Alright. Thank you. Alright. Welcome, everyone. I hope everyone's doing well on this afternoon. For those of you who don't know me, I'm John Burto. I'm the Associate Provost for Faculty Affairs, and it really is my great pleasure to welcome you to the 2023-2024 Distinguished Scholar Teacher Lecture Series. For those of you who don't know, the Distinguished Scholar Teacher Award was established in 1978 to recognize tenured faculty members who are committed to and have demonstrated excellence in instruction and in their research efforts. The award is sponsored and administered by the Office of Faculty Affairs on behalf of Provost Jennifer King Rice, and recipients are selected by prior DST nominees and recipients. I'm very pleased and honored on behalf of Provost Rice to recognize Dr. Chris Laskowski as one of our newest Distinguished Scholar Teachers. As we will hear more shortly, his scholarship focused primarily in model theory, which is a branch of mathematical logic. In addition to his research, Chris has contributed significantly to the university's learning environment, engaging students in courses he teaches and through his mentorship. Provost Rice and I congratulate Chris on his this much deserved award and recognition. It's my pleasure now to introduce Doran Levy, Chair of the Department of Mathematics, who will formally introduce Dr. Laskowski. Thank you. Thanks John, and thank you all for coming. I'm Doran Levy, I'm the Chair of the Math Department, and it really is a great pleasure and honor to introduce Chris Laskowski, our new Distinguished Scholar Teacher.  But nevertheless, I'll still mention a little bit of that just to, you know, honor Chris yet again. So Chris received his PhD from Berkeley in 1987, after which he was a more instructor at MIT. And then he joined the University of Maryland in 1989 as an assistant professor doing mathematical logic. We heard that. Mathematical logic has been a big, there's a big tradition in our department for mathematical logic. And at the time that Chris joined us, how many logicians were there? Four? Five. Okay. Missed by one. Gradually, that number went down. And over the years, as people gradually retired and were not replaced by new logicians, not that long ago, our logic group numbered one. So if you look at old departmental reviews, the report of the logic group was, we need more people, right? Which actually happened as we recently hired two logicians, Christian Rosenthal and Artem Chernikov. And now from one, we have three and probably one of the best or certainly one of the best logic groups in the country, if not in the world. So small numbers doesn't mean low quality. Actually, when it's concentrated really, really well, you can do wonders. Okay. So I mentioned something about the trajectory of Chris coming to Maryland. But Chris has been nothing short of a wonderful colleague. So being a wonderful colleague is a combination of many things. A great researcher, so wonderful mentor, teacher, and someone that's in charge and involved, greatly involved with our outreach activities. But really a great person to have around. He's here pretty much every day. Not many people are. And just seeing Chris in the corridors and welcoming him and greeting him and him greeting everyone, it's, I mean, how should I put it differently? Chris is a fixture of this department. Okay. So what Chris does for research is something I'm not going to attempt to tell you about, which sounds mostly like my letters for the APT committees. And I think that actually Chris will also not try to make this attempt and he promised to give us a very accessible talk. So I think all of us know what averages mean and we learn about averages of averages. But let me just say again that I'm super pleased that Chris has been bestowed this award of a distinguished scholar teacher by the provost office and by the University of Maryland. Congratulations, Chris. And with that, Chris Laskowski. Okay. So hopefully, people on Zoom can hear as well. So thank you, Dr. Boteau. And thank you, Doron, for the wonderful introductions. I'm very honored to be here speaking in front of all of you and everybody on Zoom. I'd like to also just thank both Doron Levy and Larry Washington, who were both previous DST winners and they nominated me for this. So none of this would have happened. And in particular, I'd like to thank my wife, Carol de Francis. Quite frankly, without her constant love and support, much of my career would not have transpired. So thank you. Okay, so before launching into things, there was discussion right at the beginning. Today is mold day. Happy mold day to everybody. Again, since most of you are mathematicians, you might not have heard about this. You can view this as being a chemist knockoff of the pie day. Think for mold, think 6.0 times 10 to the 23rd. And I sort of cryptically wrote this today is 1023. The 2023 is redundant or whatever. So unlike just reciting lots of digits of pie, one tends to sit around until mold jokes. This will play a part as we go along. So as to what I'm going to talk about. Well, can read from the thing my main field is model theory, which is a branch of mathematical logic, which is a branch of mathematics. Great. But this is really a lousy thing. Say at cocktail parties or elevator speech, it's really a bad thing. So number one, someone says, what do you do? I teach. What do you teach mathematics? The usual thing. The majority. Oh, I really hate math. Many times they'll say, I got up to level X and then I had a really bad teacher and then I can't imagine what life's beyond that. Okay. But then there are a few people that say math. Okay, yeah, what kind of math. And then you say mathematical logic. And you can see that they tense up. Oh, no. And like, look down at their hands. They're sure that the next words out of my mouth are going to be this statement is false, or some sort of a paradoxical thing. They're looking and trying to remember what's the symbol, the Vulcan greeting from Star Trek or something like this. For whatever reason, popular press, mathematical logic, certainly discusses paradoxes or in and around girdles in completeness theorem, or the collection of all sets is not a set. Or Bertrand's Russell a barber can shave everyone's head except as just all sorts of things. And when I teach set theory, I try to make a really special thing. I think the term paradox is very poorly aimed. It isn't a proof that zero equals one or that things are all falling apart. But rather, when you see something paradox it means you need to be careful. It means warning date something non intuitive is going to be happening. Rather than bore you with my research, I want to give an instance of something in popular press which goes is a paradox, but one should really view and try to see what is going on because it really as well demonstrate has a lot of real world applications. So with that as a preamble. Let's start out and let's talk about baseball. I said real world, but we'll get to more things beyond that. But let's take two players, David Justice played for a while, many years with Atlanta Braves and then moved, moved on to two other teams, whereas Derek Jeter always played with the New York Yankees. What about them. Well, let's look at the years 1995 and 1996. So you start with David Justice. And many of you know about statistics and baseball