Zambia's founding president and liberation hero, Kenneth Kaunde, has died. His son says he passed away at a military hospital in the Zambian capital, Lusaka, where he was being treated for pneumonia. The 97-year-old ruled Zambia from independence in 1964 until 1991. He was one of Africa's most committed activists against HIV and AIDS. Hello, hope you enjoyed the news. Please do subscribe to our YouTube channel and don't forget to hit the notification button so you get notified about fresh news updates.


Appalachian Wireless and Samsung proudly introduce Black November all month long get the Samsung Galaxy S9 for just a penny. The Galaxy S9 Plus just $99.99 and the Note 9 just $249.99. Black Friday all month long at Appalachian Wireless. Two year agreement required C store for details. Last weekend was opening weekend of Kentucky's firearm season for Whitetail Deer. Pike County saw a lot of activity as more than 200 hunters reported their harvests. But conservation officers were also busy on two significant poaching cases, seizing nearly 40 sets of antlers and two guns. On Sunday, officers were dispatched to the home of 64-year-old Roy Pinson on Joe's Creek on a poaching complaint. Their officers found 23 sets of deer skulls, many of which Pinson admitted to killing without tagging. According to the citation, Pinson killed the Kentucky limit of one buck earlier in the season with a crossbow but did not report it. On Saturday, he killed a 10-point buck which he did not tag and was continuing to hunt on Sunday.  Both men are facing multiple counts of the illegal taking of wild game. Both cases were successful because of anonymous calls to the Kentucky Department of Fish and Wildlife. Anyone wishing to report illegal hunting activity is urged to call 1-800-25-ALERT.


This session now is about share your model, public models in Transcribos. We always had some models available but more for demonstration purposes. We were a bit cautious to come up with larger models because of course you read German Quarant and then you expect that it can read really all kinds of German Quarant or English writing or whatsoever. And for historical documents I think we are still rather far away from having general models which are really powerful and which behave like models for maybe modern documents or like for OCR for printed documents. However, I think it is important also for the community to see and to give you the possibility to share your model. And so you will nowadays find I think something like 25 models already in Transcribos mostly based on large amounts of documents. And I am very grateful that some colleagues are now willing to show you how they produced these larger models and what the background is. That is also of course very important to see in which context such models are created. The first one who will introduce us his model is Achim Rabus from the University of Freiburg and he is working with Slavic documents. All right, I am a Slavist and I am interested in Czech Slavonic. Maybe not very many in the audience are interested in Czech Slavonic but I would like to propose a specific workflow for producing these models. I have two generic models. The first one is for Russian so-called Polo staff which is for 16th, 15th century writing. The CRR of the model is 3.8%. I used 170,000 tokens for training. And the second one is a really generic model with more or less a similar CR but considerably more tokens in training data, almost 400,000 tokens in training data and this model is capable of transcribing manuscripts written in so-called Ustav which is the oldest writing system down below as well as manuscripts written in Polo staff, as you can see here. So this model is capable of transcribing old manuscripts from the 12th century and also models manuscripts from the 15th, 16th century. This is kind of a generic model. Of course, as I told you before, those who attended the large model workshop these models introduced some hypercorrect forms because of course there was linguistic change between the 12th and the 16th century and the model learned certain linguistic features from one period of time and tried to apply it to another period of time. This is a drawback of generic models but I think it's better to have one than to have none. And I would like to propose this so-called recycling approach which means I didn't produce ground truth manually at all. I just browsed the internet and copied freely available electronic editions as well. I asked a couple of colleagues who produced editions in printed form to send me their word files and I used these word files to train the model. I copy pasted it after some pre-processing into transcribers and ran the model training and I think this is quite a reasonable approach because there are so many world files around. And you all know from your community who is a more conservative colleague who produces editions in the traditional way but if we could reuse and recycle these sources we would all be better off. And this is my recommendation for you all. Thank you very much. Yes, Arshad? I was wondering, most of the time you hear that you need a good standard for making transcriptions to introduce the ground truth. For example, Arshad tends to fill in a creation of modernized language. Did he have this problem? Yes, we did. But in my field people tend to produce very faithful, linguistically faithful editions so there's no normalization and no amundation. But of course we introduce noise due to heterogeneous editorial principles but in my experience more data is better than no noise. What was your experience with the diacritic recognition? Diacritics is difficult and in part of my training data the editors resolved to omit all diacritics altogether. Which is the model learned to ignore a code and things like that. Which is not very good but in the first model this is the case. The second one has some diacritics and this is, well, everything what is between the lines in my experience is difficult for transcribers. I actually have a comment. I think this is a very helpful method but sometimes you don't have the print text available but it would still be much easier to train on a ground truth, to create a ground truth from the print edition but then what you would have to do is export them as pages and then use them as DEI for manuscripts, as D2I for manuscripts, right? So in that case maybe the... You're talking about scanning printed editions to produce ground truth, right? Yes? Yes, not just the ground truth, to prepare the text from in transcribers for the printed edition which will be much easier and then you can use the output of that for manuscript editions, for manuscript copies of the same text. Sure, that's a sensible workflow, definitely. Except that this means a feature request to extract the text in per page segments but... Transcribers can do that, isn't it? So transcribers has a feature to export the transcription to Word or XML or whatever. Right, okay. The next one is Stefan Satthammer here from the University of Innsbruck and he will talk about Neolatin documents. Yeah, I know I was telling you something about our model we trained inside the project Noskemus, Novus Kienzia, early modern scientific literature and Latin. Let me say a few words about the background of this model. The project Noskemus tries to decipher and really advance our understanding of the interrelation of Latin and scientific literature in the early modern times. The main outputs of the project are a couple of monographs, a database with about 1,500 representative texts in terms of chronological spread, literary forms and scientific disciplines and a so-called digital source book. We want to make all those 1,500 works which are in our database machine-readable and we want also to implement the keyword spotting. A quick overview of the ETA our model went until now. As a first step I tried to create one special or individual model for each century our project deals with. This means I created one model for the 15th century, for the 16th, for the 17th and the 18th century. Each of these special models consists about at least 300 pages and those 300 pages are taken from at least four or five different works or books. The works selected for the training data were selected with the aim of ensuring their representative in terms of chronological spread and used typefaces. As a second step when I had a couple of individual models available I merged those individual models to one general model called Nos Zemus General Model Version 1. As an example here I have the special model for the 17th century. The training data consists of about 70,000 words and about 10,000 lines. The character error rates for the training data are 0.31% and for the validation set 0.62%. In this model pages are included of the following five books. You can see I tried to select some books from the beginning of the century, from the end of the century and from the middle of the century. There in December of last year I had three special models finished and available and then I merged them to the general model. The general model consists about 170,000 words in the training data set and about 30,000 lines. The character error rate for the training data 0.66% and for the validation set 0.95%. Although the model is tailored towards transcribing neo Latin texts set in antiqua based typefaces I made the experience that very good results are also possible with texts in other Roman languages like English, Italian or French. Our model is also able to handle some passages or some words in Greek and some passages or some words set in German fractur. I tried to follow those transcription guidelines and to give the user a maximum of freedom standardization in the transcription process have been kept to a minimum. Normalizations have been implemented only in the following cases. Ligatures and abbreviations have been expanded. The long S was transcribed as a normal S. Small caps weren't marked as small caps but were transcribed as myuscles. And special characters like the sign for ed and the e-cardata and those were also kept for the purpose that the end user has the maximum of freedom. There are at the moment a couple of known issues. There are occasional inconsistencies in the transcription of quotations marks, punctation marks and diacritics. The error rate for transcriptions of Greek words or passages is still high. The main problem there lies in the vast amount