The following is a presentation of TFNN. Trade what you see with Larry Pezzavento. Now toll free at 1-877-927-6648 or internationally at<PHONE_NUMBER>. Now, Larry Pezzavento. Okay, looking good, Billy Ray, feeling good, Lewis. We have Tim Boss on today at 9.30 and tomorrow we have the mystery guest from Naples, Florida. Oh, I might have given it away. Norm Winsky calls it to the minute. We'll be with us tomorrow. We've got the full moon coming in here on the 17th. What we need to do now is to take a look at this DAX. You can see we're really in a place where we could go either way. The longer term, of course, is negative. The shorter term is actually a little bit more powerful, so keep a close eye on it. It could go either way. That's the main thing that we're trying to focus on today. A lot of things happening folks in the corn market. Of course, we had the crop report and it was a real stunner. I just want to give you a few heads up of what it really looks like. Here is the, this is what, let me just bring this up so you can take a quick look at it. This is from the Ag Resources that does a lot of research. You'll notice this is where we see that red zone there. Over the last 30 years, we've only been in that zone about three or four times. What we need to do is if you want to just research some of this, just go into your monthly charts like we're going to right now. We're going to take a look here at the corn and going back to 32 years, 1987. But you'll see that year of 1996, which was a key year. The corn went up four times in price. You see another one was in 2004 or 2005, I believe it had a big run. But look at what's happened here. We've been following this on a long-term basis quite some time here in the corn because we've had this 135 higher bottom pattern. That was the work of Roy Longstreet and his son Bill. It's broken out of the monthly charts now over the last four years. Folks, the price objective on this is $6. That could really be easy to make in corn if this weather stuff and planning problems keep causing problems. We're starting to see it in some of the other grains too. That's another one that makes us really wondering whether we're going to have bull markets and everything. We had a nice run in wheat yesterday. We had a nice run in soybeans also. Both of those were really starting to act relatively nicely, which is a good sign when you're looking at the grain markets as a whole. Part of this could be related, and I'm not saying that it is, but it could be related to this chart that's getting an awful lot of play on the Internet. That's the one that we showed you from Dennis Gartman a few weeks ago. This is from David Wilson at Bloomberg. We've just made a new low, again, in the commodity index. Look at this, folks. You've got a giant ABCD pattern forming here. Boy, it's just saying, please buy me. You can see the head and shoulders pattern that was formed from 1974 to 1990. Then the high was in 2008. Here's where we are now with a big ABCD pattern. Many of these commodities are getting ready to at least go higher, at least for some type of a bounce. That's what it looks like longer term. I can't say anything more than that, but that's it. One of the things that Mr. Z and I think Ruby and some of the other folks here, the tiger Dan yesterday was talking about, was the old red metal copper, Dr. Copper. If you take a look at this, we had a three-drive to a bottom pattern. Copper this morning is setting on a retracement from 270 down to 264, right at the 61% retracement. It's also the 78% retracement on the longer-term chart. This is a low-risk trade. If you want to be looking to buy copper and you think it's going to go higher, this is about as good as it gets, I think. That would be the main thing to see that's right. Peak is telling us that we're in this massive bull market in silver. We certainly could be. All we need to silver to do is to get about $15.50, and I think that you will be. I've been saying all along, anytime you see silver around $14 an ounce to buy those silver rounds, you know, those beautiful coins with the St. Godin's picture on them, minted by the U.S., other mints make them also, but it's a great way to own silver. That's a one-ounce silver bar. Every coin in the world knows exactly what their worth. They're easily assayed. All you have to do is take one silver coin and tap it with another, and it shows you that it is 90% silver. A very simple thing to do that they've been using this for thousands of years over in the Middle East is how they assay, you know, gold and silver. They just tap one metal with the other and it gives them an idea of what's going to be looking at these things as we go through. Now, we have a real interesting thing that happened in the S&P last night, folks. We ought to spend just a moment talking about it here because I think it's important enough to really take a look at it. Let's just get this up here so we can see it here. Last night, we made a really nice 382 retracement from the low on June the 3rd. Do you remember that big low that we had when we hit 27-28? That was an exact 382 last night at 28-68, and we rallied up to a 61% retracement last night, so this is going to be the key. If we break below that 28-68 now, that's a really negative thing, folks, and we're going to be going down in the stock market for some of the reasons that we've discussed here, which are divergences, et cetera, et cetera, et cetera. But last night, when the S&P was trading, it had a really nice expansion to those of you that are watching, and I know some of you were, had a really nice expansion to the downside on a 1.618 relationship, and it made it real interesting to see that it was exactly at that same price. That's the minimum retracement, folks, because it's taken five days to get here, and as long as we, if we can go above 2,900 now, we're going to be looking at a market that could go a whole lot higher. That's the main thing. Now, something else happened last night that was very interesting, and that was in the old crude oil. If you remember here, we've been watching crude oil for quite some time, and if we take a look at it here is where we were as of Monday. I just want to give you an idea because Ruby was on top of this last night. I lost as much sleep as you did, my dear, but it was well worth it. Anyway, you'll notice that 61% retracement that's sitting there, and what happened in the crude is just get up here so we can take a quick look at it. We'll update it so you'll be able to see it. We had a 2.5% move in crude oil last night as we matched the bottom of the last, we've hit it three times, we hit it last week, and then we hit it twice this week. So that level at 50-70, boy, you can put a circle on that puppy because if we get below that, crude oil is heading down. But anyway, right now it's looking pretty good. We had a pretty nice run in it overnight, and we're going to be able to see if it's working here. Folks are talking about the 200-day exponential moving average. Folks, some people, the reason why exponential moving averages work, I believe is because a lot of people use them. I mean, they're lagging indicators, but people that know how to use them, and I'm talking about people here at TFNN like Basil and Steve Rhodes and David and also Tom O'Brien. I'm not an oscillator person, I'm just a bar chart person. I try to keep it as simple as possible and within my pay grade, which is quite low on the civil servants, 18 being the highest. I'm about a number three, but I get by. That's what I can tell you is I do get by, but not as good as sometimes I'd like, but it's still okay. All right, we'll be right back and we'll talk a tiny bit about natural gas, and then we'll have Timbopost on. The Taz Profile Scanner is the most revolutionary piece of trading software that you will ever try. Wouldn't you like to approach the markets with confidence? As you begin your trading day, it's likely that you'll be faced with lots of decisions. In order to make the best decision, the first thing you'll need is a strategy that will help you minimize your risks. Whether we're in a bull or bear market, a good strategy is to have the tools needed to help you scan and analyze the markets before you trade. The Taz Profile Scanner instantly scans and filters over 2,500 global financial markets, such as stocks, ETFs, commodity futures, and forex. 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TFNN has launched our brand-new website. You can still visit us at the same TFNN.com URL, but when you do, you'll see a new and improved homepage with a much simpler navigation, whether you're watching Tiger TV live in high definition or just accessing your newsletter subscriptions. We even have new pricing in six months and yearly options. Check out the new TFNN.com now and experience all the upgrades. TFNN.com Educating Investors. Call now. Toll free at 1-877-927-6648. Internationally at<PHONE_NUMBER>. Okay, folks, let's take a look here at Natural Gas for Peak D. We want to take a look at it because we've made a 1.618 expansion and we've got a quiet little upside and downside. This is the lowest risk you're going to get for natural gas if you want to buy it because it's got a chance here to hold. The reason is because of that 1.618 expansion number. That's the real key that I'm looking at. Folks, when we talk about Elliott Wave, I really believe that Elliott brought a whole lot to the Elliott Wave stuff. I just have a trouble doing the counting. That's not his problem. It's mine. I just find it very, very difficult, but boy, he certainly did bring us a great deal. He was the first one to introduce the F-word, Fibonacci. That was back in 1938. Very few people knew anything about it. It wasn't until Robert Frost got in. It just stood with his little protégé, Robert Prechter, and then they made it started to get it famous there in the late 70s, early 80s. It's definitely got some great stuff. I do believe that they missed a few things, but you're not going to get anybody from Tarrot, Indiana to challenge anybody from Yale and Harvard and any of those other places, let me tell you. It's not going to happen. Nibahachi, as they say, in the trade. All right, we want to talk a tiny bit more about the soybeans because we've had a heck of a run here. I just wanted to show you we had this big run in corn, and if we look at the soybeans here, we also had a 382 retracement there. We were looking for them to come down to the 61% retracement, but they've already started to move higher. Now, they haven't gone nearly as high as the corn yet, but all of these are starting to move. You had a 20-cent move in beans yesterday. That's a lot of money, and the corn was not as much, but the corn's been the leader of the pack, so there's problems out there in the Midwest, folks, and they're continuing to get worse. The problem is that they're losing time. We're in the middle of June now, and there's a lot of corn that's never going to get planted. There's about 1.66 bushels per acre, but if we get that corn on a cob, it's going to be like prime rib. It's going to be pretty, pretty expensive, so it doesn't look good, and it could get a whole lot worse, and then what will happen is as we come into the harvesting season, it's probably going to be cold and wet, and they're not going to get what a little crop they have out there, so you've got to watch this. I'm not too sure about the weather market part, because if