but one of the most popular things is batting average. So you take the number of hits. So in this case 104 and divide by the number of times he comes up to the plate the number of at bats. It's just a ratio. And in this case comes out to really should be 0.253 but no one says the zero. So he say he bats 253. So clearly the larger the number, the better off every you're doing. So in 1995, David Justice had a higher batting average than that Derek Jeter. Now you can note here that for Derek Jeter, the number of at bats was somewhat lower. This was in fact his rookie season and he got called up during the year. So he didn't have that many at bats. But then now we passed in 1996. And once again, David Justice had a higher batting average than Derek Jeter. So in both years, Justice's performance dominated that of Derek Jeter. But if you then take the two years combined, if you take. So the combined things say for David Justice. He had 104 hits in 1995, plus 45 hits. And then divide by the number of at bats is. 411 plus 140. So this comes out to the 149 over 551. Or in other words, a bad combined batting average of 270. But as you can clearly see, Derek Jeter's combined batting average is 310. Now at first this should seem kind of odd. How could it be that David Justice dominated Derek Jeter in both 1995 and in 1996. But when you combine these guys, it switches. Okay, well this is sort of what we want to be discussing with this. Now, why does this feel strange? Well, suppose we just have numbers. So we have a big number A1 and a smaller number B1 and another. So A1 is bigger than B2. A2 is bigger than B2. Then from that, if you add the two big numbers together, you get something bigger than the sum divide by two. So the average of A1 and A2 is bigger than the average of B1 and B2. Whenever A1 and B is bigger than B1, A2 is bigger than B2. Great. So, but this is not what you're doing with batting averages. Remember batting averages are hits versus at bats. So when you're getting this combined thing, same computation that's here. You're taking the hits plus in 1995 plus the hits in 1996 divided by the sum of their at bats. And in general, this is certainly not equal to the latter. Think about the right hand side is, as in my title, the average of averages, but this left hand thing is not. And now, I guess, when I gave a talk like this before Larry Washington pointed out, this is very close to we spend the math faculty spends a lot of time teaching freshmen that this is not how you add fractions. There's a half issue, but even without that. Okay. So keep that in mind. So that the thing that I want you to go for going forward is the care must be taken when averaging averages. So we're going to get to more sophisticated things than this, I promise. But this whole thing this flipping that's occurring with this goes by the name of Simpsons paradox. And from my general comments, I'm really kind of unhappy with the word paradox here, because it's simply that we need to be aware of what's going on. So, now I guess this being the new fangled thing. What do I mean Simpsons paradox. I do not mean Homer. It is not named after Homer Simpson, which is he might like it to be but rather much earlier Edward H Simpson of 1951 from from the UK, a rather noted statistician at the time. Always whenever you name something in mathematics there's some ambiguity, and some people call this the Simpson you'll effect from much earlier you will in 1903 was a sort of aware of this kind of thing and certainly I prefer the word effect to paradox that just saying something is happening here. Now, so this is just something you should be aware of to start off that just when you're looking at data sets of baseball certainly provides lots and lots of data sets, then stuff like this can happen. So, in the wild, which to as a chemistry just means that just just in nature without any sort of contriving things. It's relatively rare, but it does occur. And just other baseball pairings. The most recent I could find was actually two Red Sox players Ellsbury and Lowell who had the same phenomenon for two years in a row. One dominating the other I forget which was which you can always check these just if you just literally Google your favorite baseball player stats, you're just going to get a listing of these. And you can do these on your own. Somebody painstakingly went through cases of all star players and found these just among all stars very familiar names to baseball fans. The last ones a little remarkable with Babe Ruth and Lou Gehrig. These were the first three years of Gehrig season career and he Gehrig actually beat Babe Ruth in batting in three years 1923 2425. But if you add up all of those together, Babe Ruth dominates. Gehrig in the in the sum of the three. Okay, so there's more to it than just baseball. This can happen in the wild and it can have some real world significance.  You're the small town. And your specialty is kidney stone treatments. And now a patient comes in woman and just huge amount of pain and clearly as a case of kidney stones and you want to know what to do. What should what procedure should you do to to help ease her kidney stones. Well, you're in 1990 and you're on top of your game. And you've read the following thing. This is at the time it really was the gold standard for what you do with treating kidney stones. There was a long multi year thing all in the UK, published in the British Medical Journal, and it compared. And a lot of words renal calculate calculi is of course kidney stones.  This other percutaneous I'm not going to embarrass myself but let's call this PN for the second method.  And now this third newer thing was this extra corporeal. So in other words outside of the body the ideas you get a machine, and you just hit it just with a shock wave. And the idea is to try to jiggle things around enough that you'll just pass the stone. But that requires extra equipment. And you're in this small town. Remember, so that's out of the picture. So you really don't have the equipment. So you're either going to treat this patient by open surgery, or this PN method straw like method. What do you do? Well, you look at this paper, and it's very clear. The data says that open surgery succeeds 78% of the time, whereas this PN straw treatment succeeds 83% of the time. Done deal, right? Accept that. If you go in and ask, does the patient have small kidney stones, then this treatment, the open surgery actually beats the going in for a straw, 93% to 87%. And on the other hand, if the patient has large stones, it's more problematic, so the probabilities drop. But still, once again, the OS treatment, the open surgery dominates that of the PN. So in either sub case, if the stone is small, you should use open surgery. If the stones are large, you should use open surgery. But if you don't know the size of the stones, then you should use the straw. So now seriously, imagine you are this doctor. What do you do? So which treatment do you use? And then even a more basic question to you is, should you even bother to check whether the stones are big or small? Because if so, it will maybe confuse you about what to do. Okay, so it is curious that in this paper, this really gold standard paper, they don't discuss this issue at all. They just have just the facts. Here's the table. Here they are. Now admittedly, they were