of different Greek typefaces that were used in the early modern times. To a lesser degree also in passages and words which are set in German fractur, the error rate is not the best at the moment but I'm working on it and I'm hoping that I can publish an updated version of our model in a couple of months. Yeah, thanks. Any questions? Maybe just as an explanation, these are books printed documents. Did you try to expand the special characters? Sorry? Did you try to expand the special characters? I had the earlier version where I expanded the special characters but for the final version I decided to keep them because although many editors wish to standardize the edition they are going to make because often they want to keep especially these special characters like the ACOW data and so forth. Any other questions? Okay, thank you. Now Günther Hackel will show us some public models. Günther is the second Günther in the transcribers team and therefore called often George and many of you probably already had contact with him. He is one of the programmers and also does a lot of project management and he was working with documents mainly from the NewSci project. Hello. I want to present the NewSci project and more important the public models which are and will be created during this project. So the motivation is that historical newspapers are really a great knowledge base for the scholars, general public as well as the humanities researchers but the access to them is limited and also tools are missing with which you can investigate on them. So therefore this project came up and it runs until next year in April. There are partners involved and one speciality is that we have three types of partners and libraries, computer scientists and also humanities researchers so that they really can try immediately the tools which came up in the project and give feedback immediately. So it's not always easy but I think it's worth to have them also in this project. Here are some examples of the newspaper pages and our group deals mainly with data generation so we use transcribers for this task so we have to on the one hand create ground proof for all these research activities like layout analysis text recognition, article separation and entities and we have also to process 1.5 million pages until the end of the second year. So as a first public model the team in Rostock has trained one neural net for layout recognition of newspapers. It's already available via the layout recognition tool in transcribers and of course we also trained some model for text recognition. Here you can see examples for trained model so now this was also a training for you. I think tomorrow you all can read a German newspaper at the breakfast. Let's see, please let me know. We took not the easiest training data so it's from all over the time periods and our kind of images, good and bad images and these are the statistics of these ground proof samples. We have around 442,000 words in the training set and there is some interesting statistics. If you take the statistic of the German language which says that the word has 6.5 characters in a word in average then you have almost 3 million characters and if you then know that the character E for example is distributed in this language statistic 17% of all characters then you have almost half a million representations of the letter E in this training set which is quite a lot because a seldomly letter like Q is only represented around 600 times in this set. It's just a funny statistic. So we are quite satisfied with the results as we heard already in the morning session the guys from the linguistic department used this public model for the Alpine journal and also someone took this data and trained a Tesseract model for example in Germany and also others use it right now. And hopefully we get two other. It are three exactly because the National Library of Finland has Swedish and Finnish in the project both languages so we should get three other public models during the course of the project. And the BNF should follow very soon for the French. And here I want to show that you can also create article ground proof with transcribers so it was really a challenge to make such a huge page. At least you have some colorful poster for your office. But hopefully we have also some article model which we can make publicly available if it works as expected. So this is an ongoing research and will last some time till there are really good results. But of course the ground proof is there. And here are some other enhancements which we created in this project which transcribers benefits from the project because we made an IOP export and as well as an import for the named entities where you can also use already existent ground proof and also the annotation feature is really getting enhanced. Yes, that's more or less the created public models in this project in the new site. Any questions? IOP it's a format for the named entities it's inside, outside, beginning it's the short name of this. Just out of curiosity I have not understood the purpose of this multi-chamance in each other's... It's just an illustration of the articles inside this newspaper page. So each different carrier is an own article and it's just to present it in a human view every way. So for the computer it's just IDs in the page XML but to let the user also see what articles are separated we have these different colors for each article. Yeah. So how does this work relate to the future? How long does it take? This is something different. So the team in Rostock, the University of Rostock will take these ground proof articles ground proof and make an own training out of these ground proof. It's also machine learning but a different attempt. Yeah. I've got the microphone so I've got a position of power here. So I wondered whether you were using OCR specifically for this and not HDR and if so which kind of OCR was being used. And also I'd like to know a bit more about the layout analysis and how that works and if it's automated for picking out the columns of articles that you're transcribing and are you separating those somehow from one another or are you exporting everything off the page altogether if you follow me? So we used OCR, every fine reader at the beginning to get the first version of the text and then corrected this text for the machine learning. So it was the first step and for the layout recognition it was trained on lines on baselines only. So in this project the use cases you first recognize all lines afterwards you recognize the text afterwards you recognize the articles and only after you recognize the articles you draw the text regions around articles. The focus is not to recognize text regions at the very beginning during the layout recognition but only after article separation. So the trained layout model for newspapers concentrates on lines and not on text regions. Finally, we will hear something from the National Archives of the Netherlands. Hello everyone. I'm going to make the presentation for our newest public model. For the last two years our team in the digitization department has conducted many experiments with purpose to create a model which will be able to transcribe Dutch handwriting documents from the 17th, 18th and 19th century. After many attempts, research and experiments our team finally managed to create a new public model that is available via Transcribus platform and we call it Iceberg. Model Iceberg summarizes more than 1.5 million words and more than 800 different handwriting styles around 5 scans per handwriting type. In order to reach a point that we will be able to produce a reliable model like this we created many datasets with various material. This way Iceberg is a dynamic combination of two models that respectively has been trained with VOC documents from the 17th and 18th century and tutorial DIT documents from the 19th century. Here you can see some image samples from the material that we have used VOC documents from the 17th century here as well. VOC documents from the 18th century and 19th century tutorial DIT materials from the North Holland's archive and eight other state archives in the provinces. Our approach was quite simple. At first we categorized the available scans into three different datasets. The first dataset contained VOC material from the 17th and 18th century. The second dataset has been trained with 19th century notorial DITs material from the North Holland's archive and the third with notorial DITs from eight different state archives. For the training process we used almost 6,000 scans in total which correspond to more than 7,400 physical pages. Every model has been trained with HCR Plus and 1000 epochs. For the test set we used additionally 60 scans one scan per every 100 pages. The results that we managed to extract on the validation set were quite promising with an average CR at 5.15. Finally we combined these sets into one and we created the public model iceberg. Although despite the ambitious results model testing was essential in order to confirm the model's efficiency. As you can see in this table we created six different datasets that every one of them contains special characteristics such as different handwriting styles, different origin and have been created at different time periods. The training results were actually remarkable with the lowest CR level at 6.45 and the highest at 8. So these results lead us to the conclusion that public model iceberg can be very useful for users who wants to process archives from the 17th until the 19th century and also can be used as a reliable base for training HDR materials. Thank you very much. I especially like the name. I especially like the name iceberg and also what I'm looking for is sometimes a model called a Swiss knife. Or something else which is then really capable to read everything. Any questions? I think you also published the model in August last year. The M3? Yes. Can you tell the differences between those two? Maybe. Miss Kaiser can help me. I think it contains only one-third of the ground shoes from this model. I can confirm it works because I tried it. It's much better. Thank you very much. One remark from my side. Large models are often not better from the character rate, but they are more robust. So you can feed more different styles to a model and they will still provide good results. More special models often have better CR rates, but are not so robust.