you get these explosive moves, I think they'll be relatively important. I've been asked to take a look at the old silent car itself, Tesla, and we're going to take a look at this now so that you folks can get an idea. It's a very, very tradable stock, and it's got a lot of action going on in it, so let's get it up here so we can see it. Boy, don't tell me I'm getting... Ah, no, we're okay. Here we go. Let's start off with the weekly. Here we are in the weekly chart of Tesla. You'll see that we've had a massive double top up there. It's the ideal double top, too, folks, because it goes up and takes out the previous buy by like a half a buck at 388 bucks, and then just rolls over. We made the big Gartley down here at the 78% retracement. It's pretty much where we're sitting right now, and so we want to be... Don't forget, this thing went from $15 a share for $390, so it's pretty much a high flyer, but the real important part of this chart is if we take a look at Tesla here on the daily. This is really... You could teach a whole class on technical analysis and pattern recognition here with Tesla. Let's just look at the first Gartley that we had way back in January of last year when it was trading at $340, made a double top in January, another one in February, broke all the way down to $240, $100 a share, goes up and makes a new high. You can see the multiple ABCD patterns that are there from March to May and May to July, and then you have the double top that occurred between August and also in November, and at that one you can see that you had the three drive to a top pattern that we like to see because that gives you so many ratios and patterns to fit together at the same time, and that's the real beautiful part of it. Then starting in late December of last year, the market broke really hard. After that, it made a very nice ABCD pattern into January, and as you can see, the cycle lows, how they were coming in at lower levels. You can see then there were 14-day cycles, very similar to the lunar cycle that we're looking at, and it goes lower, lower, and you can see that it's going to go down to the 1.3.5 pattern that sets up the ABCD structure that takes you down to, you know where, to 0.9. Of course, we went all the way down to 1.79, and then we had a pretty good bounce, and yesterday's bounce was very, very important, folks, and the reason why is if you look at this really closely, and I know you will, if you look at the high, the equal rally that we had back in April was an equal rally to what we had yesterday. With those ratios, you'll see that the 1 from that high was exactly 3.82, and if you take the intermediate high, that takes you to your 61% level, and then if you take a little bit of imagination, folks, and here's where it gets difficult, you've got to do the work yourself. Look at the low in March at 2.60. The market rallies up, it makes another low at 2.30, rallies up, and then goes down to, you'll never guess, 1.80. The low was 1.79. That was a three drive to a bottom pattern, and now we are at the proverbial moment of truth in Tesla. Tesla must get above 2.30 to remain its bullishness, otherwise it's got 1.75 to 1.65 written all over it without any trouble at all. It really does. This is how you learn to look at a chart, draw in the patterns. You start doing it to look a little confusing, but all you have to do is remember two things right out of Dr. Andrew Lowe's book. Markets repeat over and over again, and this repetition is predictable within limits, and that's why the markets are chaotic, but within the chaos are these random patterns that repeat over and over again, starting, of course, with the most important one out of H.M. Gardley's book on page 249, AB equals OCD, also known as the Thunderbolt. From that, all other patterns stretch out because that's all the market is ever doing, folks. It's either going up, down, or sideways, and looking at pattern recognition, you're trying to find out what in the world is it doing now? Sometimes it's a lot easier than it is at other times, but that's neither here nor there. We've only got another minute before we have our guest on today, which is always fun, and he had some great information to take a look at it. I need to check and see what the live markets are doing, folks, just to see where we are here. It's important that the S&P, if it's bullish, it needs to get above 2900 today. The gold needs to get above 1345 if it's going to remain bullish. The euro is starting to have trouble, folks. It's already broken, you know, one full harmonic down, and the euro now down to 80 pips from the high. So that's another one. That level that we had in the US dollar that we talked about in the newsletter last week was so very, very important, and thus far, it has not broken that. I mean, it's went down here and tested it three days in a row, and now the US dollar is starting to get a little bit of a bounce in here, so it's still got a chance, you know, to make that level up into here. So that's a key thing that's coming soon. We'll talk to you soon. Thanks. One, two, three, four, eight. Larry has just started his brand new service, and he's already delivering content to his subscribers on a daily basis when the markets opened and even on weekends. Each Monday, you'll receive Larry's written report that provides detailed commentary and a summary on the charts The key markets that he is watching during the day. This will be up to the date active trading information that will help you in your daily trading. In Larry's first week alone he sent out 25 charts, 6 videos and a full report to his subscribers in just one week. If you're a technical trader that uses patterns and retracements to trade, then Larry's service Fibonacci 24-7 is something that you must try. Right now, new subscribers can get a full 30 day money back guarantee. With nothing to risk, sign up now to Larry Pezzavento's Fibonacci 24-7 by visiting the front page of TFNN.com under Trading Newsletters. The path of least resistance is David White's daily trading newsletter and if you're looking for active trading ideas, then now is a perfect time for a 30 day free trial to this powerful daily trading advisory service. David uses his years of trading experience to offer his subscribers his trading ideas each morning in his path of least resistance newsletter. Using a combination of equity trades along with options, David keeps his subscribers up to date with all pertinent market information with intraday afternoon updates when warranted. Don't miss out on this great chance to get a 30 day free trial to David's daily newsletter The Path of Lease Resistance with no obligation to pay anything. 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The Art of Timing the Trade Chart is designed to help you when scouring the markets for stocks just beginning to form the trading patterns that many investors spend days, weeks or even months searching to find. And right now we're offering licenses available at only $79 a month. We are so confident that you're going to love this new charting software that will even give you a 30-day unconditional money-back guarantee. Don't miss out on this incredible new piece of software. Get your copy of The Art of Timing the Trade Charts today by visiting TFNN.com. Okay, folks, we're online with Tim Bost out of Sarasota, Florida Financial Cycles Weekly. Tim, we had one of your biggest fans on yesterday, Arch Crawford, and he said to say hi to you. He's a dear friend. He really is. Yeah, he sure is. He's a nice guy. Anyway, Arch and I were neighbors here in Tucson, and one of the things that Arch was talking to me about off-air was, you know, he's got this really big forecast for something really negative happening in the third week of June, and some of the forecasts that he's made, and he's really adamant about it.  I mean, some of them have been really amazing. Now, he's had others like Y2K, which didn't happen at all. I don't want to take any of your time. The Y2K, Mark Douglas had just moved here in 1996, and we would go to these gun shows every weekend, and Arch was stocking up with ammo and dried food, so he had these three big trolleys, and he asked us to go to the show with him to get two years worth of food, because he really thought the whole thing was going to shut down, so we went ahead and went to the show with him. He took us to lunch, and we got this about $1,500 worth of food. It was a little more than that, but on these trolleys, it was about 450 pounds, and we packed it into a van that he had rented, and then, about three years later, he gave all the food, of course, to the, I think he gave it to the Gospel Mission, I believe. It was still under, yeah, but because it was running out, but they took care of it. Anyway, he was wrong about that one. Do you see anything? Well, Y2K gave us lots of entertainment value anyway. It was a lot of fun, right? Yeah, yeah. Indeed it did.  Do you see anything around the third week of June that could cause a problem? Yeah, we do have kind of an intense configuration coming up with a Mars-Pluto opposition that kicks in late in the day on the 19th of June, and that's followed immediately by the summer solstice. So that's a pretty intense potential inflection point there. I'm not necessarily looking for the bottom to fall out, but I'm looking for some price consolidation following that period. Do you think there'll be some financial, you know, some territorial wars or conflicts during that time also? Well, certainly, the Mars-Pluto dynamic can provoke that. You know, Pluto is about power struggle, Mars is about action, especially military action, but we have to remember that with these Mars cycles, we get them once every two years. So, you know, this is not, you know, a once in a century kind of event. Of course, we get conflicts pretty frequently too, so it's certainly within the reasonable expectation there. Right there. Hello. Can you hear me? Hello. Hello. Are you there, Tim? I'm here. Hello. Yeah, I'm far. We lost internet out here in the desert, so. Oh, okay. Good. Go ahead. You were saying about the Mars-Pluto dynamic. It's still working here in Florida, as far as I can tell. It's hard to tell that these days are the internet. No. Again, with this Mars-Pluto opposition coming up in a couple of weeks here, we do have the potential for a price pullback. I'm looking more for a consolidation pattern rather than an outright crash coming out of that. Okay. Tim, I've always felt, and this is on my bucket list, that someday, in the near future, there's going to be a segment on CNBC and Bloomberg about financial astrology. Do you feel that could happen someday? Well, if they invite me, we'd be glad to make it happen. Put it in a plug. I used to be on TFNN, and when CNBC started back 20 years ago, I was on quite a few times with Ron and Sana and Bill Griffith and Sue Herrera, because they were old friends from California. But I mean a real one, like from MIT or Harvard or someplace like this, where it says, look at the statistics behind this, because my gosh, I look at some of these things that they don't work all the time, but my gosh, they work a lot of the time, and I think it's worthy of it. We've got a pretty decent track record with it, so it keeps me out of bed every morning. That's for sure. You're in the list of the top 10 and the Timer Digest all the times, and so is Bill Meridian, and so is Steve Rhodes, and so is Kari Sezmanski, and one