rooting for this extracorpial thing, and much of the paper is discussing that. That was the newfangled thing, and they're discussing the pros and cons of it. But just in reading the paper, this Simpson's idea just isn't mentioned at all. So the question, yeah? Well, but then if you're going to go to the danger route, then certainly open surgery would presumably be worse, but in either case, it's dominating that the success rate is dominating that. Anyway, so I can imagine that each of you, being a doctor, can have different opinions about how to answer this, but at least it's an issue. Okay, so let's continue on. But sometimes, and this is going to be the bulk of the talk, what appears to be Simpson's effect can really be a hint at some missing causality in the data set. And if I've learned one thing in preparing this talk and just thinking about things for a number of years, at some level, statisticians really don't understand causality. And even what the definitions should be for it. This isn't necessarily a failing, it's just a really involved problem, and there are a lot of traps. Okay, great. So let's first take a completely toy example that will get things across. So question, should students study for a test? Yes or no? Well, let's do a scatter plot. We're just going to randomly, so the number of hours study of the x-axis and the score on the test is the y-axis. And we're just going to look at these various thoughts and what do you conclude? Here's all of the data. You get a best fit line. And clearly, things are not good with studying. The best fit line is decidedly slopes down.  Now, you can almost anticipate from the general shape of what I had here. Suppose I tell you that in this, all of these top guys are graduate students. And all of these guys are undergrads. In many cases, it's the other way, true, true, but let's, okay. Now, within these subpopulations, let's try to get the best fit, whoops, whoops, whoops, whoops, whoops, whoops, whoops. Sorry. Let's get the best fit lines for this. And clearly, if you are a graduate student, you should be studying for the test. If you are an undergraduate, then you should be studying for the test. But scrolling back, if you're a student, you should not be studying for the test. Okay, so one could easily look at this data set, this toy, and write two different contradictory, compelling papers about the conclusion. And in fact, the thesis of this, by means of subdividing what's going on, the same data set can be used to justify two different contradictory conclusions. Okay, so this was all a toy, got a good lap out of it, but this really came up, probably by far the most famous example was the issue of graduate admissions to UC Berkeley in 1973. So start off with the guts of this fact, in fall 1973, 44% of all male applicants to graduate school were accepted, but only 35% of female applicants were accepted. And this is a pretty huge data set 12,000, these are the precise numbers. So overall 41% were admitted, but eight, like this, this, if you do any sort of analysis, this is highly statistically significant. So just on a chi square value of this, this is 110 is huge. The probability that this could be happening by chance is very, very small. So this certainly would be in the realm of the legal system or something that should Berkeley be sued here, say for sex discrimination on this, on how they get in. And as you're going through this, certainly there exist many parallel situations where in today's world where it has entered the legal system. But given this, Berkeley was quite alarmed with what's going on. So just for a thought, think to yourself what could be happening to cause this. And the provost commissioned a report, which actually turned out the result of it, it became a seminal paper in statistics. It was published in science, and it was a big noise when it came out. Peter Bickel, all three of these were professors at Berkeley. Peter Bickel was a young, at the time, statistician and he wrote one of the standard textbooks for introductory statistics. Hamel was an anthropologist. I couldn't find the field of O'Connell, but they really studied what was going on. And it went one by one at the admissions data for each of the 85 departments on campus to see what was happening with this. And this all here is accurate data with it. Now, rather than go through all 85, the top six or the sixth largest departments on campus. And some of these, but A through F, you can see at first lush that there is some wild things. So first of all, in A, 82% women, 37, 34. This guy is very close to even very close to even so say, really the only big place where there's a big difference in admission rate is in A. But if you look at this a little bit more closely, one thing you're going to observe is that there is a huge difference by department in the number of applicants. Say number one, or the A is the engineering department and there were 825 people, males and only 108 females that were admitted to it. But the admission rate into engineering was sky high. It was, well, even I guess they favored women somewhat with this, but at about 70%. On the other hand, say in English, and this is either English or a compendium of English comp lit or something. And that there, there were almost twice as many women that applied rather than men, but note the stark difference. The admission rate was only 34, 35%, as opposed to up here in the 60s. So if you look at item C, there were 560 men versus only 25 women, but yet this was a relatively easy department to get into and so on. So there are big, big swings in the male to female ratio of applicants and the admission rate is by far from being uniform. Okay, and this is really what's explaining. If you go down to the 85, the departmental level, I'll now quote literally from the summary of the summary paragraph of their paper. So first of all, examination of the aggregate data take everything all together on graduate admissions to Berkeley shows a clear but misleading pattern of bias against female applicants. We have this huge Chi Square score of 110. However, when you break it down to the disaggregated data department by department. They, there were few decision making units that show statistically significant departures from expected frequencies in either direction and about as many units appear to favor women, as opposed to favoring men. What's happening is, is that women are we're we're getting tracked or we're applying to highly competitive departments, whereas men, on the other hand, we're applying to departments which accepted many more people. The ratio was was different. One thing that I found surprising just a couple of sentences down from this, the graduate departments that are easier to enter tend to be ones that require more mathematics in the introductory preparatory curriculum. And I'm curious, is that true at Maryland that again this was in 1973 and enough just really to state it that like engineering. I know here admits an awful lot of people but is the engineering acceptance rate much lower than much higher than than for English. And how do English and math compare. There's a lot of things that we can ask our associate provost here. This might again so does this does this final sentence still hold at Maryland in 2023. Okay, so let's finish with all of that and have to throw this in. What was Avogadro's