Hi, so I'm Robert. So what I'm going to talk about is indexing articles on Wikipedia. So without much further ado, I'll just start now. So about myself, my home wiki is on English Wikipedia. Permain edit on Singapore related articles and I have been actively maintaining our index, the index of Singapore related article for the last four years since 2019. So what I'm going to talk about next is why we want to create an index and what's index and how to create an index. My target audience is basically those who want to create an index for the topics that you want to that they are monitoring. And unfortunately, we will require a bit of technical knowledge because right now it's still pretty scripted. So for now it's just people with technical knowledge will probably will get the gives of how I extract the information. Okay, so first, what's an index for those who do not know? It's just an alphabetical list of articles and a system of the list itself as well. So list of all article that is available on Wikipedia for any broad general topic. So for example, it's Singapore related articles. It can be any other countries as well. It can be Malaysia, it can be Japan or even just a thematic topics even. Okay, so why you want to create an index, general is just these three reasons. So first, it's a quick way to find out if a subject article exist in context of the general topic. So let's say I want to find a topic about maker space in Singapore. Yes, we can use a search. But on top of a search bar, we can give you maker space article directly. We have a maker space in Singapore. It's just not listed here because they do not have an article. So if anyone want to create article from here onwards, they can do it. They can do so immediately. The next one is to fight pentatilism in that topic that you are monitoring. So how me and my fellow Singaporean editors are doing so is basically through using the related changes on the index itself. So this index right now currently stands at 12,000, 13,000 links, on page links. So we are actually monitoring almost every Singapore topic, almost, I mean, not all these articles on English Wikipedia. So once it comes in, if there's a vandalism edit, it actually serves the same function as a watch list. It's just that this is already a predefined watch list for everyone. And also the last point is discovery of new articles. One thing about indexing, putting on index is that it de-offence articles immediately. So it always kept at a minimum. Right now, inside this maintenance report, I just put this like just a few hours ago. It stands only at three links. And these three links, oh, these three links too were created in the last couple of days. And the rest of the, because I ran the index like earlier last week. So these links are the new articles for Singapore related articles. And also another thing is that in this chart here is between 1st June to 31st, October 2019, right in the middle around August is when I actually run the script to programmatically create the index. This chart shows the spider visits.  Before that, there's like we only be visited like maybe 50 per day, 50 times per day by the board traffic. But after updating it, we are seeing more board traffic coming in because it's getting updated much frequently, much more frequently. So search engine barang the no index conditions and stuff. It will immediately ping and get all these articles out without much faster. It's easier for the search engine to discover new articles. Right? Okay. So, but here comes the issue. Maintaining index is very hard. It's very time consuming. If you do it manually, it's a boring task. You basically have to copy the links one by one. It's a lonely task as well because no one else will know what, no one else knows what you're doing. Right? And it's maximum effort. Right? Yeah. So it comes to a point where that was actually an AFD discussing to delete 174 indexes. Yes. So all the indexes that is related to country and region topics. Right? This is part of the opening statement. It's a redundant and unmaintainable systems of indexes, articles. Yeah, that this AFD concluded with no consensus because I participated by saying that Singapore list is updated. Sama yang else participated and say that the Vatican City list is updated. The two smallest city states came in and say it's updated. Yeah. So it ended up with no consensus because there's a recognition of the effort on us, the two main editors trying to get our index up to date. There were also other indexes that were relatively updated but not to the extent of our two city states. So now this is just a quick guide. I don't have my laptop with me to demo the crawl and stuff but this is my workflow essentially to create the index. So the general steps is download and set up a script. The script is on GitHub. You can just go and clone and fork and clone it. Get a list of categories. I script users categories to get all the articles out. So you need to identify the related categories first and then you run a script and then you manually update into Wikipedia. The reason why manually update is that I was and I'm still quite lazy at trying to get a board approval to automate the entire process. I was also pregnant because I don't know how well received this will be if it's just only for one index itself. So right now, I was still doing it manually and checking through the changes. But if I want to scale it up, there will need to be more semi-automated or even automated measures. So first, downloading and set up a script My link, the link of the GitHub is, repo is there. One prerequisite is that it's Node.js. Yeah, so unlike most of other people who were in data science or data engineering, my primary language is Node.js, not Python. Yeah, so that is something that I think is more accessible for those who are familiar with using JavaScript, which is most of, I will say most of the web developers or script maintainers on Wikipedia. In the main file is index.js, you have to edit two group of lines to match the current index articles if there is, the heading, the top heading and the bottom one. And then after that, you run MPMCI to get all the necessary Node modules, right? So this is just the script that I've pulled out to show you what you need to edit. So at lines 74 to 78, if you notice, it's the wiki markup of the index header code. So every time it runs, it's already loaded there. And it will load into a text file and I just copy the entire text file and paste it onto wiki itself. I do not need to find where I need to copy out. I mean, where in the wiki article to remove and insert the new list. I just do it wholesale. Same thing at the bottom, between line 95 to 102 is the bottom end of the index where you have things like your categories and the COSO sections. Okay. So this is where I need to replace. Right. And then to get a list of categories, right now, what I do is basically go to the main category page itself. So for example, category Singapore, open your browser script console and basically type dcj query command in. It will expand the category 3, one level down and just keep repeating this until you are satisfied that you have all of your categories open up. One thing to note is that there might be recursive category branches. So say, for example, on Singapore, it opens up to streets of Malaysia, which then will open up to Singapore, Indonesia and Malaysia and I think we'll just go over and over again. So for those who are more than you can inclined, you can start modifying the JavaScript query to only open up, to only open up those that are not on a recursive branches. Right. And there might be also unrelated categories. So that's what I suggest now that also like Malaysia and Indonesia topics, categories coming in. So that's what we remove. And then after that, you copy up into this file category.list, the entire category 3 and you reformat it so that it just only shows the category names. And so this is basically opening up the category 3 and then category list. And then after that, you just run node index.js and you copy the compile text from right now. I just basically loaded as pages 3.txt, but that is the completed index article markup. And for subsequent runs, you can monitor changes with, say, the new article board page itself. So for example, this will be at Singapore search results. It doesn't monitor every time you want to load a new index because there might be changes to the category names, there may be renames or deletions and stuff like that. So that is something that you need to do the maintenance for yourself for now. What's next is that I have plan to make it into a web, collaborative web application with our graphical process flow so that average editor will be able to generate index on their own and also to template the output so that it can be more usable for other kinds of index creations as well. And that's all with my talk. I'm open to any questions. Yes. All right. So questions outside.


Okay, so good afternoon everyone, as Carsten just said, I'm Masha and I'm an MFBM ESR under the supervision of Antonio Artees at the Carlos Tercero University of Madrid. In this talk, I would like to present the work I've done during the past three years. First, I will talk about the concept of digital phenotyping, its promises when applied to the mental health field and the challenges that working with such data sources present. And then in the second part of the talk, I will show you some applications that we worked on where we try to predict mental health outcomes based on the digital phenotypes. So let's start by defining the context. So the use of digital technologies to better understand individuals and groups or even society itself remains quite a key topic across different fields. And as a result, several terms have emerged in these fields that sometimes generate a hype or fear and overpromise things. For example, the terms of digital trace, digital footprint, which are common in humanities, social and computer sciences, arose alongside the digitized or quantified self as well. And more recently, medicine has introduced the term of digital phenotyping, which basically appears to promise the ability to track biological, physical and behavioral traits using smartphones and fitness trackers. And well, digital phenotyping promises significant benefits when applied to the medical field. For psychiatry, for example, which has up to now mainly relied on episodic reports collected on site with the clinician during appointments. Digital phenotyping offers this powerful approach to systematically detect behavioral states of patients, maybe even subtype current heterogeneous diagnostic categories and measure outcomes. For neurology where measuring cognitive tools is quite cognitive function is quite expensive. It offers an inexpensive ecological assessment tool in real world setting. So as it delivers this, let's say rich data source. It opens up a new field and new possibilities to reconfigure the delivery of healthcare both for the patient from the patient's perspective and the perspective of doctors. So the workflow of digital phenotyping consists of several steps which can be broken down to two main things. The first part is acquiring the data and then analyzing this data. And as these data sources are very complex, multi-dimensional and temporal, the, let's say, most more suitable or, yeah, the most suitable technologies for them will be machine learning applications. Yes, these technologies, even though sound very promising, face a series of challenges which can come from either the data collection or the data analysis perspective. From when we talk about data collection in digital phenotyping, there are two categories. There is active data collection and passive data collection. Active data refers to anything that requires the user's input. For example, filling in questionnaires, leaving diary entries or rating their sleep. And passive data is generally anything that is collected by the sensors of the phone or any device without requiring an input from the user. Now, the problem with active data collection is that users tend to, like, consider it a burden over time. And it's very hard to keep them engaged in the studies or in a clinical setting. Then, of course, there is the issue of privacy and data security. Some of this information that is collected can be directly linked to the person, such as their phone number in some cases or even their location information. So these are things that have to be handled using corresponding techniques. And then when we go to the data analysis perspective, we can see that since