one if an individual is eligible or zero if they are not based on one or more conditions It must contain the parameters elige underscore con and tax underscore unit and The conditions may be simple. For example, we can have DAG which Is a variable For age being greater than or equal to 60 or we can have built-in queries such as is depth child Which is basically asking if the individual is a dependent child Each condition is enclosed by curly brackets and conditions may be combined with with and which is symbolized by the ampersand Sign and or which is symbolized by a straight slash The components can include variables in cumulus numeric amounts functions and footnotes and And the conditions can be assessed at different levels and again, I'll speak more about these last two bullet points in a later session So here we have an example of implementing the social cash transfer using elige. So if you remember So here we have an example of implementing the social cash transfer using elige So if we look at our elige function, we see that is made up of three parameters We have our elige underscore conned. We have our output underscore var and we have our tax underscore unit And if you look at the criteria specified in the 2015 system and In our first parameter elige underscore conned. We see that we have a variable called dhh equals to 1 So again, if we wanted to find out what this variable was We would use our data requirement document and look for this variable in that document But for the purposes of this power point, I'll just have this Video rather I'll just tell you what the variable is so The d as you'll remember is the broad category of the variable and then we know that in your mod and Microzam mod that d stands for demographic variable So in this case the d stands for demographic at demographic and the hh stands for household so this First criteria is saying that in order for the person to be eligible to receive the social cash transfer They have to be the head of the household. So that has to equal to one indicating that they have to be the head of the household and The second part of the Criteria states that dsd has to equal to one again d broad category of variable Standing or meaning are the demographic variable and in the sd stands for same district So in this case The individual has to have resided in the same district for 12 months and if you look at our comment section, you can actually see that we've stipulated These variables In the section so dsd equals one means that the individual has been in the same district for 12 months and Dr. U is equal to naught is a is a demographic variable looking at whether the Household is in a rural area or urban area. So in this case d r u equals to naught equals means that the household is in a rural area Whereas if d r u was equal to one which is the case for the social cash transfer in urban areas This would mean that the household is in an urban area So again, we can go over to the model and look at this first Elige So here it is on the model. We've got our three parameters In our elige function, we've got our three criteria for eligibility for the first part of the eligibility Which I already explained so after An individual fulfills all of these three criteria an output variable called i underscore score rural underscore 12 underscore m Is generated and this is an intermediate intermediate variable that we will use throughout this This policy in order to calculate The final eligibility and then again our tax underscore unit is at the level of the individual So that's Our first elige function We then have our arithop function and our arithop is just a simple calculator It must contain the parameters formula and tax unit a formula again can include variables income lists numeric amounts queries and footnotes and The main operators here are brackets and plus minus multiplication and division. So just normal Simple calculator and the queries automatically calculate particular conditions for example number of children in the household or carry out tests for example whether or not a person is married and If you remember our built-in query that we saw in our previous In our eligibility when we looked at a lot at our eligibility We can also similarly have queries in our arithop function So footnotes are used for example for applying upper and lower limits to a function or variable or specifying a level And this this is denoted by a hash So here we have an example of implementing the social cash transfer using our arithop and I've spoken about this briefly But I'll go back to it on the model and look at it there So we have one arithop, which is number 11.4 on the spine And in this case what this arithop is doing is it's scaling the score the score generated from This In effect proxy means test with which is the living conditions index So it's scaling the score to make it range between zero and one thousand So what it does it it takes the intermediate variable called I underscore rule underscore live under underscore score Adds a number of 1854 just a simple addition and then divides by another number 6.904 and all the reason it's doing this is just to make it scale To make the the score range from between zero and one thousand. So this is just a simple Calculator and in the output variable, which is generated is called I underscore rule underscore live underscore score underscore scaled and all of this is done at the level of the individual So again, we've just seen that on the model So the last function that we're going to be talking about is our Benkalk So Benkalk combines the functionalities of Elige and arithop and this is particularly useful for benefits Or social grants