favorite Olympic event. The mole vault. None of these are any good, but you have to throw them in every once in a while. Okay, onward. So now that was all 1973 let's skip ahead almost 50 years to a really odd thing at first blush with coven 19 in the early days of this. And this, this is brought put together by a blog post of Dana Mackenzie at UCLA and Jordan Ellen Berg, who is a professor of math at University of Wisconsin. But you can almost view him as being a hometown guy. He was went to high school in Maryland and was one of the winners of the his of the high school mathematics competition. He was a very bright guy. We don't want to put those updates, but, but also I also had the, that was before I came here, but I had the pleasure of actually teaching him. While I was at MIT I was teaching a graduate course and he would walk over he was an undergraduate at Harvard and came over to to take this so anyway, good guy. So let's give a couple of slides of data from the CDC from early on in in in the coven thing so things started well really really started going in March 2020. And now. We're going to be concentrating just the first four or five months. And I know this slide is hard to see, but we're just going to be concentrating here on the white non Hispanic cases, roughly a third 35% of the cases were were white non Hispanic. You can't read that up here is the Hispanic. It's almost the same and the black is here. Those are the main bulk ones, but by contrast. If you look at the deaths that happened between this among in the white non Hispanic category, it's gone from one third roughly up to a half. If you take a look at that this goes a completely against what what everyone was saying in the newspapers about about that somehow that it was the pandemic especially early on was really hitting all of the minority communities really hard. And it was having a profound effect there. But why is it that among the white non Hispanic, you could think privileged people, they had a third of the cases but a half of the deaths. So, what is going on. Let's try to answer that by looking at a breakdown of things again for this March to June CDC data. And here it's it's lots and lots of cases a million plus cases and 100,000 plus deaths. This is not a small data set by any means, but we're going to break things down by age. And among people in the 30 to 49 range, 26.5. So all of this is for whites, not non Hispanic whites. So, 26.5 of the people, the percent of the people 30 to 49. The COVID 26.5% of the 3049 who had code had COVID were white non Hispanic. But in that range, even though we had 26.5% of the cases, the deaths were only 16.4%. So, if you look at this a little bit, a little bit more closely, you'll see, well, first of all, in the zero to four thing, thankfully, there were very, very few cases, especially early on. I read somewhere that among the 100,000 deaths 13 were in this category early. So, we can just sort of ignore this thing is really microscopic. And then just going line by line. Again, if you were white, then your probability of dying was less than your compatriots in every one of these categories. Okay, okay, the better outcomes are due to the privilege and the better health care and better diagnosis and these various finger things to see about your blood oxygen levels. But still, how does that explain if the whites are doing better in every one of these things, how does this explain the 35 to 49%. And the key thing here is not written with this, but white people are old. This can really be seen, not in the assembled room, but in the general population, 9% of whites are 75 or older, but of non-white only 3% are 75 years or older. So in these two problematic categories, and these were really where I now need to put better outcomes in quotes, because quite honestly, the death rate was absolutely huge. It really, again, the lion's share of all of the deaths were really just in these two categories. So what was happening was that just there were just so many more very elderly whites that that was dominating what's happening throughout this. And that can be just explaining this but but you want me to be really careful that if you looked at that first graph about the explanation is somewhat deeper than that then what might expect. Okay, so now continuing on. What do you get if you cut an avocado into a large number of pieces. Guacamole.  Okay, well we needed that after the talking about this and especially since there's going to be another maybe not completely cheery topic coming up. This is something known as the low birth weight paradox. So it was studied excessive extensively by Alan Wilcox, who was a researcher at NIH and research triangle. So, in order to get to this I need to define three things. First of all the median weight of a newborn is 3.6 kilograms and for years gone by a baby was labeled LBW low birth weight if his or her birth weight was less than or equal to 2.5 kilograms. And for the chart that's going to come the mortality rate is above these babies is the number per 1000 that do not survive their first year. So, a lot of data was collected on this and here it was split by whether or not the mother smoked during pregnancy. So, the mortality rate for 1000 so in the general population so just among the non LBW babies for maternal non smokers, the death rate was 11.1 so enough roughly a 1% chance of 99% let's be positive and 99% chance that that the baby would survive the first year. But among maternal smokers it's slightly worse than that. But if you go to the low birth weight things then 210 out of 1000 low birth weight babies lie to maternal non smokers. So, if the mother smoked during pregnancy then this rate would drop to 114 so roughly get cut in half. Okay, and this was published by this guy Yaro Shalmi and I'll talk about this was actually I would say an important paper in an odd way but at this moment you might want to think what is going on. And hint, not advocating that mother smoked during pregnancy. Yeah, yeah, yeah that's that's that's going to be that that's going to be the kicker and I'll be able to illustrate this by by a series of pictures, but good. Great. So what is going on with this. Well, let's start off just straight. This couldn't get anything else but this this is for birth weights in Norway but I think it's basically universal. So the average birth weight is like 3.6 kilograms. And this is very roughly a normal distribution. However, to the left, there's a tail, the tail to the left is much longer than the tail to the right. But fundamentally, it's a normal distribution as one would expect the numbers are large. What else could it be. So on this, doctors have arbitrarily determined that the low birth weight cut off is at 2.5 kilograms, which in the normal in the standard population cuts off this tail. Now, here, just the grim things to think about this includes almost all pre term babies, but also ones with genetic problems with just something wasn't right in this. So here I hesitate to write everything in red. It sounds like things are doomed when in fact the good news is really 80% of these did survive for at least the first year, but but it's really the flattened part of this tail. Okay, now, another thing that is known and has been tested many times throughout is the effect of mother smoking through pregnancy. This decreases the average