we are using different sensors of these devices, the data is very heterogeneous. We have real value data. We have categorical information. And also due to sensor failure or other collection errors, sometimes the values that we are recording are outliers, like you can see, for example, on the bottom left plot for the step count. Sometimes we end up with values in, like, two millions, which, well, would be quite impossible for someone to perform two million steps in a day, right? So these are challenges in the data that we have to deal with. And also, there are a lot of missing observations, which can be due to sensor failure or behavioral aspects of the patient. Like if someone, I don't know, turns off their phone for the night or for the weekend, then we don't collect data for a very long time. But we have no way of knowing why this is happening. Okay, so for these in mind, I would like to go to the second part of my talk where I will show some applications. So during my PhD, I basically worked on three main topics in mental health. One was mood prediction. One was anxiety prediction and the last project was functionality prediction using digital biomarkers. And the data that we used for this work comes from two ongoing clinical studies in Madrid. And for the Mobile Essence Data Collection, the EB2 tool is used while the clinicians use a tool called MeMind to record information about the patients and also the outcomes of their, like, for example, questionnaires or evaluations. So in the first project, we worked on a generic machine learning based approach for emotional state prediction using only passively collected data from mobile phones and variables. And as a target outcome, self reported emotions by patients. This topic is quite important in mental health because changes in emotional state or long periods of very negative emotional state can be indicative of worsening. Like disease evolution or like, yeah, for onset of depressive phases, for example. And if these are caught in time, then we can intervene and help patients before things get very hard. So for this project, we used the listed data sources, so step count, distance indicators, whether the patient practice sport during the day or not, how much they sleep, how much time they spend at home or using their phone. And then the self reported emotions, and we had a cohort of about 940 patients that had at least 30 days worth of data recorded at that point. And also, well, we wanted them to have at least recorded once an emotion. To represent the emotions on a lower dimensional level, we used these two dimensional mapping that is commonly used in psychiatry, where we map the emotions based on their balance of whether they are negative or positive and based on their intensity, which is called arousal here. And as you can see on the plot on the left, in the data set, most of the reported emotions belong to the negative category, which is quite expected with mental health patients. So in the overall data set, we had over 170,000 entries or samples. And as you can see on this graph, the data was quite sparse. There were sometimes long chunks of missing. Overall, it was about 80% of the observations was partial and it was around 5% only. That was complete, meaning that for a day, we had information for a specific patient for every variable that we are tracking. And moreover, the emotions, because it was self-reported, so the patients were not forced or coerced in any way to introduce emotions. Well, some patients tend to do it more often, right, that like to keep track of their mental health, others maybe less, and we ended up with only about 10% of the data being labeled. So even though we started with a quite large data set in the end, we lost quite some of it. So to deal with the missing data, which was the first challenge we had to tackle here, we decided to use generative models, namely hidden Markov models and mixture models, which learned the underlying patterns in the data and are able to like learn even the distributions even in the presence of missing data by marginalizing out with regards to the other variables. And we trained these models in a semi-supervised setting because we wanted to enforce the states of the model to recognize the different emotional states that we were considering. So this means that some of the states of the HMM would always emit negative emotions and some would always emit positive emotions, for example. And this way we managed to link the emotion better with the behavioral data. And after we trained these models, we used them to impute the missing values before performing classification. And how we did this was that we found the most probable state sequence for our observation sequence for a patient based on their behavioral data, leaving out the emotion information and then generated samples from the most probable states to fill in the missingness. Moreover, we also experimented with including the latent representation provided by these models as additional features for our classifiers. And we tried different temporal and non-temporal models. So we compared classical machine learning applications such as logistic regression support vector machines with RNN based temporal models. And we also defined a hierarchical regression model to try to better capture the individual differences between patients. And yeah, it's a short summary of the results. So we achieved, in some cases, larger than 70% area under the rock curve in the three class and five class problems. And as you can see, including the posterior significantly increased the performance. And also, as we found that accounting for these individual differences using the hierarchical model, we could gain a bit on performance, but the results were comparable to these. And then in the second project, which was a collaboration with psychiatrists from the Mount Sinai in New York, we aim to evaluate the usage patterns of social media and communication data from patients during the COVID-19 lockdown period in Madrid. So it was a short, relatively short period. And we wanted to relate this to their anxieties in terms that were measured using the seven item general anxiety disorder scale. And we had a small cohort of only 95 patients here from the mobile essence data site. We focused, as I said, on social media and communication app use. And alongside these we wanted to include other covariates that were relevant for anxiety in this situation. Such as information about whether the patients were exposed to COVID if they were living with someone else who was an essential worker. And if, yeah, like the perception of threats regarding their jobs, like losing their jobs and so on. So when looking at the data, we could see already that there were different patterns emerging in the temporal data and there was also quite a non-uniform distribution in the other features that we considered. And given the small data set we wanted to keep it simple and try to find a way to like combine this multi-model data into relatively easy and interpretable model. Yeah, so as I said here the target outcomes were the GED7 scores that the clinicians recorded via phone calls after the lockdown. And this is basically a seven item questionnaire where patients score how they feel in certain situations. And we defined the cutoff at 10 indicating like based on literature because then it's kind of a diagnostic value for screening. So, yeah, like agreeing with psychiatrists they seem to be a good cutoff. And we applied a simple pipeline where we reuse this idea of having a hidden mark of model modeling the temporal data. But here because we wanted to reduce the temporal data into a feature vector we actually aggregated the state posterior probabilities that we found for every sequence and concatenated that with the covariates that we were considering to then perform the classification with a simple linear regression. We tried more complex non-linear models but they improved like there was no improvement so this also gave some explainability. Another interesting thing that we found was that the three state HMM captured like quite interesting patterns in the data. So, yeah, I don't know how well you can see what's up there but we found that for example state three decoded the parts in the data where there was a low communication app use. And I think average, yes, like average social usage while the other states were capturing the more extreme values and when decoding the sequences we saw that state three actually was usually linked to those periods where there were no observations for a more consecutive time. And then I still have one minute so I will quickly wrap this up. So the last project I've worked on was trying to predict declining functionality. And here we also wanted to opt for multimodal approach where we combine the temporal observations of mobility descriptor variables and some social demographic information. Here, talking to clinicians and following their advice we included covariates such as patient age, gender, their employment status and cohabiting status. And yes, so again, we have the same overview of a data with a lot of missingness. Here the temporal sequences were represented as half hour windows for every feature so basically a day is captured in 48 slots of these windows. And well, our outcome of interest was the mobility domain of the World Health Organization disability assessment schedule, which asks patients if they had difficulty performing a given set of tasks in the past 30 days and then they get an overall score, the higher the score, the higher level of disability they have. We again categorized this and here the cutoff is at 25% where we consider like the healthy cohort people with less than 25% disability and everyone else was put together in the impaired cohort. Most patients only had one entry, only about 400 had two, but out of these 400 patients only 50 had actually a change in the score during the period of the observations. So it was a bit tricky. And here we applied deep learning pipeline where once again we used the valid working agent approach to impute first the missing values. And then once we had the imputed temporal sequences, we work with a pipeline constructed of basically some LSTM layers and the tensions. We included the attentions because we wanted to gain some insight into the important patterns for patients and we wanted to be able to compare what are the temporal patterns that the model would pay attention to more. For example, for a healthy or an impaired patient, and then well after we extract these like encodings or embeddings of the temporal data, we concatenated it with the socio demographic information to hear the predictions. And our preliminary results were somewhat low, but promising. We tested the model on a different cohort of patients and it did not manage to generalize very well. But as you can see, for example, on those plots, the important patterns in the data like variety between patients and this is something that we still want to look into. Yeah. Okay. Yeah, I had to like sentences about the ethical problems with this kind of research so we have to keep in mind that we are talking about the mental health of patients and this is kind of a field that can easily be abused. And there are certain aspects that have to be considered for safety and yeah. But as I'm running out of time, thank you very much. These are my collaborators and yeah. Thank you. Like to start. Maybe I have. What is a race tent. No, okay. I'm starting to hallucinate. The lab is working a lot on another type of time series ICU data sets from intensive care, which seems very similar at least on an abstract level to what you're modeling. So have you thought about applying these techniques to intensive care data sets like mimic or EICU? Well, actually, in a small experiment during my second month at Siemens, I tried using the HMMs for electrical record data. So it was lab measurements as well and yes, they did not seem to work that well, especially in the case when the time between measurements is very large. And like, of course, the different, for example, lab values there were collected at different time intervals. Here the time intervals are more more aligned because we have this summary of daily or half hourly behavior. So that makes it a bit easier. So these, these times years are quite nicely behaving. They are regular and not so. Yes, like frequency wise. Yes, even though there is a lot of missing, but like, yeah, in theory, we have always these 48 times lots of. Yeah. Good. Is there another question, comment. If not, then we thank MSHA again for her talk.