and is often referred to as the benefit calculator as previously mentioned So there are eligibility conditions similar to our Elige function called comp underscore conned and formulae To calculate the value of the benefit assigned to eligible eligible individuals or tax units and these can either be called comp underscore per Elige or comp underscore per Tu which is tax unit So again the same rules for syntax apply as for Elige underscore conned in Elige and formulae in arithop So it's just combining the two those two functions So Benkalk can have multiple conditions with different amounts being assigned for different conditions And this is what makes it particularly useful is that instead of always using Elige and arithop together you can just have a Benkalk and Have multiple conditions with different amounts being assigned for different conditions And the eligibility conditions and corresponding formulae are Specified in separate parameters So here we have an example of implementing the social cash transfer using a Benkalk So I think I'll go over to the model to explain this one So in this Benkalk work combining the functionality functionalities of both Elige and arithop so our comp underscore conned is our eligibility criteria for rule areas So as I mentioned earlier on we have a one condition stating that the individual must be the head of the household Which is DHH equals to one? We have another condition specifying that the individual must have resided In the same district for 12 months or more and then in that we specify that intermediate variable called I underscore rule underscore 12 underscore I mean underscore 12 m And so if that is equal to one that means that they have been living in the same district for 12 months or more and Additionally, we didn't look at this because it's in the second Eligibility function, but we also have a fit for work ratio test and the output variable that we generated from that The intermediate variable is called I underscore rule underscore fit underscore for underscore work And if this is equal to one this means that they meet the they have passed that fit for work ratio test in other words That there are no adults who are fit for work So we have three those three criteria For rule areas which are all initially defined in the first two Elige functions So if I can open those up Very briefly we see that we have all those criteria and in addition The fit for work Ratio tests so we've combined all of those into the single Bend calc Additionally, we have one more criteria, which is that the scaled score which we generated from This living conditions index has to be less than 460 and The intermediate variable that we use in this regard is called I underscore rule underscore live underscore score underscore scaled again When I go through the entire model and go through every policy step by step, this should be a lot more clearer than it is now so if a individual Fulfills all of these criteria then they are allocated the standard monthly cash Social cash transfer amount per household, which is 70 kwacha per month and the output variable which is generated is called BSA underscore S B standing for benefit SA standing for social assistance and underscore standing for simulated and all of this is undertaken At the level of the individual and the amount is allocated to the head of the household so that's how That's how we've Implemented the social cash transfer using a Bend calc So the last function that we're going to be looking at is a function called shed calc So shed calc base basically takes a base amount and then calculates a schedule of rates for example income tax bans So again, I'll move over to the model to explain this one as well so if we go to our Income tax policy Which is number nine on the spine if we open that up go to our third function Which is our shed calc. We see how this function has been implemented in the model So what this function does it takes a base in this case our base is called ttb underscore s It then applies a series of tax bans on the income so for example if an individual is earning between zero and 36,000 quatcha per year they pay zero tax So if you earn between zero and thirty six thousand quatcha per year you pay zero tax and in the next band Once you start earning thirty six thousand and one quatcha to forty five thousand Forty five thousand six hundred quatcha then this next Band of income is taxed at at zero point two five so 25 percent and then From forty five thousand six hundred and one to seventy thousand eight hundred That next portion of income is taxed at thirty percent So as soon as you start making forty five thousand six hundred one this to seventy thousand eight hundred You then taxed thirty percent on that portion of income and Then the last band is when you start making seventy thousand eight hundred and one quatcha per year and above You're then takes taxed thirty five percent on that portion of income and Then so the model calculates all of these Various tax applies all these tax bans to the income and then generates a variable called tin underscore s Which stands for the t stands for tax and the ions stands for income Underscore s stands for simulated and all of this is done at the level of the individual so I've already gone through How The schedule of rates to income tax bans is applied in the model So Now we're going to look at running the model So micro zermod output is based on two inputs an individual and household micro data and The rules on how to calculate taxes and benefits stored in the content file So using these two information sources the model calculates all taxes and benefits that have been implemented The