other astrology, what's his name? Barry Rosen. Yeah, he's very, very good, too, so out of the 20, I think there were five astrologers in that group. That should be something, I think. They need to put a little star by our names in Timer Digest so people can recognize that. That would be a good idea. Okay, what do you see right now that's really interesting? Well, what we've got coming up about tomorrow, in fact, is a kind of a minor inflection point that's a precursor to this Mars-Pluto opposition we're looking at on the 19th. Tomorrow we've got a Mars-Saturn opposition, and as you recall, back in May we were projecting forward using primarily the Trans-Neptunian stations and the Mars second harmonic dynamics, and this is an important one here occurring tomorrow with the Mars-Saturn cycle. What we're dealing with is essentially a two-year cycle pattern with Mars-Saturn. The last time we had this opposition was on May 29th in 2017. We've got it coming up again tomorrow. The next time after that will be July 1st in 2021. About every two years, plus a couple of weeks, we get this configuration. This is all about action, which is Mars. We were talking about that a moment ago in terms of the proclivity for creating conflict and sometimes geopolitical events, warfare, things of that sort, but Saturn is all about limits and consolidation. We've got two contrasting forces at work here. Will the market consolidate or will be able to break out to new highs from where we are right now? This is kind of a testing point associated with that. Essentially what we're looking for coming out of this is some short-term bullishness and by short-term, I mean about a week coming out of this, and then followed by some consolidation after that as we get into this Mars-Pluto configuration and the summer solstice that I believe Arch was referring to with his work. When the summer solstice, that's on the 21st of July, isn't it? No, of June. This month. Oh, gee, let's try it again. Yeah, OK, so that'll be pretty big because that's one of the things that the ancients used to use, those solstice and equinoxes very, very extensively. Right, and of course, W.D. Gann paid a lot of attention to those cardinal points in the beginning of spring, summer, winter, fall, whenever the sun moves past those cardinal points in the zodiac, very, very intense times for the markets in general. Most of the cases, based on that alone, we look for a major inflection point and then we see other factors combining with it that intensifies the picture. OK, that sounds great. Any other thing that really looks exciting coming up in the next week or two? Well, I've been tracking Bitcoin and that's always worth some excitement along the way, and certainly with the recent rally in there, we've been paying more attention to it. Tim, take a minute here for a break and then will you be right back with us, please? There's a couple of questions that people are asking us and we'll talk about Bitcoin. Tim Boss, Financial Cycles Weekly will be right back<PHONE_NUMBER>. If you're in the CD market and looking for a secure investment, the Tiger First mortgage program may work for you. The security for these first mortgages are building lots in the tax opportunities zone in St. Petersburg, Florida. The Tax Act of 2018 set up tax-free zones across the country where you can build and hold for 10 years and pay no tax on the profits, which makes these lots valuable. The investment is anywhere from $30,000 to $75,000. 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Tim, we're talking about the cryptocurrency Bitcoin and I have a question. Tim, do you trade these things? I don't and I don't know anybody that does so I'm just curious if you do trade them. I am not an active trader in full disclosure. I do hold some positions in several of the cryptos including Bitcoin, Litecoin and Ether. So I have very, very small positions. Stay informed about what's going on in that world. Well, it's certainly a different world than most, that's for sure. You want to tell us what you're looking at with Bitcoin? Well, once again, I'm looking at these current planetary cycles in this case that Mars Saturn opposition that we've got coming up tomorrow. And one of the challenges of course with Bitcoin is that we have a very short-term trading history and what we like to do with our studies here in Astro Trading is do backtesting. The last time particular planetary configurations occurred we don't want to see what happened with any given market and we don't have a lot of trading history with Bitcoin so we're dealing typically with five or six iterations of different cycles and whatnot. But at any rate with this what we're anticipating is a little bit of a pullback in Bitcoin prices coming in immediately following this opposition and we're looking for a retracement back down to about the 6850 level probably by early next week. So a pretty sharp pullback but then this brings in what I believe is going to be a strong buying opportunity for a short-term play as well. Once again, we're looking at that potential of that summer solstice, Mars-Pluto opposition configuration coming in toward the end of next week as a potential consolidation point. So what we would be doing there would be taking a long position in Bitcoin at the first of the week and then playing it with a very, very tight stop with the expectation that the trade could run to the 19th of June maybe even into the following week about the 24th of June for a move to the upside. So we believe then it will be back to the $7,900 range about where it is now by the end of the following week. So it's an interesting little blip that we're looking at and it also coincides with the solar cycle that we tracked in our book, Bitcoin Astrology. That's available on Amazon and it gives a lot of the background work that we use in the analysis of the Bitcoin and cryptocurrency markets using the astrotrading methodologies. Tim, are you of the opinion that it's actually the blockchain technology that's important as opposed to the cryptocurrencies themselves? Do you follow that theory that we hear about? Not necessarily. I believe the blockchain technology is very, very important and has a wide range of applications outside of the cryptocurrency space. But its key role with cryptocurrencies is providing a validation mechanism for transactions and I think that's one of the things that makes it quite unique and the fact that it's a public dynamic there. So the idea of developing private blockchains for specific purposes I think is basically bogus. The strength of the blockchain is multiple copies of the same information that can be verified independently and so there's a lot of confusion around that. I believe it has a lot of strength as far as cryptocurrencies go and some positive applications elsewhere but everything's blockchain and forget the cryptos. How do you go about selecting which of the cryptocurrencies that you want to look at because I understand there's thousands of them. Yeah, we have four or five thousand something like that. Oh my gosh. I'm ignoring. The key thing for me is trading history and so Bitcoin's been around long enough and a few of the others have established enough trading history that we can begin to track that empirically so I'm not just jumping into cryptos simply because they're cryptos and there's three or four new ones every day. That's real. The question that someone's asking me, how do you select an exchange to buy the cryptocurrencies? Do you know the process behind that or how did you learn to start? There are a number of exchanges but one of the critical factors that I found, Larry, in protecting yourself with cryptocurrencies is to keep them off of the exchange. In other words, do the transaction and I like to use a physical wallet that is off the exchange. This is where most of the trouble occurs with bad accounting or hacking of exchanges and so I use something called a Trezor which is an independent device that stores the data relative to the cryptocurrency and so that takes it off of the exchange once the trade is on. Could you explain to the folks what a wallet is? From what I understand it's like if you had a credit card you had one of these little metallic money clips that keep people from hacking into it. Is that what the wallet is? Not quite. Basically with a wallet what we're doing is storing data and that can either be online or offline and I prefer the offline measure there but essentially what cryptocurrency transactions are all about is a long string of numbers and characters that's kind of like a very, very elaborate password that authorizes the transaction and there are just three pieces of information that we're concerned about, who's sending the money, how much and who's receiving it and all that's encoded into a very long, complicated number and so as we go about storing and transferring cryptocurrencies that becomes a critical piece of information. You don't want to lose that. And so a wallet is essentially a way of safeguarding those passwords if you will and again it can be done on a website, sometimes a website connected with an exchange. In my book that's a little bit treacherous so I prefer to keep those passwords stored on an independent, it's kind of like a USB drive sort of gizmo. Well Tim, I've known three people that have been involved in Bitcoin. One of the guys is a reporter over in London and then there was one of my students from Kiev, excuse me, from Sofia, Bulgaria, was there, he actually worked for Google for six months in cryptocurrencies and he realized that he didn't need Google and he went out on his own and gosh, he made a massive fortune and of course you don't want stuff goes from a buck or two bucks to 19,000, it doesn't take much to make that kind of money but it doesn't appear like it's the typical bubble that people originally talked about because it went 19,000 down to 3,000 and bounced up to almost 9,000 so that's not how bubbles act, they go to zero. So we're seeing a more mature market now and that's what interests me and learning how it actually behaves. We're still early in the game there but I believe it's worth tracking as we move ahead. Tell us more about your astrology book on Bitcoin. I know it's pretty popular but tell us about it. Bitcoin astrology is actually a compilation of articles on astrological dynamics using Bitcoin. I edited the book and contributed a couple of the chapters to it. Bill Meridian, our good friend, also contributed some to it. Christine Skinner, a well-known astrologer from the UK as well as Wendy Stacey also from the UK contributed their perspectives astrologically and then there's a fellow who works with Fx Street in Barcelona and he's Consola Mariera. He's one of my students and did some brilliant technical analysis that's included in that volume as well. The title is Bitcoin Astrology. It's available on Amazon and I recommend it. It's still preliminary in terms of the research in this area but we try to define what the parameters are and give some ideas of what we've observed with trading cycles with Bitcoin. It also includes over 50 separate horoscope charts relative to the history of Bitcoin for those that are into that kind of analysis. It's a great resource guide. Okay, stay with us. We'll be right back with Tim Boss. I'm certain you are or strive to be one of the best of the best at everything you do in life. It's the most common trait that we tigers