The following is a presentation of TFNN, the Tiger Technician Hour with your host, Hazel Chapman. Call now. Call free at 1-877-927-6648. Good morning, everyone. I'm Hazel Chapman. On this Monday, the 20th of November, can't believe we just about wrap up the month of November. Well, at least we've got Thanksgiving coming up. We've got Dow up 81, at 35,031, just a hair away from the 35,052 that starts leg C. Well, let me just show you this for some of you that it might be new to my methodology. Here we go. It's a real simple. I try to identify the lowest low bar and count each successively higher peak. A peak is made, look, this becomes a floating letter until it forms the inverted V, right? That becomes a peak, that becomes A. Usually it's gray, then it pulls back. If it doesn't take out that starting point, it continues up. It goes from a bicycle, maybe it gets upgraded to a bi-mode in B or off to B, but that says one penny above that peak A starts leg B until it makes a peak, then it becomes peak B. One penny above B goes to leg C. In my methodology, you can go A, B, C, D, E, F, G, that's seven higher peaks, but it's the fourth highest peak, peak D, where other things can happen. That's the objective is to get to the fourth highest peak and then reassess. With that context, you can see right here that if we go one penny above, well, it will be one penny above 35,051 point whatever it was, and that starts the leg C. Now within that context, look at this, the weekly chart has gone to a peak, a very sharp pullback. This is called a gray A, but the moment it had to, but if it doesn't, well, I don't know. There's a real good chance of 35,500 right there, 35,679. Is that right? Yes, 679. One penny above that starts your new leg up and that says if it continues here, it's an A. If it goes one penny above, it becomes an F because it didn't take out the starting point of this from the October law. This is a weekly chart because it didn't take that out at 28,660.  A says, are you kidding? Every single pullback should be bought. So we don't have to deal with that right now. Let's get the data because the data needs the weekly, weekly needs the monthly. Look at the monthly. Really gone sideways with higher highs and higher lows, slightly high highs, high high lows, and there's a real good chance that 36,952 will become a target the moment we take out this high that was right here. Was that the 30, yeah, that was it. That was the 35,679 level. All right. So within that context, we're looking at a positive market, looking at the S&P, do the same thing again. S&P is in leg C already. Now, I need to talk about this, I said to subscribers in my hour long overview over the weekend. I do this almost like a little webinar I give every single weekend for subscribers. How high this leg C goes is going to be really important because after a spectacular move like this, let me just go back to the Dow because it's a little bit more applicable to the Dow right now. If after a spectacular move from 32,827 to 35,051, if you take a few days to digest and then you break to the upside, but if you only go slightly higher, then you pull back and peak C is deeper than the trough of B, then you've got to be careful. Then what you're saying is now the upside is limited. We could get to a D and then stall, have a sideways consolidation or maybe a slightly deeper pullback, but not that deep, but deeper than you would if you had to make very much higher highs now. That would be important so that S&P is doing that right now. It's in the process of getting nicely above the high that was made in 452, it's trading at 45,21, now it's at 45,28, so it's starting to push away.  It's a propellant to the one to one to the upside. That really projects way to the 4607 last high that was made in July. Let's go one step at a time. This here is very non-balanced volume, the blue line, very overbought. The manky is very good, stochastic is fabulous at 97.07, it's almost at 100%, I mean that's great. It does say that at some point you've got to expect that you're going to go from the high 90s to the 70s or the 60s. You've got to be prepared for that, it doesn't say when, that's a different story altogether. The 9 is way above the 14, the price is way above the 9. Look at the QQQ, there's the index 100 trading vehicle, in leg C as we speak in the daily, but here's the issue. What's the wave count now in the weekly chart? Well now what I have to do is I had a gray A, gray because it was under the previous high. Now what I have to do is make it a blue E, oops, type it incorrectly, there you go, a blue E and I can keep it as a blue A, it doesn't matter, but the fact is I have an alternate count. Now I'm waiting for the MACD, the week is just barely started, so the MACD at this moment is just turning positive, well it's at 0-0, so it hasn't really, it hasn't given me a clue yet, actually I need to move this to the side here. Yeah, it's 0-0, so it hasn't, it's Monday, we've got all the way through to Friday even though the holiday is on Thursday and I think, I'm not sure if there's a half session on Friday or a full session or anyway, there is a session, I think we're closing Friday. Now what we're looking at here is the MACD is about to turn, the 9 is over the 14 and the price is way over the 9, that is really positive. Sycastic's finally got a big move up to 77%, the on-balance volume's a tad overboard, just like it is in the daily, but that just means it can have a bit of a pullback, it doesn't say, oh my god, now you've got to sell everything, no, it just says, you've got to hold on, but what's really important, we've got the falling axe formation, oh another technique, here we go, falling axes, where you go up sharply, start to make lower highs and much lower lows, then all of a sudden the price find support turns around, makes like a V shape or a cup shape formation takes out the falling axe, the expanding cone, the expanding declining cone down through the line, and then you can go to the each high on the left side if it's a cup formation, you wait to see if you're able to take out the high over there, the flagpole high, so in this case, we've done everything right, it's A, now it's starting to gray leg B, why is it a gray leg B? Well, first of all, we went to a G in the QQQ, there's never an H, so that says you have to find a new count, secondly, there's a chapter with instant restart, so yes, there could be a G slash C here, you can have an alternate count, but the slide from 408 in November of 2021 to the low of October of 2022 of 254, there's no question about everything that looks like that has to be called the brand new buy mode in leg B, and that there should be higher prices to come going into 2024 in the QQQs, actually says about all of them, but this one is confirmed, now let's go to IWM, IWM, let's say the day from the 160, if it took out the 160 level, it's making an arch formation, this is good action, not great, so it's doing okay, I want you just as we go to the break, I want to go to the semiconductor to say, look, that monthly chart is a brand new leg C, monthly chart is E slash A, and the baby chart is leg C, so far everything's positive, I'll be back down to 79. 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The gold report. New subscribers get a 30-day money-back guarantee so you have nothing to risk. Subscribe to Tom O'Brien's gold report newsletter now at TFNN.com. TFNN has launched the Tiger's Den, hosted at Discord. TFNN has been educating traders for more than 20 years with live programming hosted by a variety of professional traders during market hours. The Tiger's Den. Available to all Tigers and Tigresses for just one dollar for the year. There's no catch or added costs when you join our community of traders. Sign up today and become a part of this educational community of traders. Just visit the front page of TFNN.com. Toll free at 1-877-927-6648. Internationally at<PHONE_NUMBER>. Let's ask if I showed you my charts with the 10-way notation for the E-mini. Yeah, this is the S&P E-mini. There's a December contract. There's a beautiful cup formation. Went to D. Remember, we're just looking at D's. The C1, C2 is equivalent to a D. And I've got an alternate count here because there's a lot going on. I'm not sure yet. This is actually a leg B. Even though all the technicals are pretty good, I'm watching this very closely. It seems to me that there's a little struggle going on here. And I would look at this scans if I just need to do this. Give me a second. So what I'm looking at here is F. You see the nine-period moving average? We don't know because it's just the first time it's gone S since it crossed positive back at about 9.30 this morning. If this does turn out, then this whole area here of 45.36, it's at 45.39 will become important. And then there could be a touch of the 200-period moving average where it took off from back at 9.30, and that would be at 9.531. If it does get down to that level, what I would be looking at is what's happening to certain key stocks and I'll get to them in a moment. In the meantime, this does look to me like it's about to pull back. But it won't do anything because if the S turns back to green, to a B, one L for long, it just says it's stuck in a rectangle formation. It can stay that way for a little while. So as long as there isn't bad news, remember I have this thing about the Dow. I talk about it all the time. In other words, if the yield starts to accelerate higher, or if the yield starts to accelerate higher, or if the conflagration in the Middle East really flares up, we actually start to see gold really start to soar. Then we're looking at a situation where what I call dark news cloud cover, imperative, it means that as long as something there is warring the market, all the time there's a worry. But if it's something that the market actually perceives as negative, so it goes down sharply, then you've got to look at, I don't see anything right in this moment. There's more good news than bad news that the market is dealing with. So within that context, let's get back to our story over here. So estimators, as long as they're doing well in all-time highs, it's not necessarily a precursor to market action, but it's a very strong hint because a lot of the time where the estimators go, the semiconductors, remember I call them the oil of the 20th century, everything has chips, not potato chips, but regular semiconductor chips, and also oil. So there's a combination there.   Just the TLT, what do I think? TLT, look, if I was convinced that yields are going to come tumbling down, I would have been looking at the TLT instead of being at 89 right now and kind of stalling. Yes, I had a question about this. This is a Chapman Winston restart. I'll talk about that in a moment. What I wanted to say is for yields to really delineate and say, hey, we have now changed course. The tide has changed.  