calculations are carried out for each individual and household in the data set and the result is written to an output file The simulated output is at the individual is at the individual level unless specified otherwise So in order to run the model we need to go to the run micro zermod, which is currently still called run your mod Button in the top left corner of the user interface to activate the run micro zermod dialogue box So then a list of systems which are ready to run Will will appear and we need to select a system for running by checking the box to the left of the system So the list also provides a drop-down box for each system which contains all available data sets kindly We only have one data set that we're using in micro zermod, which is the 2010 LCMS so the output path path which is at the bottom of the Run dialogue box to find the folder where the model stores its output So once the required selections have been made click on the run button to start the simulation process So we can go over to the model and actually do this now So we'd go to the run your mod Button as it's still currently called Depending on which systems we want to run we would either tick or untick So in this case, I'll just run both the 2010 and the 2015 system and If we click on run and most importantly the output path is specified and Most importantly the output path is specified At the bottom of the run dialogue box. So then we can click on run and Wait for our model to run So while it's running, I'll just go through the rest of the PowerPoint So that's this is what we've just seen so all system data set combination selected for the running Are listed so currently we are running the ZM underscore 2010 system With the 2010 data set and the ZM underscore 2015 system again with the 2010 data set In the second column, we have the status of the run So the run can either be running as we we just saw it could be queued. It could be finished or aborted So On our PowerPoint we can see that the run has finished and we'll go over to the model in a second to look at Whether or not our run has finished But it's important to note that the time needed by the simulation depends on the PC processing speed the size of the data set and The complexity of the system So now we can go back and check if our model has finished running So as we can see our model has finished running and it took just under a minute to run both Systems so as we can see there's an error log But in this case there are no errors if if the run is finished and there's an error log It will simply contain warnings But if there are fatal errors that run will be aborted. So in our case that didn't happen So this is our error error log You can see we have no fatal errors So in micro ZM has finished its calculations the output is stored as one or more text files at the storage place Defined in the field output path, which is at the bottom of our run dialogue box the main text files called for example ZM underscore 2015 underscore STD as Defined in the policy output underscore STD underscore ZM And in this case is ZM stands for Zambia 2015 is a system name and STD stands for standard output The file contains the variable defined in the policy output underscore STD underscore ZM Output files can be viewed in a text editor program or imported into any statistical analysis package for example stator for more detailed analysis a Selection of summary statistics can also be calculated within the model using the statistics presenter application Which will be the focus of session five of this training the stats presenter uses the output file For example ZM underscore 2015 underscore STD The model also produces a header text file relating to the output file and error logs Relating to the output file or also stored as text files So now we're going to spend some time exploring the benefit policies together and In particular, we will look at in detail at the functions Elig arithop and Ben calc Thank you very much


What's going on guys, welcome back to another video. If you guys are new to the channel, make sure you guys hit that subscribe button. Today's video is gonna be the most wild prank I think I've ever done on this channel. If not the most wild, definitely top three. So as you guys can see by the title and thumbnail of the video today, I'm gonna be hiding a pregnancy test in my car for my girlfriend to find. The pregnancy test is gonna be used. So the other day I went to go get gas in my car and on the way back home, I ended up stopping at the pharmacy and I got this two-pack, double-pack, whatever you wanna say, pregnancy test. Now I ended up taking one of the pregnancy tests out yesterday and I dipped one of them in apple juice. So it gives it that yellow looking color, so it looks used. Obviously it says not pregnant unless the apple would have been pregnant. But yeah, I'm gonna take that pregnancy test and I'm gonna hide it in my car in a rag. Now Janice is upstairs right now, she's getting ready and I asked her this morning if she can help me clean my car because it's looking a little dusty in there.  