and tigers share. If you're looking to become the best of the best when it comes to managing your money, let me teach you to do what most wealth managers can tell you can't be done, which is how to time the markets. I'm Steve Rhodes, author of Mastering Probability and for the last 12 months, Timer Digest has been tracking my newsletter signals which have earned me the ranking as their number one market timer in the nation for the S&P 500 for the last 12, 6 and 3 months. Timer Digest also ranks me as the number one market timer for gold as well. The fact is markets can be timed and I'll teach you the exact set of tools that I use that has transformed me into one of the best at what I do. Sign up for Mastering Probability today by clicking on the newsletter tab on the homepage of TFNN.com and get immediate access to workshops where I take you step by step how to use an extraordinary set of tools as well as provide great market calls too. Sign up today. If you haven't checked out the newsletters page of TFNN.com what are you waiting for? All of the TFNN newsletters are informative, up-to-date, affordable and a must-have for every trader looking to gain a competitive informational edge in today's markets. TFNN newsletters cover every aspect of the markets to offer you the very latest in market news. Plus, new subscribers get to test drive our newsletters risk-free for 30 days. From all aspects of the markets including stocks, bonds, metals, commodities and tech there's a newsletter to fit your needs exclusively from TFNN. Stay informed each day you trade and get that competitive edge that will help you stay ahead of the game. Visit our newsletters page by going to TFNN.com and click the newsletters button near the top of the page. TFNN.com Educating investors. Since 1984, Basil Chapman has been using the Chapman Wave methodology to advise traders of his expert market opinion. While originally hand-drawing charts from the late 1970s into the 1980s Basil noticed that prices under most circumstances virtually always had a certain number of legs to the upside before declining sharply. Later Basil found that computer software which included the standard market technical indicators enhanced the degree of accuracy in calling price turns as well as market trend calls. Thus was born the Chapman Wave sequence. Using the Chapman Wave methodology along with other indicators Basil Chapman advises his subscribers of his expert market opinion to pay with his opening call newsletter. Right now you can get a two-week free trial to the opening call Basil's daily trading newsletter by visiting the front page of TFNN.com. Cancel it anytime during that trial and pay absolutely nothing. Get your two-week free trial to Basil's newsletter of the opening call today by visiting TFNN.com. We've got a couple of books to refer to on Amazon. One is called Bitcoin Astrology. That's the one we were just talking about. Just go to Amazon.com. Look that up. My name is Tim Bost, B-O-S-T. And then also take a look at Mercury Money and the Markets. Another book that's available on Amazon that describes short-term trading is called Bitcoin Astrology. This book is called Bitcoin Astrology. It's called Bitcoin Astrology. It's available on Amazon. It describes short-term trading strategies with Astro trading methodologies. It's a pretty good reference to some of the methodologies and techniques that we use across the board. So I'd recommend those highly. Our flagship website is financialcyclesweekly.com. We have a number of subscription services there. We're also doing a special webinar and you can reach that at bit.ly. We'll get you to the registration page for our webinar. We're doing that a couple of times. One later today and another one this weekend. So you can pick the time that works best for you. We're going to take a little time then to explore what's working now in Astro trading and answer questions directly during the webinar. Thank you very much. Thank you very much. Thank you very much. Thank you very much. Thank you very much. Thank you very much. We look forward to your future offers as well. We appreciate you preparing for those sessions directly during those sessions. So if you want to get engaged with figuring out more about how this stuff works, we'd be delighted to have your company and one of those sessions. Tim, what's the charge of those at that webinar? That one would be absolutely free, so... Is there is a money-back guarantee on that? Yeah, double your money back on that one. definitely lock in because we are interested in learning more about the financial astrology and you've certainly helped us a lot here. So again, thank you again. We'll have you on maybe next month when we get closer to some of these big things that are happening and it's always a pleasure, Larry, to life hold to spend some time with you this morning. Always like seeing you too. You're a real man among men. We appreciate it, my friend. Thanks. Have a great day. That was Tim Boss Financial Cycles Weekly.


How do you look at Prime Minister Modi's leadership as well as Green Growth and Green Planet itself? Well, I think it's an ambitious leadership. I mean, it's a fantastic nation, a very big nation. But with the scale comes also a lot of problems and therefore transformation towards a more green future is probably one of the biggest tasks you can have on this planet. And it takes strong leadership and we can see from the discussions that we have today about leadership that the Prime Minister is indeed taking that on. I think there's a great opportunity here, especially in the collaboration and having that foresight to team up with a country like Denmark that's been working for 30 years on the green energy transition to see how can we actually combine the experience and the knowledge that's been here that's been working here with the great also experience and knowledge that exists in India to see how can we use that experience in an Indian context and that can only be done through collaboration. So by bringing the companies together and bringing the two nations together I think India will indeed move forward.


Good afternoon session. My name is Milita to the other which I come from the University of Brooklyn Finland and I'm one of the organizers of this meeting so I hope you're all enjoying it. And the first speaker in this afternoon session is last buckle from Rory in Bochum and he will be telling us about some machine learning and data driven studies on experimental data so thank you very much go ahead Lars. Hello everybody. Can you understand me okay. What's on the back. Yeah. All right. Yeah, thank you for giving me the invitation and to present here, a little bit of our experimental work. And I would like to start with a brief introduction. With a brief introduction in what we are doing at the chair for materials discovery and interfaces. This is the chair of Professor Alfred Ludwig and I'm a postdoc in this group. And what we do here is we do combinatorial material science. So we use combinatorial magnetron sputtering to create composition gradients and or also synthesize process gradients. We form high throughput characterization, we can for example go for structural properties using electron microscopy or x-ray diffraction, but also electrical optical and magnetic properties, as well as we are looking into shape memory alloys and also thermoelectrics. Let me briefly introduce you to magnetron sputtering. So on the left side you can, you can see such a magnetron sputtering chamber from the inside. And we have here four cathodes. This could also be like more than four, five to six, seven, eight cathodes. And on these cathodes you can place the base material from that, from what you want to create a composition gradient. We have then here in the center of the substrate. As you can see here in the video we are spraying or sputtering from individual sources simultaneously. And if we keep the substrate as a static state, then we can create composition gradients. So instead of having a single chemical composition in these kind of libraries on a silicon wafer, we can create continuous gradients of composition and if we then put a coordinate grid through them. Our standard grid would be a 340 roughly measurement points that can be characterized using automated high throughput techniques. And so then you have the gradients over the substrate area, you can screen them and identify for the property of interest and thereby map chemical composition to materials properties. The typical way how we describe the high throughput materials innovation cycle is we start with some kind of hypothesis for example from theory guys. And then we go to the lab synthesize a set of materials libraries, we perform a high throughput characterization, the properties that we can do I already mentioned. And so from this relatively large data sets then we can perform a data analysis. So first visualize the data look at the data try to identify trends, select a region of interest with a material that has the properties that we would like to that we are looking for, and then perform an in depth characterization of those regions of interest with more expensive techniques like atom probe tomography or transmission electron microscopy. And based on that, then probably you can, we can go ahead and process these materials libraries so you can achieve different processing states by annealing them for example or oxidizing them, and can repeat the cycle, and hopefully identify the new material of the future. And there are several opportunities provided by artificial intelligence in this materials innovation cycle, which is first of all to speed up the data analysis, but then also to visualize large data sets. So for example here dimensionality reduction is really a game changer because you can suddenly compare relatively large data sets and in the past it was kind of complicated to do that. Active learning is a good opportunity to speed up further the analysis. For example, we would like to identify synthesis composition property relationships, but also guiding of the experimentation is where I can help us. There are several use cases that we have been investigating in the past years today to present to you. And the first one is to identify the influence of process parameters on the microstructure of thin films. In order to. Yeah, let me start with this. So actually the magnet transporting process is relatively complicated physics. So you can see here inside such a reactor we have here the cathode with the target, which is the material that we would like to deposit. So on the target there are atomistic processes going on. So we are accelerating argon ions towards the target. And then material is removed from the target transported through the plasma and during transport there's interactions with other plasma species. There are also collisions that where the material particles lose energy, and then they land on the substrate, they condense there, and there can be diffusion, depending on the process parameters, and then the particles nucleate and the film grows. Depending on how you choose process parameters like, like the iron energy or the degree of ionization of these butter particles, but also the substrate temperature and generally the chemical composition, but also impurity elements. So of this, the outcome is a thin film with with different microstructure with different structural properties for example we