I would need to see a lot more. Within that context, a lot more essentially says, within the context of all the things that you look at, you want yields to be coming down. Let me just move this over here. The XLF, which is the S&P Financial Index, XLF, where it is, Fund, is holding really well. I'm just thinking that yields are at least stabilizing. I think that's really the picture. Now, to say they're going to go down sharply, yeah, but I need that proof. You can see the XLF holding really well right now at 35.01, well off the 31s, low that it was here recently. Now, if you go back to the TLT, yes, in the Chapman methodology, within three bars, if there's a new recovery high, you get a leg E to the upside. At that point, I usually call it E slash A. I don't even type it. I just say in my mind, say it's E slash A, if it goes high, it goes F slash B. Next one is G slash C, and that usually goes to a D. But this Chapman restart very often gives you a clue that, in fact, you're going to a whole new buy mode with another four higher peaks. I go one step at a time and I just say, this is positive, but until we get to this really important candle, the candle of the 22nd of September, that is at 91.61 high and 90.67 low, until we really get there. I'm just saying, yeah, it's acting quite well. And you can see the weekly chart, the 90s way under the 14, it's a big problem. All right, with that said, a couple of things I want to look at here is PLTR, I had a question, what leg is it in? PLTR is Ballantier Technologies, Diverse Data Fusion Platforms. I always like to say that, not really knowing what the fusion part means, other than that it's probably a mix of different things. And here it is, the weekly chart is ready in a leg D to the upside. That was a leg F at 20.24 in August, week of the fourth comes plunging down and makes a cup formation, goes back up and it's already in leg D. That's a leg C in the monthly and it's a leg C in the in the daily chart, acting really well. And it hasn't even thought of filling in the gap over that's what I always tell you, just don't fixate on gaps when they come into focus, start fixing it and fixating on them. Like a 200 period, would I even talk about 200 period moving average down at 14? Why? I've got these to worry about the nine, the 14 and the 50. So this is just a it's a work in progress. Okay, with that said, what I wanted to show you is this. So the question came in, it's spoken over the weekend. Oh, and first of all, Gigi, thank you so much for all that information on the EVs at what is it? I can't remember that Chinese city where they're having the EV, they have all these EVs, 57 EVs for show. We used to have the Boston auto show here in Boston. We haven't had it for years. I wish they would have it again. It'd be great to see. And I've been talking about this for some time. And I don't know if it's going to be accurate, but I think we're getting into a new phase here, where the looks that design like the 1950s design becomes absolutely fantastic for automobiles, especially with the electric cars coming up. It's going to be quite exciting. So with that said, I just wanted to show you Tesla. See now, Tesla is very interesting. Tesla's kind of stuck here. It's gone to a leg B and now it's a nine peak B way above the 200 feet moving average of the weekly, but that 200 feet moving average of the day, he says it's kind of stuck like a yo-yo up and down up and down from the 230 level. Now, the other thing that I've been looking thinking about over the weekend is, you have to wonder why Elon Musk, maybe because he's one of the richest people in the world or the richest person in the world. We don't really know because we don't know what some of the other countries, the leaders there, when they've siphoned off all the money. So I'm looking at this and saying, within the context of your products, why would you, why would you open your mouth? Just step back. You can have opinions, but don't, I mean, don't, don't cause this kind of friction and it's showing up in your stock, Tesla.  If it's illegal, you must calm down. And some people say, Hey, I love what he's saying, but I think that the majority of people are going to say, Jesus, I'm driving a car. Currencies, commodities and bond markets are as important as ever right now with how they're driving the volatility in equity markets across the globe, which is why it's a great time to try out Teddy Keg stat's tiger forex report. Teddy Keg stat breaks down the forex markets every Monday using his 30 plus years of experience as a trading veteran of futures, forex stocks and options. 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Steve Rhodes is committed to sharing his techniques and knowledge with anyone who wants to learn and he shares his vast amount of trading knowledge every day in his mastering probability newsletter. Steve's award-winning newsletter, Mastering Probability, is delivered every trading day with updates throughout the afternoon. Sign up for Steve's market newsletter, Mastering Probability and you'll receive access to seven of Steve's educational webinars absolutely free. At TFNN, all our newsletters come with a 30 day money back guarantee, so you have absolutely nothing to worry about. Visit TFNN.com and try Mastering Probability 30 days risk-free today. TFNN, educating investors. I mean I'm showing you the SOX index it came out back in 1994 I think it was and the high that was made in 2000 was at March of 2000 remember the Dow made its high in January and the S&P and the SMEs have made their highs in March. Yeah that was at 1362.10 and here I'd say that the last high oh wait a minute you might be right it was 4068 in January of 2022. Oh okay all right well you are correct because the SMEs went to a higher high oh very interesting you're absolutely correct the SOX did not make it and you're all-time high but the SMEs did I thought I said that I said the estimations I didn't know that I didn't actually check on the SOX so correct thank you for that correction and it's very interesting because when you get a diversion like that I was talking about that just for a minute there was a period over the decades where lows were made in the S&P and the semiconductors and six months later one of them made a low and the other didn't the other one was already usually was the seminars were already on their way up so it's just fascinating to me that there's also that rotation the other thing is that that was a six months that's what I recall a huge internal low and then a residual low all right so with that said there is a new all-time high in the SMHs not the SOX index which is the core this is the derivative this is the ETF and then semiconductor ETF next question I had was where did it go where did it go yeah so could you just you had spoken about your kind of benchmarks that you look at like URI which is the rental united rental Inc doing very well it's at a peak C in the daily a peak C in the weekly hasn't gone back to that previous high all-time high I believe this is an all-time high at 500 was it under 492.22 492.33 a two-bar reversal at an F slash C I think that's a C and it's going to go to a D coming up in this chamberwave inside track repellent line so this zone that I'm talking about right now whoops it didn't mean to hit that stand right now yeah you see this it means that make it pink it means the chamber inside track repellent zone could see a new high and then it sort of stalls now let me just talk about market conditions for a moment because you've got URI which is the united rentals you've got CTAS which is the Sintas Corporation overalls uniforms rentals and as we speak just off an all-time high which had made on Thursday at a peak C there's only a peak C this should go higher it broke that resistance level I'm calling this it can't be a G so this has to be a brand new leg B in the in the weekly chart that's a big big positive and look at this of egg E in the monthly chart it could even recycle could be anything right now but I've written a writing it in as an E fabulous this is overalls uniforms rentals so the so the economy is way better if you're looking at areas like hospitality or anything where your uniforms or even Sintas I saw recently in the whole cleansing area like the washing hands and all that so this is a very very important area what was the other one oh canapola just to be in in the large cyclical had a very good move up to the high it was made in july about 290 something plummets down to the 220s and now it's kind of stuck at 250 as is a legs a leg C in the dating chart but it is close it's making higher highs and higher lows and this is telling me together with PAVE which is the ETF Global X US Infrastructure and Development ETF it almost looks the same look peak C in the in the monthly chart very sharp pullback it's almost the chapter may fall in exclamation here it's a little unusual when you get these double tops where do you take your trend line I would love to take it from there all the way down but I can't I'm going to take it from here into the high of the candle right there not not the wick okay so then we'll do this right now just say there's a lot of work to be done in canapola to break to the upside if I'm using this as trend line resistance but once it gets to 30 I said PAVE that is PAVE once it gets to 32 this is a hop skip and a jump to make a new alt I believe this is an all-time high which will be at 32 30 32 39 was the highest of 32 40 becomes leg monthly leg D so look it's it's making high highs so this is really important and that's the reason why I said subscribers that we want to be buying want to be adding to positions but at the same time we still want to be a bit careful I still want to have stops in place because each stock is doing its own thing and that's a really important phase in markets to identify that not everything in a sector goes up at the same time so here's another question that came in could I look at uh oh did I write that alternative count oh that's right could I look at it there it is um you are I spoke about this just as we were closing out on Friday urnm which is the uranium spot uranium minus etf uranium so this is a leg E with a little doji can reform today above the previous high it's done everything that you would want I do this in as a an example for the for how a uranium position but you want to see that the core the uranium etf itself urnm is doing its job well it just broke to a new recovery high not all time high recovery high past that pt remember these were other things can happen that's where you can sometimes get biggest pullback well at 49 53 back on the 29th of September you made that peak D and plummeted all the way to 41 I would say that you know it's a 20 decline but then it went peak abcd and now it's in leg E with a little doji candle with the 9 period moving average very strong the mag D strong stochastic and 88 percent on balance for him says just be careful here it's a little bit overboard and the monthly chart is still improving still very strong a cup formation there as well big question will be and I I'm going to do this in now and we'll put it in and see what happens over a period of time over the past and now it's two years I've been saying that the the weekly and monthly charts so many of them especially monthly charts had a temporary instant restart that's within three bars if it makes a new leg above D that eventually turned out to be useful because we had alternate counts so this is an E but it could have an alternate count E slash A right so I'll put that in because it's all very important this then becomes the starting point and that's an A a gray A and this is a new high so that becomes F slash B doesn't mean oh you're not sure no it means