So all the girls out there, how would you react if you found a used pregnancy test in your man's car? That is the most craziest thing ever. So if you guys are ready for today's video, make sure you guys smash that thumbs up button. Comment down below to see my day all day every day and let's get into it. All right, guys, we are in the garage right now. I have the pregnancy test right here with the rag. I'm gonna go ahead and wrap this up. I'm gonna show you guys real quick what it looks like though, so. So as you guys can see, it looks used, you know, with the yellow color and then it obviously says not pregnant. So what I'm gonna do is I'm gonna take this pregnancy test. I'm gonna wrap it just like this, kind of make it look like it's hidden and then I'm just gonna put it on my car door. Janice always knows that I keep a rag in the car because I'm always wiping down my interior whenever it gets dusty. I'm just that car guy, all right? I'm kind of like annoying when it comes to like keeping the inside clean and all that. I'm gonna set up the camera right now. We're gonna wait for her to come down. She should be coming down soon so I gotta hurry up. Camera is hidden. I got you guys on top of like this shelf ramp car thing that I got from my car. Anyway, yeah, camera's hidden. We are ready to go. I think I just heard her come downstairs. So if I know her, she's probably like getting something to drink in the kitchen or whatever and she should be here within like the next two minutes. I'm gonna be pretend to just be messing around in here and we just don't wait for her to come in. Let's get it. Forgot I had this. Run!  I don't have any more white gloves.  All right. I'm sorry. All right, so I figured the fastest way we could go about this is you do one thing while I'm doing it. Okay. So I was thinking you could wipe down the interior while I back in. You want me to clean the interior? You know- Nobody said none of that will blast, baby. Oh! Stay away from my glass. Oh wow! Yo, you believe in streaks like crazy. We not, don't touch my glass. All right, you believe in streaks in the toilet but you don't hear me saying nothing. Why you gotta take it? Yes! I don't understand how that relates. Anyway, so yeah, just wipe down the interior like the dust and all that. You don't have to use little sprays or anything. While you do that, I'm gonna go ahead and vacuum. If you're gonna start in the front, I'll go ahead and start in the back. Eh? Eh? You just woke up with jokes that they didn't you? All right, ready to team break. No, no. Don't be squeezing. No. That's it, that's it, that's it, that's it, that's it. Babe, you're delicious. Thank you very much. You can't have more sweats. There should be a rag in there already that you can use. This one by, is that one dirty? This should be clean. Where's the other attachment? Did you use the vacuum last? Where's the attachment? Did you use it? Probably dust. What is your problem? Why are you shaking? What is it? Why are you shaking? Because. Why would you leave a pregnancy test in my car? Because it's not mine. Also, I'm pregnant. Female goes in my car. Why would I leave a random pregnancy test in my car? I'm not pregnant.  I didn't tell you yet, I don't know who took me. My guess is that you did take it though and you just left it in my car and forgot about it. Cause you believe in everything in my car. You do, don't even lie. On your side? Don't even lie. You believe in everything in my car. Your sunglasses, your earrings. Okay. Why would I leave a pregnancy test in my car? I'm not pregnant. 24 seven, I don't leave the house unless I'm doing car stuff. All right, so you're just gonna keep lying to me? Like anything, I didn't do anything. Bro, I'm gonna tell you to tell me to tell me to break it. I'm gonna need you to tell me to tell me to. You need to stop playing with me. Stop playing. Did you leave that pregnancy test in my car? No. Okay, chill. Ask if he left it in my car. Why would he leave a pregnancy test in your car? He's the only other person that comes in my car. So. Why would he. And then the chip gave him that? I don't know. I'll ask him though. Like just chill. It's not mine. It's not yours. I'll figure out who it is. Relax. Figure it out. I am gonna figure it out. Relax.  You have something. I didn't do anything. But do you understand? I understand that. But I'm telling you right now that it's not mine or a girl that's fit in my car. That's what I'm saying. And I'm just supposed to believe you. Yes. Why? Why should I believe you right now? Because I'm not pregnant. Do you understand that? I'm not pregnant. Okay. It's gonna be left in my car. One more time. How serious is this? Is this a girl pregnant? No, it's negative. Then it's not that serious. Excuse me? It's not that serious if she's not pregnant.  Why? What does the window have to do with? I'm gonna bring something. Something like what? What are you looking for? You did not bring, huh? Plain is what? All right. Put the weed wacker down. Relax. All right. It's a prank. Relax. Relax. Put the weed wacker trying to use it on my car like that. You did not. I'm not doing this by myself. I'm not doing this by myself. It is too damn hot in here. And I'm not taking it outside. This is like 97 degrees outside. I'm not doing this by myself. I'll be always messing up my car. You know, for someone who does all the dirtying in my car, you sure don't want to clean it. I don't do any dirtying. Why don't I just leave my glasses in there with like water bottles? Glasses, water bottles, earrings, wrappers. I do not leave wrappers in your car. Germs, pregnancy tests. I didn't do that. You gotta admit it though. It was a good one. Yes, it was. You started shaking. Yes, I did. Shaking like a hooker. No. Yeah, just like a stripper. Say team Isaiah. That was a good one. It was a good one, right? I know. Wait, did you just give me credit? Maybe. Did you? All right guys, I'm about to go celebrate. That was it for today's video. Yo, we got her team Isaiah with another W. Is there anything else you want to say? No. I'm just happy that it wasn't real. I mean, like I said, it was not pregnant, so. I don't care. All right, anyway guys, that is it for today's video. I hope you guys enjoyed it and I'll see you guys in the next banger.