have the morphology roughness density grain size, but also a crystal structure related properties that are being changed by the parameters. And the idea is now in order to handle this, this complexity of the parameter space to bypass the the physical processes inside the plasma reactor by, by a artificial intelligence model. What has been typically done in the past is the, there was an introduction by a John a form in 1972 of so called structure zone diagrams, where this is a low dimensional representation of possible microstructure types that you can, or a characteristic can achieve in a magnetranspatter process, depending on only two parameters, which are the pressure and the deposition temperature in relation to the melting temperature of the material. And so this was done for refractory metals. And this works in a way that you can, for example, go to this diagram and you like, if you would like a characteristic structure that is porous and not very dense. If the high pressure and the low temperature, then basically the diffusion of the atoms is reduced. They lose a lot of energy in the plasma, and you end up with a porous microstructure. If you would like to have a dense microstructure you would increase the temperature and reduce the pressure because then you increase the iron energy, and the firm is densified by bombarding species. If you increase the temperature further than you, and this kind of microstructure is then like a column microstructure. And if you increase the temperature further, then you end up with large grains that are like similar to bulk materials. So it was a relatively simple model they had to do a lot of experiments and then draw the sketch up these for the structure zone diagrams. However, the modern plasma reactors have much more parameters that you can vary and individually vary. And therefore the parameter space is larger. And so the idea now is, can we use an AI model to predict such kind of microstructure structure zone diagrams. And this was a collaboration with you really Zagorski and draft rods from icons. And we ended up using a conditional again conditional generator adversarial network, where we use the process parameters as as the conditional inputs. And we trained then on experimental microstructure images from a data set of chromium aluminum oxy nitride ceramic film films. Yeah, so as a first comparison between, if the model is able to predict the kind of microstructure. So first we should say that there's a lot of variation in the microstructure and microstructure images are rather complex to describe and complex to, to grab what you are seeing there. And so the first impression was that the conditional again was able to relatively good pre reproduce the microstructure images that were in data set. And you can for example see that if you have a faceted microstructure here, then you can also clearly see in the, in the conditional again, a similar microstructure but also if the grains are getting smaller than the predicted smaller, and there's, it is a little bit problematic when the, when the feature, when the features get too big to large, because then the, the model is somehow producing a finer, finer microstructure. Okay, but then this was just really to see if it's somehow working. And then of course would be can we also extrapolate or like predict inside this five dimensional parameters based different microstructural outcomes. And so this is then what we do we take two parameters that we can vary and three parameters are kept constant. And then we can produce such kind of microstructure overview maps. It's a little bit hard to see the difference here, but if you take a close look then you can see that there's a variation of the microstructure. You can access the deposition temperature, and here the aluminum concentration and chromium aluminum nitride. And this by itself is a little bit tough. So, you could in principle now identify the microstructure that you would like to achieve and then select the deposition parameters, with which you, you most likely will achieve them. So what we then did is train a convolutional neural network to predict from microstructure image, a microstructure type. So it's like a classification model. And since the GAN has has the randomized input, latent latent parameters. It's possible to produce for each condition, multiple variations of the microstructure type. And so then the CNN can predict the microstructure type of of multiple microstructural representations. And by that you can get a probabilistic microstructure map. And so here you can then say okay if I would like to have a certain microstructure type which is, for example, let's say fine grained here. If you want to have a fine grained microstructure, then I should increase the aluminum concentration as high as possible, and keep the temperature relatively low. Or on the other hand, if you want to have a faceted microstructure, then the probability of having a faceted microstructure is highest at higher temperature, and up to a certain aluminum concentration. You can find like the boundaries where, where you know that our physical physical boundaries for example if the aluminum concentration is too high then you would change to a hexagonal structure which you would like to avoid. And therefore you can limit yourself into this region and use this to to optimize the process parameters. For example, out of my PhD thesis. And the next thing I would like to show is how we use AI agents for x-ray diffraction analysis. But it could also be any kind of spectrum like data. So x-ray diffraction is being used for crystal structure classification in our approach. So we have a machine that can measure all fully automated the 340 measurement areas and produce such x-ray diffractograms, which are characteristic for the crystal structure that we're However, there's a challenge because what you typically then do is in order to to classify a microstructure you would compare the experimental XRD pattern with reference pattern from from literature. But imagine you would have to do this 340 times. And maybe there's like 20, 30, 50 possible phases that you can select from literature. So then you would have to do a lot of comparison. And this is a tedious task and it used to take us months to do that. And the essential problem that we have is that the the quality of the x-ray diffraction data and sin films is not always very good. So depending on the growth parameters that I just explained you can get crystal growth in a certain direction and therefore you have texture in your XRD pattern which means that if you would have typically five characteristic peaks. So if we are growing only in one direction we can only measure the single peak and the others are are not visible. And so this means that we lose a lot of information, and due to the film growth and and the subsequent measurements. And so what you have basically is a trade off between the number of possible phases that you have on the one side from reference structures, and the data quality of the X-ray diffraction. So if you have for example, high data quality, then you could probably classify a large number of possible phases. However, the worst case is if you have very low quality and a high number of possible phases and this is often the case and especially super alloys or high entropy alloys. We're just the chemical space is relatively large and therefore there's also lots of lots of possible crystal structures. And so this demands somehow for a model that can handle the uncertainty that is represented in the X-ray diffraction data. And this is what we have been trying to achieve with this study. So let me give you some brief examples of what kind of aberrations we can have in thin film patterns. So typically you would, if you have a certain material system cobalt nickel aluminum you would go to a reference database look for all possible combinations of cobalt nickel and aluminum and check what what crystal structures would be possible theoretically. So you would obtain such kind of stick patterns, where the individual peaks on the diffraction angles are plotted like this. And these you would have to typically compare to the experimental pattern. However, then we have the challenge that if, if the fun grows and preferred orientation, you only have certain peaks and others are completely invisible. So you have texture which means that the relative ratios of the peaks are changing. Another problem is that you could have two different crystal structures, but due to texture and to strain, they produce the exact same X-ray diffraction pattern. And you can of course also have noise depending on the grain size if you have small grains you get a lot of noise and relatively low signal. And these are the problems that we are having. So we need to have some kind of model that can, or now I'll introduce the crystallography companion agent for autonomous phase identification. So the idea here is to use a synthetic data set simulate physically correct X-ray diffraction patterns by accompanying all the possible variations in X-ray diffraction patterns that we can observe experimentally. And roughly 100,000 patterns per phase that we have in a material system. Then we use an ensemble of neural networks that classify the structure trained on the data set on the synthetic data set, and then we can use the real data set and perform phase classification using the ensemble network. And then there will be an ensemble vote and you will have some kind of probabilistic output of the phase classification. And this is how the pipeline looks. So, gathering the data from the ICSD for example, then simulating the data set. These are the ensemble networks. And then we will, we are using two kind of phase probabilities. The one is only based on the X-ray diffraction pattern and the second one takes also into account the chemical composition.  