birth weight, but it still happens a lot. And this this can be quantized. So it's known that mother smoking during pregnancy decreases the birth weight by approximately 200 grams. So forget the units on this, but roughly what's happening is we're getting this normal distribution, but we're shifting it to the left by 200 grams. So the peak is going to be 200 grams off and the same general shape. But then what really the problem is, in my mind, the definition of low birth weight is not changed, depending on whether or not the mother smokes. So as a result, if we cut off this thing, now there's going to be in the blue, a much bigger tail. This is exactly what you were saying that among mothers that smoke, there are many, many more low birth weight babies. But also, many, many of them are fundamentally healthy. I'm not saying that smoking is a good thing, but fundamentally, there's no, there's not much wrong with them. So if you're looking at a ratio, think you're just dumping in lots of fundamentally healthy, healthy babies, and you're putting them to the left of this divide as opposed to the right. So to summarize this, the point is, because of the shift in birth weights, well, at not making a corresponding change in the definition of a low birth weight, many more fundamentally healthy babies of smoking moms are put into this category. But among the low birth weight babies, so there's a greater share of fundamentally healthy babies, and that's what's going to cause that ratio to go down. So just to be clear, this does not mean that the mother's smoking is good for the baby, causing this shift to the left. However, this year, a show me who was biostatistician is trained at Johns Hopkins, moved up through the ranks, and then actually in the 1950s, he created the biostatistics lab at Berkeley. So he really had a national following with this. He was also a smoker. And this was right at the time, 68 to 71 was when there were just discussions about smoking that everyone smoked all the time, but but the CDC others were trying to cut back and the FDA warning labels were coming on. But he used this data, this fact, scroll back here. This is his data. He was using this as an argument in favor of mother smoking or saying it wasn't bad because look, here's actually a benefit. With hindsight, I find this quite shocking. But worse, this paper that he submitted got into the general press. And two titles that I managed to find in the Boston Record American, I don't think it still exists. The title, Mother's Needs Worry, Smoking of Little Risk to Baby from 1971. Worse, In Defense of Smoking Moms and Family Health Magazine. That was still, I remember that from being a kid, that this really got into the thing. So to see that this effect doesn't all have to be this smoking versus non smoking. If you want, say, a more a cheerier example of this whole thing. So this Wilcox, who's been studying this effect. If you just concentrate on babies that are born in Colorado, then maybe it's because of the elevation. Who knows, but the percentage of babies born that are low birth weight is significantly above that of the US population. But for any other purpose, they're just as healthy as in other states.  This I view as being a cheery thing. I'd say if anything, upon epidemiologists that the big problem might be that just absolutely fixing this low birth weight thing at 2.5 kilograms. The outcome heller high water is what's causing these these seeming seeming paradoxes. But in any event, just the takeaways, if you want to look at all of these examples taken together, the main takeaway I would have is that we're looking at a single data set. And then by doing various means of subdividing the same data set can be used to justify contradictory conclusions. I'd say beware of headlines or sound bites with this. If there are any journalism majors here that wants to really be careful about how to how to interpret this. And then finally to link it back to my title. In short, averaging averages can be a perilous undertaking. With that, thank you very much for listening. And I hope you'll enjoy the snacks and lacrosse and long.


Creativity is not a thing. It's the way you approach and engage everything. Creativity is not a product that can be bottled and sold. It's simultaneously the fuel and subversion of capitalism. Creativity is not an activity that you start and stop.  What do you think? What is creativity? And what is creativity not?


Hi everyone, Sean with jaspyscasebreaks.com here doing a series one hanger box, random team number seven, where at the end we're going to be giving eight spots away in the upcoming baseball mixer. One more hanger box and that sells out for the night and we can do that. But in the meantime, break number seven random team, here's a list of customers from Amber Bishop with the first spot, down to Sean Maddock and the teams from the Washington Nationals up to the Arizona Diamondbacks. Let's go ahead and roll the dice, five out of five ten times, one, two, three, four, five, six, seven, eight, nine and tenth and final time, Chris down to Allen. Now ten times on the teams, one, two, three, four, five, six, seven, eight, nine, and tenth and final time from the Cardinals down to the Cubs. Hanger box break, I will give you guys a chance for trades if you would like. Now all the teams are on wax, no talks of trades, so we'll go ahead and break. Series one baseball, hanger box, see the thing Tiger is on that for the top's finest flashbacks. Even if the team isn't something that you specifically like, every purchase team does give you a chance at winning the Chicago White Sox. Remember we're giving away the Chicago White Sox in the break, so so you might not like the team that's specifically available, but if you like the chance of hitting Louise Robert Rookies, I think Louise Robert Rookies are eventually, I believe are right now going for about $200 a piece. On average we were finding one of those per box in flashbacks. So no matter how much that spot is, you're buying it for the chance to win the Chicago White Sox and potentially whatever else obviously comes out for that team. So veteran Basin, this won't ship, obviously Rookies will, so that Abraham Toro obviously will be going to the Astros, Randy Arrena is going to be going to the Cardinals. See if we can find any of the bigger rookie names in here. Aaron Nola, Niko Horner, Turkey Red Rookie, Chicago Cubs, that's going to Allen Murdock. Jordon Alvarez, nice. Houston Astros, Travis Melberg. So got a couple of decent Rookies in there. But the most important part is the randomizer we're about to do. So we're going to take the original list of customer names, enter that in here into a new randomizer and the top eight names are going to be entered into the baseball mixer with a new dice roll five and a four nine times. One, two, three, four, five, six, seven, eight, and ninth and final time. Jacob down to Tyler Brenner. Congratulations, Jacob Adonis, Travis Melberg, Amber Bishop, Anthony Venturino, Chris Freeze, Chris Freeze, Darren Redding and Tyler Brenner. You all are entered into the baseball mixer coming up, hopefully potentially later on tonight. Just need one more filler break to go and we should be good for that. And that was 2020 Series One Hanger Break number seven random team. I'm Sean with jaspyscasebreaks.com and we'll see you next time.