A Wabashaw woman has died in a head-on car crash in Little Falls after a wrong-way driver struck the vehicle she was in. According to the state patrol report, alcohol appears to have been a factor in the crash. The patrol says a car driven by 70-year-old Lana Jean Nornberg of Little Falls was headed southbound in the northbound lane of Highway 10 Monday afternoon. Nornberg's car side-swiped a semi-truck and then crashed into an SUV driven by 52-year-old Patricia N. Gurchy of Bemidji. Eighty-six-year-old Dolores N. Miskal of Wabashaw was inside Gurchy's vehicle and died at the scene. Gurchy had non-life-threatening injuries and was taken to St. Gabriel's in Little Falls where she is currently in stable condition. Nornberg was taken to Central Care Hospital in St. Cloud. Her current condition is unknown. The Minnesota State Patrol report says alcohol is believed to have been a factor in Nornberg's role in the crash. The driver of the semi and a passenger in the semi were not hurt. If you've enjoyed this segment of Lakeland News, please consider making a tax-deductible contribution to Lakeland Public Television.


because this has probably the most notes that I've seen out of any fragrance that I've ever come across. Hey what's up guys it's Josh here today I want to do a little review on this Dua fragrances drowning in blue de savage fierce. Now apparently this is kind of like a mix up between blue de Chanel savage fierce and I think Annie Nechaine. Now Annie Nechaine is the only fragrance in this mix that I have not tried it. There's an element in this fragrance that doesn't smell like blue de Chanel savage or fierce so I assume it's that Annie Nechaine. It's almost got a little bit of a creamy vanilla vibe and when I looked up Annie Nechaine apparently it's very known for that vanilla vibe. I contemplated not even writing down all the notes for this fragrance because this has probably the most notes that I've seen out of any fragrance that I've ever come across but let's jump right into it. The notes we got here are Sicilian lemon, Calabrian bergamot, green notes, black currant, spearmint, pink pepper, grapefruit, petite grain, orange, turquoise rose, blue ginger, isoey super, black pepper, Szechuan pepper, lavender, nutmeg, fur, aquatic notes, geranium, rosemary, jasmine, sandback, labanum, cardamom, patchouli, sandalwood, vetiver, benzoin, ambergris, oak moss, brazilian rosewood, incense, elami, ambroxen, vanilla, lily of the valley, sage, cedarwood, and white musk. Now anyways guys this fragrance just kind of smells like a lot of things in one. It's really kind of hard to describe or it's really hard for me to give you like any specific nuances. I smell a little bit of fierce in the background. I smell a little bit of savage at times but to me it overall smells kind of like a general likeable new age blue style scent. Now it doesn't really smell to me like Percival or like First Instinct blue but in my opinion it's in that same ballpark.  Not overly unique but it's very likable, very new, very fresh, very blue and it's kind of weird because fierce and savage definitely do not have a blue vibe and I would say the blue vibe is probably one of the strongest things I smell here but if you consider that Annie N' Shane apparently is blue. Also Blue D Chanel is blue. It kind of makes sense why it definitely has a very very noticeable blue vibe. The pros is that it is very very inoffensive. I don't really think anybody's going to have a problem with this fragrance. Even people who generally tend to dislike fragrances it's kind of got this middle type of projection. Not really too too light but definitely not beast mode so it's just very very likable. At times you're probably going to get little hints of fierce and savage coming out just a little bit of nuances but I don't think it would be enough to trigger somebody's memory of oh that smells like fierce or oh that smells like savage. It might get a little bit of a tint in there but overall it definitely smells like its own kind of blue new amalgamation. Another pro of this is that it is creamy and fresh at the same time. That's probably the most unique thing I could say about this fragrance is that it has this it pulls off this creamy vibe. You don't really think of blue fresh and then also creamy but this fragrance actually does it and that's probably my favorite nuance here. And then the last pro is that I can smell the little nuances of all the fragrances that are supposed to be in here so I do kind of appreciate that and it's nice like you'll smell like a tink of savage.  Overall it doesn't really smell like any one of those to me like again it smells the most like a blue style fragrance more than any one but I do appreciate that. You can pick out the little bits of fierce and savage in here and I do appreciate that. Alright guys let's jump into the cons of this fragrance. The main one I have is that it doesn't really have enough character. Now I got to say when I first started spraying this I was pretty unimpressed just because my standard for fragrances is so high especially with duo fragrance. My favorite version of Abercrombie and Finch Fierce is Duo's version of Abercrombie and Finch Fierce and they also have Fierce LaTar which I really really liked but most of the community seems to like Fierce LaTar much more than the original Fierce so they have two bangers there and then they also had Fierce Supernova and