everything is positive there's no question that this could certainly turn into a B but I've got to be prepared that with this little potential double top that there could be a bit of a pull back there so it's fine I keep the alternate count but look at the monthly chart this even bigger that that is the weekly chart you're looking at this is a monthly chart with a double top pattern I'll talk about that when we return because as patterns go this is really interesting I'll be back does it cause it's the gold report as a precious metal gold is still king it continues to hold the most effective safe haven and hedging properties across the global major trading hubs of the london otc market the us futures market and the shanghai gold exchange the gold report tom obrien publishes his weekly gold report every monday morning for subscribers consisting of coverage of the xau hui gdx the dollar bonds the south african rand as well as 25 different mining equities with specific buy sell recommendations the gold report new subscribers get a 30 day money back guarantee so you have nothing to risk subscribe to tom obrien's gold report newsletter now at tfnn.com are you ready to take your trading to the next level introducing tom obrien's award winning newsletter market insights your key to successful active trading tom obrien renowned for his expertise in the financial markets has designed market insights to be your daily guide to profitable trades tom publishes his daily market insights newsletter every market day before the market open along with updates when warranted stay ahead of the game with tom's real time analysis and trade recommendations delivered straight to your inbox whether you're a seasoned trader or just starting out market insights provides the edge you need to navigate the markets with confidence ready to join the ranks of successful traders head over to tfnn.com and subscribe to market insights today don't miss out on this opportunity to supercharge your trading results market insights comes with a 30 day money back guarantee for all new subscribers so you have nothing to risk don't miss out on this opportunity to revolutionize your trading game head over to tfnn.com right now to join the thousands of traders who have already experienced the power of tom obrien's award winning newsletter market insights first hand tfnn educating investors biotech is booming but for how long whether you think the biotech bull has room to run or has run its course trade labu or labd directions daily s and p biotech three times bull and bear etfs visit direction investments dot com slash biotech today an investor should consider the investment objectives risks charges and expenses of the direction shares carefully before investing the prospectus and summary prospectus contain this and other information about direction shares to obtain a prospectus or summary prospectus please contact direction shares at<PHONE_NUMBER> the prospectus or summary prospectus should be read carefully before investing an investment in the funds is subject to risk including the possible loss of principle the funds are designed to be utilized only by sophisticated investors such as traders and active investors distributor four side fund services llc this program is brought to you by vista gold traded on the nyse american and tsx under the symbol vgz i was asked if i would just explain the cup formation so this is the cup formation right here now this would be the natural fulcrum the plumb line the bottom it says the number of bars on the left that go down should equal the number of bars on the right you can see it's lopsized so if you started doing this here there's no use making that the the plum the plum line the the midpoint because you're already there i mean you and you haven't made it back to the high in the 49th so then i use the next i use either a particular candle i've got webinars on this in my uh music if you subscribe to a newsletter umpteen webinars they can go to and each one describes a different thing the left side right side how you choose that how you get the cup formation how you get the chat wave inside wedge target resistance line and that just said that if this occurred the way i've i've i've drawn it in that by today it should hit the 49 40 53 high of the 29th of september and it's done there but i also then say well now you've got to look at your technicals you don't want to say oh it hit it now we go oh that's no it's not a problem it's something to monitor but so the candle the day's young but it looks like it's a doji candle right now a doji candle could become a halfway marker or a reversal point so in this particular point uh you've got the mag d good the nine over the 14 the mag the mag d is not as good as it was before but it's rising as the castings fabulous it's 88 and the nine and the on little blue line on balance volume is a little bit overboard and this gray line the red strength at about 60 maybe 61 percent is very good so all of this is great now there's a chance of a double top you've got to be prepared for that but how it breaks out above this and then closes above it is going to be very important so the weekly chart this is a small time frame of this cup formation it's gone above the left side high but the week has only just begun we're an hour and a quarter into the week so you can't talk about as if it's closed then you go to this uranium we're looking at uranium spot uranium minus so i just wanted to tell you this is usually what i do on a saturday on a friday so i didn't have chance so here you're looking at the monthly chart so the monthly chart says where would i where would i try to find a midpoint because it doesn't look obvious from this low well then i move from the high and i just grab the bar and i go sideways and i say okay in this particular instance this particular candle right here this indecisive candle but that just takes you to where we are this month so if i did it that's really aggressive but i don't mind storing it off then saying hey if you miss you miss i'm not you you're the chart i'm just a chartist so it says this is the month and it's kind of aggressive and i say if i was to confirm it what technique would i use well i would use the chat with inside wedge target repellent line i'd go to a particular candle a particular trough or high or a candle and i say okay that's what i'd be looking at well it kind of fits for now so i'll keep it if it misses it misses but i have to choose something else and it just says in the month of november we really haven't got many days left how many days do we have today's the 20th we have one two three four five six seven eight eight days is plenty um to get close to that level yeah in fact look today's high is 49.984 and the high that we're talking about is 52 we could do it we'll see all right so i'll draw this in i always make it a dash line because it's a subsidiary line doesn't have to take the importance of a solid line and there it is okay and that just says that sometime the uh 52 level sometime this month we might touch 52 and that's what i like to wear support nine period moving average of 57 51 14 period moving average of 46.99 that's where i would start right next thing i was asked about is could i go to where was it um tw what is the question dude asks good morning basal your thoughts on tw would be appreciated is that uh tw is trade yeah i can't get to oh there it is so let me go to this chart right here and squeeze trade web okay trade web marketing what happens is they have it on this kind of dark gray background and then they put the black print fine print so what do i do first of all remember the double tasks we're looking at so this says that the high in the monthly chart of the december of 2021 102.33 102.32 i'll just put that in now so i don't have to waste time 102.33 and the low that was made not the most recent low but the low that was made in october or 52 was 51.47 all right so i've got those numbers there's a good cut in half 51.47 having a fabulous move so i don't know what this this if this was an earnings candle i'm just going to guess from the candle what it is a b c remember identify always check to the left whether this is the nearest low um our most obvious low that you can use for a starting point a b cd stalls looking ugly over here and almost a dreaded age pattern and suddenly it goes from 82 or so screams up to almost 90 in one day that's good and then what happens is it gives back a little bit a very little bit and then it starts to move even higher so normally what i would do is i'd go look at the chapter instant restart one two three bars so i go e looks a little messy i'm sorry to do that e slash a f slash b tend to get even messier g slash c and you remember what's my rule of thumb that invariably with the g slash c and especially in the strong move up it'll pull back and then it'll go to ad and that's where you've got to be careful well the high that was made on the 14th this is tw when i say it was trade web market i'm thinking it's a platform for trading obviously some kind of trading so it went to 5th and 93 93 on the 14th it pulled back quite sharply to the 90s and then it goes today to 94.03 yeah you've got your d it just says on a very short you didn't tell me uh whether you're a long short going to wanting to go to it i i was suspecting that it's on your list and that it you might be in it already if you are like it'll be nice to know uh dude let me just see maybe you've typed in something um all right so maybe maybe you're listening but you haven't typed in it so this is what i would do if you're not in it i love the action and i love the action because look going to the lowest low in the weekly chart b a b b this is how simple the technique is see it's when you get to d that other things can happen well low and well d it goes to e pulls back sharply so it's a brand new a b and we're in c right now so i like this very much my my eye says at 93 it wants to go nine points higher over the next few um it's it's going month and a half maybe two months could be quicker but i don't think i i don't know because we have to see how digest these gains could go sideways just for a little bit we saw that in the e-mini just a moment ago look we went in the e-mini you made a high and then you just went sideways with one quick pull back and now you're back above where you were so now you actually have so now you see the reason why i said alternate counting the 10 minute chart because now it's in f slash c and everything about it says um i think it's actually an f sorry i think i think it's c but you don't have to make those decisions because you've got your 10 and one minute charts to be thinking about that's the same as what we're looking at in this particular instance with tw we've got the daily chart acting really well the weekly chart acting well only thing is um what happens next so i'll talk about that when we return i like to talk very much the reality is that navigating financial markets can be risky markets can be chaotic and difficult to understand having the latest market advice can help you turn this chaos into a key for creating winning trades at tfnn we understand that it can be hard to find reliable market news that's why each of our market experts offers their very own market newsletter a must-have tool for every