Going on everyone Sam here with late in sports cards doing are not doing the random We're ripping two boxes of 23 leaf art of sport solo edition for Stephen. Oh, it's good luck here Stephen art of sport solo Edition go fancy box for this man. I got it Like all Mac Don't throw that away that would be good We're starting off with a job or an auto five of five. Hell. Yeah, that's pretty sweet. Take that all day Stephen We'll be coming back soon and next one here to see you really have a crazy look I know dude Lost everybody really bad What's Clark coming back? He's been out for like ever it seems like yeah, I don't know yeah for the rest of the season he is Another auto is gonna be Doc Gooden Nice one there. That is gonna be number One of two. So two super low numbered autos right there Doc Gooden. Thank you legend His number got retired by the Mets actually that'll do it for your break there Stephen Thanks again for the sport as always buddy get him right out to my


Hi everyone, my name is Gaurav and I work for an antique company from Indonesia that's called Xenius and I'll be talking about a very interesting case today that happened in 2019. So it's more like I used to hear in the news like a lot of times like Google versus Oracle and Apple versus Samsung those kind of cases but this is the first time and I heard like an open source project being sold for a bit in like written litigation. So that's why I wanted to share the story with everyone in the community around that if anybody has missed it and what went through this and like I've covered a lot on this case I followed a lot of documents and tried to read a lot about it. So I'll just try to share with you the story and what happened around. And also what kind of implications that this case has for the entire community. So let's get into this. Thank you. Okay, before this I need to be safe for myself because this is a legal case and I need to put myself on the safe side so I'll just read the disclaimer, which I stand by so the views and information presented in this talk slides are on of my own. So whatever I say I'll be right when we're like the opinions which I got from news articles and legal documents which I read for the presentation of this talk and anything I say or present in this talk or the slides is not approved or having any consent of GNOME or GNOME Foundation or my employer it is me who was presenting this talk and the research and views are there at my own capacity and responsibility. Okay, so before getting into the case I'll just talk a little bit about what's what's the business and what kind of business model that we are dealing with here. So basically, what I what I see is like anywhere you go and if you could see a scope of margin or profit where you could invest a specific amount or you take a specific risk, and you could see a scope of margin or profit that that sounds like a business right. So for around this concept almost every kind of business established. So similar is your business model which is called NPE. So NPE is short term for non practicing assertion entities or in the otherwise general public called them patent rolls. So I'm trying to be on the legal terms here so I'll not use the earlier term patent rolls to be on the safe side again. So basically, there are these entities whose core business model is using IP which stands for intellectual property. So they use this IPs to collect licensing fees instead of producing actual products. So usually people use IPs to protect their products and inventions and services for their businesses and they are not mostly into licensing fees. But instead NPE is there they their entire goal to get IPs just to get collect licensing fees, you know, like also get people into courts and assert for their patents, infringements, etc. So they could be a single investors with sufficient resources and they could have been public company, they could be at any scale, they could have a single patent to thousands of patents. So that's an NPE. So their business model is around this. So the risk here is that let's say if they invest in a patent and they could not find anyone to collect license from that. That's the kind of the risk they have. And if they find a lot of a lot of people around who are, you know, they could be infringing their patents, so they could go and ask licensing fee from them. So that's their marginal profit, which I was talking about. Okay, so that's the business angle to the side. Now let's get into the background of the case what happened. So now August 28 2019. Basically, there is this one called Rothschild imaging patent imaging LLP, which is headed by Mr. Lay Rothschild. This is a very well-known group known community for eligible infringement of this US patent number 9936086. I'll call it 086 was a big number and hard to remember. So against the very well-known open source software called Short Bell, which is being developed and maintained by the room community. So let's talk a little bit about what's Rothschild LLP. So you'll get to know the idea like where it's coming from. It's going to be on the safe side again.  So RPA LLP, I'll also use the short term for Rothschild LLP. It's again a big term. So RPA, they are having like almost 300 patent litigations in last five years. Their owner or like Mr. Lay Rothschild who is heading RPA, he is on the name and mentor of like 150 US patents. So you could imagine the scale. And when they showed been home, they were already fighting two more organizations in parallel for the 086 patent, that same patent. And by the end of 2020, they have already fought 11 organizations in total or individuals in total for the 086 patent, the same patent. So their patents are so generic or I don't know why, but they could find a lot of people infringing their patents and they have lots of patents with them. So you could see that where this is going and how many people they are, like how much they go for the litigations, how frequent they are with this. So that's the kind of organization which showed the GNOME community moving ahead. Okay, what's this 086 patent which I just mentioned. So just a little brief around this patent and you will get to know how it stacked this patent was. So basically any image capturing device, image capturing mobile device and if it could receive multiple plurality of images, photographic images and if it provides you like, you know, filtering criteria, which you could use after you let's say you say that based on this particular filter, whichever the images that satisfy this particular filter, I'll send it to the other device using this method, right. So this idea. So that's that's being covered by the patents, it's being claimed by its claim cause clause four in the patent document. So, and if you will think about it, it's very abstract, even if you let's say have a laptop you let's say select five or four images from your laptop, which are being taken by the webcam of the same laptop and laptop is a mobile device and you filter on let's say more modified date or something. And you send it over internet to some other laptop so that kind of comes under what's being over here. So, that's what being covered by the patent wait six, and you could read more about this patent in the documents, which I'll be linking in the slides. So, this was the main idea which was being used at the short well itself itself. So if you read the features which are