And the idea is that the companion agent only gives a rough estimate of what kind of structure there is so that the human can in the end decide what, what is the most likely crystal structure. What we, what we observed also is that compared to a single learner that has always 100% uncertainty or 100% certainty, an ensemble scales with the variations in the X-ray diffraction data. So if you, for example, artificially decrease the quality in the data, then you would also see that the, that the uncertainty of the ensemble networks go down. So this is exactly what we wanted to achieve. Another metric that we were also investigating is that with increasing data size, the model actually becomes better. So it is really the large number of physically correct simulations that we can do that helps, helps with good classification. Yeah, then here's the final result of the experimental data set. So we can use here, see here the ternary plot of a nickel cobalt aluminum materials library. So we were able to cover almost 100% of the composition space and one materials library like 100 millimeter wafer. So here are these the typical 340 measurement points where an X-ray diffraction pattern is taken and then you can see here in this ternary diagram, the face probability, first only on X based on X-ray diffraction. And you can see here that this is for nickel cobalt three aluminum. And the probability also spreads out quite large and this is due to the fact that the crystal structure might be similar so there could be a match based on crystal structure, however, due to the chemical composition of this phase, the classification should be should be bounded into a region where the chemical composition actually makes it reasonable that this phase is existing here. And so if you include the chemical composition between the reference pattern and the actual measured composition, then you can reduce the classification boundaries to a reasonable region in the composition space. And so this is for comparison then analysis performed by a human expert. So, my colleague then is now I was able to classify 20 more than 20 different face regions, manually, probably took a very long time. And you can achieve now similar results using the XCAD, the crystallography companion agent. And also benchmarking this approach with other models. There's the physics informed model by Ovidou et al. And they were basically simulating X-ray diffraction patterns in a non physical way in the sense that there are patterns that are unphysical but still representative somehow for the for the data. And we were comparing when we used the ensemble method on the data approach by by auto XRD, and we were comparing XCAD with the data from auto XRD, and the other way around. So the take home message is that only if you use the physically correct measurement that the physically correct simulation and the ensemble, you achieve relatively high accuracy. And then there can still be some edge cases for example what is if you have a pattern in your data set. So if you produce the material that has never been synthesized before, then it will probably be hard to identify. So first of all, if you just look for for the typical phases you would never find a reference structure that you could compare to. So the question is, can we find the model that can help us with detecting out of distribution data, thereby informing the scientist hey this this X-ray diffraction pattern is very unlikely that can be classified by by by your model, because it's not represented there. And therefore we just used a variational auto encoder to learn representations of X-ray diffraction patterns, which can actually be helpful in for for a lot of things. And so first of all we took the same pipeline we took reference structures from the databases simulated the relatively large synthetic training data set trained a variational auto encoder. And afterwards, we have a representation of crystal structures is now the synthetic data set of FCC PCC and a HCP structure. And you can see here in this relative in this representation relatively nicely the symmetry of the crystals that we have so if we have a FCC pattern in this angular range, we have basically three peaks that are occurring. And this representation is nice because you can use it on the fly for a rough estimate of structural similarity. And this means that, for example, in this case where we have the red and the blue pattern here, they are textured along a certain axis, and therefore they produce a relatively similar pattern. And we can observe this directly here in this latent space. There's a proximity between do these two but they are somehow still separated because it's different classes of materials. And this is that this is an interesting thing for on the fly visualization like during the measurement or very brief overview analysis. You can also use this to train, then a disrepresentation to train this classifier this was just a plain simple and can use can use neighbors classifier. So then, depending on where in this in this latent space, the x-ray diffraction pattern is located, you can make a rough rough estimate of crystal structure. But then the question is what happens if we have the pattern that is not represented in the data set. It would still fall in this latent space. However, it is actually, it is, it is not this kind of structure. There you can use the reconstruction error of the variation law to encoder to to easily spot that the reconstruction error is almost one order of magnitude larger than of all the other patterns and therefore you have a way to inform yourself that this kind of data is not represented by the model. And this is actually very helpful in experimental. You can use the x-ray diffraction classification then because you can have also face mixtures. And this is a really combinatorial problem if you imagine there are 20 possible phases, and you can even have all kinds of mixtures of these phases, then you end up with a huge data set that you cannot handle anymore. In this case, we could show that when creating artificially face mixtures, the model uncertainty or the actually the reconstruction error would increase, and thereby this can be a metric to inform that the certain that the pattern is, for example, a face mixture, not a single phase. Right, and then so coming back to the experiment then. We have here then again a materials library of a cobalt chromnical rhenium. We have been mapping the typical, in this case it was a square grid 225 x-ray diffraction patterns, and you pass the data set through the auto encoder and it directly can can highlight the regions where the face classification is challenging and these are we could show that are mostly face regions that include multiple phases instead of a single phase. Okay, now, towards the last parts. It's going to be really nice if, if, or, let me start differently. So typically, if we create such a materials library, it could be a high entropy alloy or ternary system. You have sometimes relatively simple trends over this materials library. So if you measure 350 data points, that's actually much more than you should have measured because you're just measuring more data points in a very simple trend. And the idea then here is if you can, if the, if the measurement device can directly identify that there is only a simple trend, then it does not need to measure so many data points and so this is where we use active learning. And you can see here is the original data on the left side so this is the electrical resistance of a high entropy alloy. And you can see here then the GP, the prediction by Gaussian process and the uncertainty or the covariance by Gaussian process, and by initializing randomly or with the, with maybe five measurement points. So you can start with an, with an initial fit, and then afterwards you're sampling the uncertainty of the model, and the measurements are only occurring by the uncertainty as high as and thereby you optimize the prediction during the measurement. And this can be seen now. Yeah. So you can see that the machine now decides on its own where to measure. But after only roughly 30 data, 30 measurements, you can see that the model converges and that the fitness is relatively good. I mean there's always some kind of variation in the measurements. And this means that a simple trend like this could easily be measured with only 30 data points instead of 350 which is a huge. Yeah, huge gain in time. This basically shows to the same plot, but you can, you can then see where the machine was measuring and how it went through the materials library to characterize it. Right, then one last topic that I would like to address is how can we scale with the chemical complexity, because there is an issue issue associated or let's start with the motivation. High entropy alloys have been observed to be relatively good. Electro catalysts for example for the hydrogen evolution or the oxygen evolution reaction. And this effect is due to the, they call it high entropy effect but basically in high entropy alloys you are producing statistical distributions of active sites where that can act as catalyst centers. And so it was observed that if you take individual elements like iron nickel cobalt manganese which by themselves are very poor catalysts. If you combine them in high entropy alloy as a nanoparticle, you can achieve the performance of a platinum catalyst. And so this they describe as the high entropy effect, and hence it's very interesting to investigate such kind of materials as well. However, first of all, the problematic that there's a huge number of possible quinary systems if you consider roughly 50 chemical elements that could be technically produced and used in our systems. And so you would have more than 2 million possibilities of quinary systems, for example. This is the one problem but the second problem is that while you can produce a ternary system by by a magnet transbuttering in full like 100% compositional coverage. If you use, if you go to a quaternary system you are only producing a two dimensional cut of the quaternary space. So this is visualized here. If you go to quinary space this is then even further dramatized and in the end in a quinary space like one materials library has only 0.5% of the compositions and the quinary composition space. And this is then not any more high throughput or, or impressive it's basically just a point in the center that you're measuring, and you're missing out on everything else. So what would they with a strategy to overcome this simplest way that we thought of is, okay, there's the problem is that the geometry plays a role now because when you deposit from five, the position sources on a two dimensional substrate. It's always a cut through the space and therefore you can permutate the cat folds, and by permutating the cat folds you're producing a different cut in the quinary space. There are 24 possible permutations. And so we produced a, we did some ranking of the or we simulated the coverage by the deposition and performed the ranking and drank the materials in the way or the permutations in the way that the permutation that will produce the next highest coverage is always synthesized next. And with this approach we're able then to with only, if you only consider the five to 35 atomic percent region in the quinary composition space to reach up to 30% of that with only six permutations. This, however, then creates properties in the center of the composition space and from that you could extrapolate and maybe also do active learning in order to further explore these large composition spaces. All right, so with this some already at the end of my talk, and I'm happy to answer any questions. Okay, thank you very much, Lars for giving us an insight into many different things that are possible to do with experimental data. Any questions. Don't be shy Patrick. Thank you for the very nice talk very impressive. The, can I come back to this x ray companion that you had this comparison to the human right. That was very interesting. And that you said there was one before the three triangles. Yeah, yeah. So you said that so the map in the middle was your best one from the companion right. So it was basically just showing that that by using these different probabilities, you can somehow constrain the prediction to be like the face probability to be high and only the region where it should be high physically. So there's, for example, it does not make sense that a certain structure. It's not in this region because there is no, but the chemical composition is not supporting it. I see so it's the agent only looking for one phase, because the diagram on the right by the human looks a lot richer in color than the others. Oh yeah. Yeah we know it's looking for all the phases simultaneously and distributing the probability also fall phases. So it's just a simple way to represent it. And we did not love actually the real results by the agent, we just measured the accuracy. This