I just prefer Fierce Fierce LaTar and Fierce Supernova because I feel like this has the least amount of Fierce. I almost feel like they should have probably lowered the amount of combinations in here just to kind of fine tune it because it almost feels like it doesn't really need a savage or I mean it seems like you should have gone like savage or fierce. When you try to mix some the two it's just savages fruity spicy and broxen and fierces it's just it's on two different ends so it's kind of weird putting them together. I felt like Fierce Supernova was a much better combination of blend with fierce and I actually think it smelled a lot better because it was only those two fragrances and again this isn't a very bad fragrance at all. I know that when I looked up reviews online people were saying that this was unbelievably high marks five out of five fragrances, compliments you know as much as you could get just any good praiseable thing you could think to say about this fragrance. People have said the things that people say about this fragrance it might as well be the best fragrance Dewey has ever created however I would go personally Fierce number one, Fierce LaTar number two, Fierce Supernova number three and this number four. I will say that this definitely has pretty low projection as well when you compare it to Fierce and Fierce LaTar but I would say this probably projects just a hair more than Fierce Supernova although I do probably prefer Fierce Supernova overall. This kind of has a light to middle projection at least on me. I've sprayed quite a bit of this fragrance. I actually haven't done a fragrance review in like a week or a week and a half so I've literally just been spraying only this fragrance trying to get more of nuances with it and again I've grown to kind of like little bits of it but it overall to me doesn't have enough character. It just is a little bit of a blurred mess to me personally although it's probably one of the best blurred messes that you can get but at the same time it just doesn't have enough character for me personally and it just smells like a general New Age blue scent. Probably the best situation for this fragrance is the gym because it is likable. You get all these nuances from these fragrances and it's also not overpowering as well. It's probably perfect for the gym. For a date I mean this is probably pretty good for a kind of like a day date. It would be pretty solid for the office. I mean it would be okay for the office that it's light. It just has a little bit of like a sexiness and a freshness and a lightness to it that you know maybe you'd want something a little more robust for the office but it's just a great everyday you know bright weather style fragrance and I definitely think it is worth checking into if you want a fragrance with pretty much all these fragrances in one because I do smell them all and again I haven't smelled the Annie Nechain but this fragrance has almost like a vanilla creamy vibe to it that I would assume is from Annie Nechain. So anyways guys if I was going to give this a smell rating I would give this between a 7.5 to an 8 personally very solid middle of the pack. Likeability I'm going to have to give this an 8.5. I just don't see very many people disliking it. The only reason I wouldn't put it up to like a 9.5 or a 10 is because it doesn't have like a like a super complementable DNA that I know of yet like a latent or a eventus or a fierce you know it's I don't know if it's up to that level where you're gonna get these crazy crazy compliments but I'm almost positive nobody's gonna have a bad thing to say about this one. Long gravity and projection I'm gonna have to give this a solid 7 and I've said this a bunch of my videos before but if a fragrance is really really bad I will rate it as low as like a 4.5 to a 5 for longevity and projection so I give this one an even 7 so anyways guys I don't really recommend this as much for people who like fierce but if you like I said if you want a fragrance that really smells like all these into one definitely check it out I knew I was a little bit wary when I heard about it I trust Dua usually Dua is just fantastic any of the ideas that they come up with I was kind of wary about this one and I guess it did what it said it was gonna do it doesn't smell bad but to me I just would like maybe one to two fragrances blended or maybe a different blend than this because it just comes out a little bit too bland and blue for me but anyways guys let me know what you think of Dua fragrances let me know what you think of this fragrance down below I'll probably be looking to get the cold in version next or pretty soon I think that's one the one of the only other Abercrombie Dua's that I have not tried but anyways guys we're on the road to 50,000 subscribers and I couldn't do without any of you guys help you guys are the best I'm having a great day out here hopefully having a great day at home see you all in the next video peace


Anyway, Steve McFarland here with Mountain View Dodge. What I have here is a beautiful 2015 Chrysler 200S. Gotta love the block, all tinted windows, 8.4 screen, very nice automatic headlights, power seats, nice lots of room in the back. Like I said, it's a 15-200. It's got the sunroof. It's ready. It's road ready to go. So, new and used, you're going to come to Mountain View Dodge. Who are you going to see? Me, Steve McFarland. Have a great day.