being mentioned at the short well. The short well allows you to import files from images from a disc or camera right so camera is a mobile capturing device again and if you are getting it from from a different device to so you will be importing it to let's say laptop. So again it comes under what the patent claims. So that's where they got it and also they were when I saw that patent litigation documents, they're mentioning that it allows users to share to major web services and that means wireless as well. So they're also they try to say that you know this point infringes are patents claim. So, in these points they try to challenge short well in the court. So when the known got this this case for them so they obviously needed help cause in US the patent courts are very patent litigation costs are very high, which is this model against comes in. So, if you have to fight a patent litigation, it's it usually costs around $1 million at the very start, and it could go up to $25 million and like in millions of dollars it goes. And when you have heard of like very famous patent cases like Google and Oracle they are of billions of dollars of scale. So that's why it becomes it is like no community will ever like to go after such go and fight such cases. So they offered them a settlement and like they offered a choice if you want to face in the court face them in the court or like, you want to face a roster in the court or you want to like settle down for a less amount. At the same time, you know, had this choice there, which I'll make and the resources are also limited because the norm is a NGO NGO and non profitable, not NGO non profitable come organization in US and running on donations as well. So, fighting a case that requires millions of dollars is not so feasible for them, but the kind of help they got it, it was like, it was like very kind of you know, you find water in desert like that. So, here, there are three main allies which are the norm, which caught, which were helping the norm. So first was OIN. I'll talk a little bit about when in the later part of the slides on my talk. So OIN help uncover prior of this patent. I'll talk about prior art as well. Sherman is Sterling is there is another entity which is a law firm out of US. So they offered a pro bono representation pro bono representation in legal word means that you are you as a legal firm is offering your services for free to society for societal cost to some organization or individual. But the reason behind is that out of that case which are helping out it is societies and benefit and as we know that open source is already very beneficial for our society and society. So, helping open source project ultimately helps out the society. So that's why they offered the pro bono representation for free and it was the like main reason by this cost of 4 million or millions of dollars got down very less. And the third entity was the community which we have a lovely community. So GNOME reached out to the open source community for help and it was received very well around the community they raised around like 150,000 dollars in very less time and over 4,000 individuals they help out for raising this amount. And plus one of the biggest point here is that which makes this case very important that even this law firm which is like very well known with the patent litigation word they also heard for the very first time that NP is doing an open source platform. So it's like that kind of unique in the sense. So that's why that's what also make very interesting this case because NNP is doing an open source platform is very rare and for me it was unheard of and apparently for Sherman is telling also that it was unheard of. Okay, so open invention network it is also something that we in open source community should be aware of. So that's why I want to bring this topic as well here. So open invention network is like a company that acquires patents and licenses them royalty free to the members who are a member of open invention network. So they also agree that they will not use the patents against any Linux or Linux based applications and applications and the projects. So it's like a patent bank so where you deposit your patent if you're the member and you will be having access to all of other other members as well and you will you will agree that I'm not going to use it against your project things like that. So for which are related Linux and software Linux related software. And so this is the one of the strategy which started on and these days when also uses a strategy where they try to work with us PTO which is the patent granting body in United States. So when somebody applies for a patent in your US. So they have this mechanism where they work with OIN and they try to see if the patent that's been applied for grant is having a broad scope or not and could in future you know disturb any open source project or not. So if OIN finds that that the patent that the idea which is being applied for the patent is very generic and very broad scope so they try to get that application down. GNOME turns to be a member of OIN but in this case GNOME can take the advantage of OIN's patent bank or the pool and as well as this US PTO mechanism because the patent is already being owned by an RPI. So OIN has to help GNOME in a very different way because not with the traditional makers they have been helping the open source projects. So that's about OIN and now I'll talk a little bit about what's prior arts and how it helped in this case and also what are abstract ideas and what are some of the observation in US history for abstract ideas. So basically if somebody approaches you for the patent infringement and you prove that whatever the product you have it was being made before the patent was granted. So that actually not just helps you win the case but it also invalidates the patent of the owner who tried to accuse you of patent infringement. So that is a very good way to get out of any patent litigation. So in other words if this case if GNOME could prove that the shortfall was made before when the patent was applied it would simply invalidate the patent and GNOME can easily get out of it. And on the second hand when we talk about abstract ideas so any idea which is very general and it's like a natural feeling and like mental process so you cannot basically patent it. So that's where US Supreme Court ruled multiple times in many occasions of patent litigation that you cannot patent an abstract idea. There are 10 minutes left. Yeah, thank you. So when I was mentioning about the idea like GNOME could also prove that this idea is abstract like it's very generic to share images between devices and anybody could think of such an idea so it's not patentable. So you have like these two different ways on how on the basis of which you could fight the case. So here the thing which RPI was you know on the disadvantage was that the claim for where on the basis of which the LS GNOME for the infringement of the patent it was basically on 2010 it was patented and while shortfall program itself it was released in 2007. So this predated the claim invention so this covers the prior art point. So here from this point GNOME could easily win the case while the courts agreed basically on the abstract idea point as well which was ruled out with Supreme Court and if you will read the laws you know abstract ideas are also being in the patent