plot that this plot was actually produced by a person. And there's only this region here that we compare in this in this book, but actually you could produce the same plot by x. I see so the machine does identify the other. Thank you. Any more questions for Lars. This is pretty good because I have a ton of questions. Kevin, anything on zoom. No, okay good. Well, I'll start. We have a few minutes before our next talk. So one thing I did want to ask you is how large are your data sets. How many individual sample how many data can you retrieve. And do you actually for the same experiments or same combination of elements, do you generate multiple samples. So to the first question, it's, it's always this typically this fixed grid that we are producing so 350. That are then located differently in the composition space depending on, we can go from binary systems to quinary quaternary. And then the compositional coverage on this is not so good anymore. So it's always 303 50, 350 per wafer. Yeah, roughly 350 per wafer, and we can produce. We can produce reproductions of the same way forward relatively good. Not a lot of variants, so we can produce two times the same materials. That was going to be my next question how much of the statistical reproducibility noise do you have and do you did you actually take this into account into in the models did you integrate this noise in the model. So, I mean if you would perform the same measurement on the same spot twice, then depending on the measurement system you could have a noise and variation there. But we also get statistics from the neighboring points. So, typically you could assume that there's some kind of trend with the property and the chemical composition except you have something like nickel titanium where there is 51% boundary where there's no effect or not. These were probably miss but if you assume a continuous gradient and by looking at the surrounding data points and you can see if there's if the trend is reasonable, and this is also kind of qualitative metric. So you do use the continuous gradients to smooth out your data if needed. Yeah. Okay. How about any further questions. Not maybe I can. Patrick. Yeah. I saw that your vision optimization also sample the edges of the way for a lot to you and this makes sense because it doesn't have what that's where it's most uncertain, right. You know, it doesn't know how the function continues outside the boundary. If you if you if you go to the one after the animation. Yeah, there. So a lot of the numbers are actually on the boundary. And we also observed this and it seems to be something in this very hard to get rid of in Bayesian optimization if you don't have continuous variables. So because the model is trying to put the uncertainties pushed towards the boundaries. So have you, have you noticed this and have you sort of ways to get rid of this to be even more efficient in sampling, if you need it. So far, not know we only did so far the first implementation of this and we actually have a measurement test and that can do this, this kind of sampling. And but we are now, and that's actually also the point that we have to do. We have to, in order to gain trust in these methods because in this case I mean know the data. And how it's represented. Then you need to have some way of stopping the model where it says okay this is the uncertain the global uncertainty is now relatively low I stopped the measurement. And so we are now trying to set up an automated benchmarking platform so that each materials library that is that is measured with this machine. Everything is stored. And afterwards the GP runs with different parameters and then over a period of time we'd like to, to see what kind of stable parameters can we use for for distortion. Yeah, you can throw in a few measurement points as test set and then always compare your model against it. Yeah, or something like this. Yeah, so this is what we're trying. Thank you. Thank you everybody and thanks again Lars for your talk. Our next speaker is you can from Altar University in Finland. And while you is preparing I wanted to make an announcement.


and welcome to the session. Let us discuss the following question. The question says the length of the side of a square whose area is 441 meters square. Before proceeding for the solution let's recall the formula of area of a square. The area of a square is equal to side square. So this is the key idea for this question. Now let's see its solution. In question we are given that the area of the square is 441 meters square. So let's write area equal to 441 meters square. Now by the key idea we have that area is side square. So let's replace area by side square. So side square is equal to 441 meter square. Now let's take square root on both the sides. So we will get square root of side square equal to square root of 441 meter. Now that means the square root and square will get cancelled and we will get side on left hand side equal to square root of 441 meters and out the square root of 441 by long division method. We are over every pair of digits starting from one stage that is one. So let's place a bar over 41. Left will also have a bar. So we will place a bar over 4 as well. The number under the left most bar that is 4 as the dividend. So here our first dividend is final number whose square is less than or equal to the dividend that is equal to 4 that is equal to divisor and the coefficient also and 2 times 2 is 4. So subtracting 4 from the dividend we will get the remainder as that is 41 to the right of the remainder and get the new dividend as divisor that is and enter it with a blank on it's right. Now we need to find the largest possible digit to fill in the blank which will also become the new digit in the quotient such that with the new divisor is multiplied to the new quotient the product is less than or equal to the new dividend that is 41. 41 into 1 is equal to 41 which is equal to the dividend. So we will take the digit as 1. So let's write 1 over here and get the new divisor as 41. Also 1 is the new digit in the quotient. So 41 into 1 is equal to 41. So let us subtract 41 from the dividend and we will get the remainder as this the remainder is 0 and there is no bar left in the number therefore square root of 441 is equal to 21. I is equal to the square root of 441. So we will get a required solution and with this we finish this session. Hope you must have


Phillip Smith, 59 from Greensboro, North Carolina, is now more than 90 days alcohol-free. Phillip, do you know what day you're on, alcohol-free? I believe I'm on 105. 105 days alcohol-free. Right. Fantastic. And how are you feeling now compared to day one? I feel great. Feel much better. Much clearer, more energy, and just kind of a more even keel, I guess, than I was before. So just tell us a little bit about yourself and what you do and what inspired you to want to quit drinking in the first place. So I'm an attorney and work in-house for a corporation. And I think in terms of what inspired me to want to stop drinking, one was that my wife really was on me to stop drinking, and I had wanted to stop drinking for a long time. I tried a lot of things, AA just never appealed to me. I tried things on my own, a variety of things. And what I finally found was that without accountability, it just wasn't going to happen. And really, it just wasn't going to, it didn't work for me to try to make my wife be the one that held me accountable. That didn't really work for our relationship. So that's how I found this group and thought that this might be something that would work for me. And what were you drinking how much and when? I would drink, I would drink at night. I would drink when I came home after work and I would drink, you know, several glasses of wine, you know, it could be two, three, four, and it was pretty much every day. I would come home and drink wine. And what were the results of that consistent drinking? How was it impacting you? You know, it didn't feel good in the morning when I woke up, maybe sluggish in the morning and not, I think, not as clear as I needed to be. I feel like it made work harder than, or I know now, it made work harder than it needed to be. And honestly, it took away from the enjoyment of work because that struggle from coming out of that alcohol haze. And it just made you feel, and I felt guilty all the time. I was not proud that I was drinking. I didn't want to drink. I just didn't know how to stop. And so you joined our 90-day quit drinking program, Project 90. And what do you think inspired you to stay quit this time? Well, it was a combination of the things. The weekly calls with Kevin, the counseling calls helped a great deal, helped me not only with the alcohol issue, but with other issues that really played into the alcohol issue and helped me in dealing with tough relationship issues at work and other places. And then I think the other big piece was the weekly calls with the group. You really established a bond with a lot of people. They were very interesting, very smart people from all over the world. And I think the first time somebody was very kind of raw and honest just sort of blew me away in a situation where you're able to be honest and frank and tell people things you might not tell to anyone else. And so you feel a real bond and you feel like you need to get on those calls and be able to say that you stuck with it for another week as much for yourself as it was for them. Beautiful. And so now that you've accomplished the 90 days alcohol-free, how do you feel? What are some of the benefits that you weren't experiencing when you were drinking? Well, I'm in a period of work where I've got an awful lot of work and I've actually been enjoying it. It's helped me to juggle a lot of different things with a clearer mind and do a better job, I think. More energy. I always went to the gym a lot, but I feel like I just generally have more energy and more focused in general, both at work and on a personal level. And I feel like I have more time to figure out more things to do. So my wife and I, you know, we started to go on hikes on weekends, something we never did before, but you sort of find this time that was wasted because you were either drinking or not feeling good or something alcohol-related and it's interesting that now you've got all this extra time and that gives you time to do new things. You mentioned before that you had someone at work write you a complimentary note, so can you just share a little bit about what colleagues have noticed about you in your work performance? I've gotten compliments in terms of being able to handle the issues that have come in. They recognize how much pressure I'm on because of this increased workload, but I've been very complimentary that I've been able to stay on top of it, get back with them when I need to get back and get back with them with an answer and a clear answer, which is obviously in my profession, something you need to do. And I got to compliment from a boss that doesn't really hand them out very often, so that felt very good as well. That's great. And anyone else outside of work who's noticed anything about you and your way of being since you've been alcohol-free? Yeah, the other was funny. It was on day 90, as a matter of fact, and I had to go to a reception which was full of cash bars and wine on the table, none of which, of course, I had. But I ran into a friend of my wife's that I've