trader out there striving to find an edge in today's markets tfnn newsletters cover every aspect of the markets so you can analyze the market before you trade try any of our great newsletters risk free with our 30-day money back guarantee just visit the newsletters tab on the front page of tfnn.com tfnn educating investors you might think that if you want to be successful at trading in the stock market 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yeah so actually the question was i'd like to get in so tw i like it pretty much you're buying at a recovery high a monthly monthly i actually uh you're high or you're high i like it and i don't know any other way than to say look tiptoe in right here 93 59 but i would prepare to buy it in a slightly greater volume if it could pull back and hold i might have to wait to see so start your position you're 19 saying 95 say hey uh this is not the position this is just a started position because it could go higher then come back and retest it and even go a little lower how it holds the law the 15th of november of 1952 is absolutely imperative if it takes it out this is okay be careful because you can go down to the 88 89 area i like it a lot but it is still acting very well and it could go a tad higher before it does a digestive phase so let's look at it again in fact i'll make a note of it and i'll put tw either tuesday or wednesday but i have a look at it again but this is where you want to get your foot in the water i'd prefer if you could buy options on it i'd buy a 92 in the money a 92 50 or something like that option for i'd even go out to uh uh december probably even go out to january december or january but you didn't ask me so yes i like it tiptoe in but that's not your real position that's just get a feel for it next question was it could i look at u u u u which is in the uranium sector one two one two there it is one two one two come on there it is um yes it's actually very nicely um i i've got a target on this of nine dollars is an 854 um as i said before this is an a this is an alternative this is an e and a cup formation in the weekly chart your energy fuels uranium oh well it's gonna break coming up oh this is the end of the show um there was one more that you looked at and now i can't find it but yes the acting very nicely and yes ravelle wrote uh he's on a train trip i believe and it was a great success and was for about it so a great great action i didn't talk about microsoft tomorrow we'll talk about microsoft i want to see who closes the day


Unopened gift from ex under tree for 50 years, until wife finally puts foot down. It was the same day that she broke his heart that she gave him a gift. It was a present for Christmas, which was only days away. He promised himself that he'd never open it up, why should he? It was a gift from an ex who broke his heart. But 50 years came to pass in the blink of an eye. Is it still unopened? He went to George S. Henry Secondary School in Toronto, Canada, and when he turned 17, he met a girl named Vicky Allen. This was the first girl he fell in love with and had a serious relationship with. The two were together for a while, which made it hurt so much more when she broke his heart. With the swirling concoction of emotions that he felt, he made himself a promise out of the crushing feeling he felt inside. He promised himself that he would never open the parcel, not until the two got back together. But he was naive and they would never get back together, so the parcel would be left and forgotten. His ex fell out of contact and they both ended up together with other people and got married. They both forgot about their time together, but Adrian still remembered the gift. He would take the parcel and leave it under the Christmas tree with everyone else's presents, but it was never opened. It stayed sealed. This tradition stopped when his wife told him he was no longer allowed to put it under the tree. The kids would pester him and it was a strange tradition for a married man. After my family opened their gifts at Christmas, there was still one Christmas gift left, and it's the gift this girl Vicky had given me. I told my family I'm never going to open that present, Adrian said. Now that he wasn't allowed to open the gift he would keep it hidden away. But when Christmas was around the corner every year he would take it out and look at it. I kept it initially because I guess I had hopes that we would get back together and open it together, Adrian said. Now it's just become a habit after 47 years of looking at it and having the pleasure of not opening it. One holiday season, Adrian decided that he would post a picture of the parcel on his Facebook and added the background and story of the gift. Soon he got way more attention than he had expected. Hea don't want to know what's inside it. It's more exciting right now not opening the thing, he said. After all of the attention he got that he didn't expect, he decided that he'd try to find his high school sweetheart. He called numerous women after finding many phone numbers. There was a woman with the exact name of his ex that lived in Canada, but she was 91 years old and definitely not the same woman he dated. But then, he had found the right number after all these years. The phone had no answer the first two times he tried calling, and he thought about it, and decided to call her a third time. She answered finally, and they caught up over the phone and both agreed on an interesting way for the gift to be open at last. At the Christmas Bureau of Edmonton, which was a fundraiser for families in need, Adrian and Vicky decided that they'd open the gift there after 48 years of mystery. Adrian was still going to stay true to his word and not open the gift after nearly 50 years. So how would the gift get opened? The gift was a book entitled, Love is, New Ways to Spot that Certain Feeling. Which is rather inappropriate as Victoria herself admits in an interview, I do not recall buying the book, I do not recall wrapping the book, and certainly don't recall the book itself. In hindsight it couldn't have been more inappropriate under the circumstances, it couldn't have been less careless of his feelings, and it just goes to show how ignorant a 15 year old can be. The reason why Victoria didn't want to give him the book was also discussed at that same interview. She said, well yes it is a bit of a surprise and yet I know the back story that on the day that Victoria was buying this gift for me she fell for the other guy, so she figured she'd got the present for Adrian so I might as well just give it to him. It turned out that while Vicky was shopping for Adrian's gift at the mall, she met another boy, and he kissed her on the spot. It wouldn't have been so bad, but I kissed him back, Victoria said. She said she knew Adrian would find out she had betrayed him. She knew it was over between them. It was a very innocent relationship. We were in high school, Vicky explained about her relationship with Adrian. I didn't know he felt as intensely as he did. When Adrian was asked how he had finally found Vicky, he had a bit to say on the matter. It took quite a long time to establish a relationship again, I had to prove to her who I was and she had to prove to me that she was who she said she was. And there was a simple way to do just that. There were three women who said they were her, and we had to check them out because I asked them very specific questions about something we might have done on a date. She continued, two of them didn't answer this one did, and then we connected by telephone, and as soon as I heard her voice and her kind of mannerisms I was pretty sure it was her. Are you surprised that Adrian kept this present, putting under the tree year after year? Asked a reporter. She replied, surprised is a bit of an understatement. Shocked. When I first heard about it I was astounded that anybody would hold a grudge that long or possibly remember me that long or even be able to stand the temptation that long. He's since written a book about the whole experience and his wife and Alan contributed chapters. I think it's absolutely fantastic that we're friends. My wife is friends with Vicki, Adrian told the crowd. We're in a fantastic place where all you can feel is love. But now many people wonder just how do his wife really feel? Adrian still placed the dog-eared gift under the tree every Christmas because he enjoyed the mystery of it, but eventually his wife put her foot down when their daughter, then five, wanted to open it. She said, Daddy, when you die, I'll be able to open it then, right? I told him he could keep it, but we just didn't want the gift out in public, she explained. I'm pretty secure in our relationship. I've never been jealous. And all of this came to be due to a friend who knew them both in high school. He sent Victoria a link to one of the many articles written about it. Vicki responded by clicking like on some of Adrian's Facebook posts and Adrian figured out who it was. They got in touch, and eventually, Adrian and his wife were invited to meet her.


Quiet, peaceful paintings. 20 years ago, I started to plan this series of peaceful paintings. And this show at Moudam is the result of this long-lasting project. Large landscaping the very north of Luxembourg. The painting I made for the city of Trois-Bierges. The feeling of space. Nowadays, many people look at the world by staring at the screen. Through this painting, I wanted to give back the idea of living in a body in real space. The village of Holla, also in the very north of Luxembourg. Another work on customers' requests. As it offers me a certain independence as an artist, I do them. Humble paintings at first glance, yet the compositions reveal their complexity as soon as one has a closer look at them. The very last one of 13 mosaics that Mosaic master Carlo Signorini realized after sketches or paintings I made during the last three years of his life. In the tradition of the Ravana mosaics, yet free of dogma or ideology. The ancient cheese shale stone quarry in our village in summer. In the ancient days, until the 1950s, people took the stones from that place to build their houses. This mount in nature reminds me of my mother who died of a tumor 35 years ago. I also try to express how we treat nature through this kind of paintings, as far as painting can do this. Small landscapes with trees. After a heart attack in 2011, I was not able to work on large-scale paintings for almost one year. So I developed series with miniatures. And as these series have a very special charm, creating an intimate relation between the viewer and the image, as one has to have a closer look to really be able to see them, I continued the series of miniatures over the years. Three variations of the road leading to our village.  I often work on variations on the same subject, as what we consider being the same actually never is exactly the same. This special landscape also remembers me of certain book illuminations from the Middle Age, as well as certain Renaissance paintings. Actually, the larger of these three versions is an allusion to a late painting by Peter Braggill, the fall of Icarus, with the difference that in my painting Icarus crashes into a hedge on the left of the image, and that the farmer works with a tractor instead of a horse.