laws it's somewhere written like you cannot patent an abstract idea as well. So it's kind of like a fault with the USPTO that they should not have granted the patent itself. So any point could have held out GNOME in the case but the court and the like I don't know which entity itself but eventually the abstract idea point was the basis of winning on this case because it also allowed for the litigation to get quickly resolved and the settlement gets done. Now after this, after this win the GNOME, GNOME sued RPI back for filing the counter claim. Initially they were in talks with Sherman their lawyers that we want the attorney fees back but due to some reasons they were not able to get the fees back instead they got something very big than just the attorney fees. So the result out of this case was that you know after some negotiations and discussion with the Rothschild GNOME got to get released out of this case and they got access to all of the Rothschild patents. So when I mentioned that Rothschild have over like 150 patents not just this 086 they got access to all of the patents and on top of that Rothschild agreed to give access to all of its patent to all of the open source projects which is the biggest thing I think out of this case. All of the projects which are approved by open source initiative get access to Rothschild patents they cannot come after any open source OSI approved project now and even in the future if Rothschild get any patent so they cannot come after the project. So this is the biggest thing I think like open source could celebrate our community could celebrate out of this case which I also wanted to highlight on this one. So yeah that's one the biggest thing I am really happy about to present you all. So the takeaways of this case are like you are not here about just winning the attorney fees back or getting out of this case. There are many lessons learned the community got access to a big pool of the patent and also OAN learned the lesson that you just don't need to collect patents and you know try to build a pool now the challenges of these NPs as well. So they hold a lot of poor quality patent abstract idea patents and they go after open source projects as well because this was the first time as well. I think OAN heard that NPs could come after open source project as well. They are like very soft targets for NPs because open source projects run on donations and they might not be able to fight such cases. So OAN they pivoted their strategy they are now going against Rothschild for invalidity of all its patents which is like a happy news for me. And they are also trying to go after such another NP entities. So this is the strategy change that they have taken off and also you I think like being legally correct for me this was a takeaway like you have to be legally correct. Like sometimes I used to mock the US press conference tech conferences like you know why the CEOs are being like very legally correct and like they're not saying what's the reality. But now I get to know that being legally correct is very important and as a developers like we take less care about laws and everything but I think we should take more give more attention to law as well. There are five minutes left. Yeah thank you. So and obviously you know first projects don't need to worry about Rothschild and now and this case also proves that patent system is broken in US and they must have some sort of a prime of ACI invalidity test before granting the project. And yeah you should join and go for the GNOME project or any other first project you'd like to do. Thank you guys. Thank you. So that's what I want to talk about. There is questions. Do you think this acted as a deterrent for others? Can you translate everything in your speaker? What does that mean? It means a thing that discourages or is intended to discourage someone from doing something so maybe it discouraged some others from doing so. Yeah so the thing with patents is that like patents are actually a very good tool if used in a right way to protect any inventors invention and they have proven themselves. But if you are trying to patent an abstract idea or like if your sole goal is to go after people and use patent as a tool to basically bully people and like try to make money out of without even offering anything to the source. So if you are trying to make money out of society or any product or service so that that is not a right way to do it and it discourages anybody I guess. And this particular case it actually encourages like open source committee that it actually proves a very big point that if someone tries to touch open source projects for random reasons and like just try to bully any open source project the community is there they support each other and will come out of it. That's the thing that I could take away from this this case and on your question I think it does not discourages it like and could you like please elaborate a bit of how you could see like want to specifically ask on this one.


Hi everyone, Steve here from ConflictSource.net. As you can see behind me today, there is a property that's currently experiencing a labor disruption. If your meeting was taking place here today, would you be ready? So the topic for today is to ensure that you're prepared for a labor disruption and three contracting best practices. So the first thing that is needed is a contract that speaks to the remedies of a labor disruption. Many times this is included as part of the force majeure clause, but the force majeure doesn't really speak to the remedies should something go wrong. Make sure you have language that allows you to make changes or even pull the plug on the event entirely in the event of a service disruption because of a strike. The second thing you want is to ensure that you're not contracting with a hotel that currently does not have a collective agreement in place with its employees. I would also recommend that if you are contracting further out, ask when the collective agreement that is currently in place runs out. You might ask yourself if you'd be better off contracting with a non-union hotel. I don't know I'm not sure that's a reasonable fix you know by trying to avoid a unionized property in today's market, but it's something you might want to consider. The last thing you want to do if you find yourself in a strike situation is to find out what other space is available and could be used to pitch if the venue that you're contracting with is unable to fulfill its obligations. If you find space get on it quick as other groups around the city are likely looking for the similar space. It's times like these where having a partner like Conference Direct becomes invaluable. We are able to source a lot of venues very quickly and find space that is available. I hope this helps in that you don't find yourself in a situation where a strike is imposed on the success of your event. We all want to ensure that employees are treated fairly and those delivering our conferences are treated with mutual respect from our delegates and their employers. It's unfortunate that employees seemingly need to strike in the hospitality industry. As always if you'd like to talk about this topic please reach out to Leanne and I and while we don't offer legal advice we can give you a first-hand account of what to expect behind the picket line. Thanks for watching. Look forward to speaking with you soon.