known many, many years and talked to her for a while. And then my wife saw her a couple of days later and she was telling her how good I looked and how kind of that I had a good energy and that I seemed really calm and just a lot of compliments that kind of came out of nowhere because she doesn't really have any idea what I've been doing here. But obviously, she noticed a big difference. Yeah, wonderful. Anything that you noticed about your sleep or anything like that? I think it's getting better. It's still a little bit of a challenge for me, but I also think I've got an issue with probably with sleep apnea, so I've got a doctor's appointment early next year to figure that part of it out. But I think overall it's better than it was when I started. I had gotten in a pretty bad place with sleep. How did you feel just before you enrolled? Were you scared, nervous that you'd be able to do it? Were you worried about whether this way was going to work for you? What were some of your fears that you felt just before you enrolled? Well, I think you always have a fear about whether it's going to work because nothing else had worked. But I also think I had just gotten to the point where I really wanted this change and I had looked at so many different things and I had never really committed to something like this and for whatever reason in doing a lot of research and looking at a lot of what's out there, I thought this will work for me. And I think I just was at the right place that I was ready to commit to that. And finally, what would you say to someone who might be watching this now who's considering taking this plunge, I guess, of quitting alcohol for at least 90 days and doing so within the Project 90 program. So what words of encouragement might you offer to them? Obviously, I think it's a great program. It worked for me after many years of trying to stop in different ways. And I think you're going to find there's so much that grows out of stopping drinking that so many more positive things than just the fact that you're not drinking, but it opens up so many doors and makes you feel so much better that you're not going to regret it. And with the support of the people in this organization, I think that you're likely if you're committed, you're going to do it and you're going to be happy you did it. Well, Phillip Smith, 59 from Greensboro, North Carolina. Congratulations again. And thank you so much for playing full out. Tell us how many days you are again. 105 days alcohol free. Wow, amazing. Congratulations. And how many days are you going to go? Well, hopefully forever. But for now, I committed to another 90 days. So for sure, another 90 days. Yeah. Well, congratulations, Phillip. And thank you so much for sharing your story. OK, thanks, James. Thanks for listening to the Alcohol Free Lifestyle Podcast. I want to load you up with some free stuff right now. So if you want to go to JamesSwanick.com slash guide, I will send you my quit alcohol guide, which has helped six figure entrepreneurs and top professionals reduce or quit drinking. You can also text the word quit guide to the number four four two two two if you're in the US, of course, it doesn't really work anywhere outside of the US. But if you're in the US on your mobile phone and you'd like that guide, text the word quit guide to the number four four two two two. Or you can go to JamesSwanick.com slash guide. If you'd like to schedule a free 15 minute call with one of my top coaches, just an exploratory call to see if or how we can help you. Then you can go to JamesSwanick.com slash schedule or you can text the word project 90 to the number four four two two two if you're listening in the US on a mobile phone. That's JamesSwanick.com slash schedule or you can text the word project 90. That's one word project 90 to the number four four two two two. Feel free to send me a direct message over on my Instagram account, which is at JamesSwanick. You can also watch video episodes of this podcast and a series of other educational videos on my YouTube channel, which is JamesSwanick one, or you can direct message me on Facebook at JamesSwanick official. And finally, a request, would you please now write a short review of the podcast inside of the Apple podcast app on your phone or on iTunes on your desktop computer? Would you please give the show five stars and write a quick one or two sentence review? This will help the show get in front of even more listeners, potentially transforming someone's life. You can rate and review the show inside of your Apple podcast app on your phone or over on iTunes on your desktop. Thank you so much and I'll catch you next time.


It's been an unbelievable round scouts rally essays turn it up as usual our outright category third place Congratulations to this team done a fantastic job Ashley James Dennis Neagle well done. Good job. Yeah. Thanks very much to know It's some pretty good way to finish off a really really difficult year It's been Dennis you jumped in at the last minute as well So this is your first time tell me about this car this rally and what it was like from the passenger seat This is an amazing car. It handles well. It takes off well lands well, and it's been a great ride Actually, this is just it's been a big season for you really the beginning was pretty tight Most people wouldn't have blamed you for missing the rest of the season when you've fought on This must be great for you and your team and your family to get this home and finish the season on a really big high Yeah, definitely look, you know Mara was pretty low especially even after the last event And I just went back to the team and said look I'm not really that fuss whether we go to the last round and You know the undying support of my wife and my dad. They just said no you go to the last round You've done this much. You've gone this far We want to try and get there and finish on a high It's been a tough weekend for this team, but a fantastic year Well done the Molly Taylor Bill Hayes second this weekend and second in the championship well done Yeah, a lot of runner-ups isn't it? Hopefully we'll change that next year It's better to be runner-up than nowhere at all though, and you've showed great speed. That's more important to you Isn't it? Yeah, look at the end of the day.  So we we did our best at every rally and you know Eli did a fantastic job, and he was a great benchmark for us And we enjoy battling with him and congratulations to him and and we we're proud of what we've done Well people don't probably understand you know Molly's got a lot of experience We've heard her name for a long time But it's their first time in this series and on a really tight budget So it's an amazing job by the team and by Molly It's been sensational year every event I go to something happens that just amazes me with the effort that they all put in and You know speed on these roads in particular this event That's very specialized and to be that fast against someone as good as the 11s has been really good And can you just explain us why your park like this and everyone else's reverse park? You got the dramas going right to the finish Yeah, you can imagine how many women parking jokes I just got for that But um, we've had uh, yeah an issue with the clutch all day So we we pushed it in and there wasn't enough room to turn it around so we kind of pushed it until it stopped and then We've got through the finish and now it's just uh, she's going to stay there for a while And the winners of this round of the championship and of course the champions of the year well done to Eli ever the glen We're some team sits in Australia. Congratulations guys. What an end of the year But it's uh, yeah, well, I think we've won the last six heats now so um To just top it off Winning today was just icing on the cake really championship yesterday in the win today I really wanted that it wanted a clean sweep and we managed to do it. So um, we couldn't be happier right now You talk about those heat wins, but in actual fact you guys together with your champions Like I think it's only like 16 wins out of 20 events unbelievable. What's that? Yeah, yeah Um, I still can't believe it 12 in a row and then you know another six over the last um year in a bit It's been awesome. So yeah, yeah, it's fantastic. Uh, this is your third championship What was it like to I mean one what's this one like a different manufacturer different team? Yeah, you're running as a as a driver and also to run the last day already having won it You've had a couple of different feelings this weekend. Yeah, no, it was nice It was a good up this morning We tried a few different things with the car to see if we can gain some speed and there's a few little things We can still work on now. So um, yeah, it was uh It was a nice feeling molly taylor did really well all year. Steve McKenzie had the pressure on two He's always improving and uh to knock off big brother Simon Evans who unfortunately wasn't here But on the first four rallies that was something special as well. Well, under you guys really well deserved. Congratulations. Thanks


Warren Maze Mola Arrested Today Warren Maze Mola Arrested The reasons will shock you Warren Maze Mola is undoubtedly one of the most versatile actors in the game. He can convincingly play any role. He has switched from playing a flamboyant gay character to playing a hardcore criminal and we are impressed by his skills. Last week, the actor won a SAFTA at the South African Film and Television Awards which were held in Sun City and according to Sunday Sun, he is the latest celebrity to be caught drinking and driving. The paper alleges that the actor was nabbed for driving under the influence of alcohol in Free State.


Is he actually jumping? Is he like bungee jumping? It's getting too minty. I feel so bad. I feel so bad. I feel so bad. Oh my god. Oh I got so much respect for you. No. I could not do that. Oh it's always like that. You're always scared but when you do it you get that adrenaline rush. But I could not do it. I could not. I could not. I got respect man. I got so much respect. The oof. Please come back man.  Come back to MONSTERS. We all support you man. You know it. MONBEBE are here for you. Even other capable fans. Everyone's here for you. It's pretty easy to do. Okay. I thought that'd be hard. All right. Okay. We got one more video to watch and it's just MONSTERS One Ho being One Ho and I can't wait to watch this because uh dude I'm loving him. He is such a good person. He loves food which I support.  Come back man. Come back. I don't know if you'll watch this but come back man. Come back. Just sign a new contract with Starship. I believe. But anyways let's just get into this one. I can't wait to see it. I wish I could do that right now but it's 2AM. I'm a baby face. You got a good taste of music. WJSN. Baby face. Good song. Oh my god. Does he get it? Does he get it? You can do it. You can do it. I'm a baby. Probably breaks glass. Hey in the mouth. Dude his life is contagious. I swear.  Yo you just gotta pray. When you get... Look at them freaking out. Menu with ramen. Menu with ramen. Does he love ramen or something? I don't blame him.  Oh man. Oh man that was a good watch. Oh yes. Oh dude. One more like.  You're amazing. Man why does this happen? Why did he have to leave man? Yo. One hurt. Come back. Please. I'm just getting into monster x and I can't get into monster x if it's not all seven of you guys.