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This paper is available on under CC 4.0 license. Arxiv Authors: (1) Md Masud Rana, Department of Mathematics, University of Kentucky; (2) Duc Duy Nguyen, Department of Mathematics, University of Kentucky & ducnguyen@uky.edu. Table of Links Abstract & Introduction Datasets and Results Methods Conclusion, Data and Software Availability, Competing interests, Acknowledgments & References 4 Conclusion The study of protein-protein interactions (PPIs) and the prediction of mutation-induced binding free energy changes are of great importance in understanding the molecular basis of biological processes. The application of geometric graph theory and atom-level graph coloring techniques provides a powerful framework for analyzing biomolecules and capturing their intricate relationships. By utilizing the concept of geometric subgraphs and constructing multi-scale weighted colored geometric subgraphs (MWCGS), we can effectively represent the structural and functional properties of PPIs. The site-specific MWCGS features allow us to extract meaningful patterns and characteristics, shedding light on the effects of mutations and the underlying molecular interactions. In this work, we developed a mutation-induced binding free energy change predictor, called GGL-PPI, by incorporating site-specific MWCGS features for PPIs and gradient-boosting trees. Our method demonstrates superior performance compared to existing methods. The model was validated on three datasets: AB-Bind S645, SKEMPI 1.0 S1131, and SKEMPI 2.0 S4169 and S8338, showcasing its robustness and effectiveness. Furthermore, GGL-PPI was evaluated on a blind test set, the Ssym dataset. To prevent data leakage between the test and training sets, the model was trained on a homology-reduced balanced training set Q3488. This approach ensures the reliability and fairness of the evaluation process. GGL-PPI exhibits the most unbiased and superior performance in predicting binding free energy changes for both direct and reverse mutations, outperforming other existing methods, particularly for reverse mutations. Overall, the results highlight the potential of the GGL-PPI approach in accurately predicting mutation-induced binding free energy changes in protein-protein interactions, providing valuable insights into the molecular mechanisms underlying protein-protein interactions and facilitating drug design and discovery efforts. 5 Data and Software Availability The source code is available at Github: https://github.com/NguyenLabUKY/GGL-Mutation. 6 Competing interests No competing interest is declared. 7 Acknowledgments This work is supported in part by funds from the National Science Foundation (NSF: # 2053284, # 2151802, and # 2245903), and the University of Kentucky Startup Fund. References [1] Dana Chuderland and Rony Seger. 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PPIs and the Prediction of Mutation-Induced Binding Free Energy Changes: What It All Means

This paper is available on arxiv under CC 4.0 license. Authors: (1) Jeffrey Ouyang-Zhang, UT Austin (2) Daniel J. Diaz, UT Austin (3) Adam R. Klivans, UT Austin (4) Philipp Krähenbühl, UT Austin Table of Links Abstract & Introduction Related Works Preliminary Methodology Results Limitations Conclusion, Acknowledgements, & References Experimental Setting & Additional Results 7 Conclusion We present a method that efficiently scales thermodynamic stability prediction from single mutations to higher-order mutations. Our key insight is that the effects of mutations on the same protein are correlated. Thus, for a target protein, it suffices to run a deep backbone once and decode the effect of all mutations simultaneously using a shallow decoder. With the AlphaFold model as our backbone, our method outperforms existing methods on a variety of single and multiple mutation benchmarks. 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Predicting a Protein’s Stability
under a Million Mutations: Conclusion, Acknowledgement & References

THE PINE PROCESSIONARY: THE PROCESSION

THE DORTHESIA

THE LOCUSTS: THE LAST MOULT

THE LOCUSTS: THEIR FUNCTION; THEIR ORGAN OF SOUND

THE PINE-PROCESSIONARY

So far in 2022, the entire crypto market has been impacted by wider economic and political uncertainties, led by Bitcoin, with prices down more than 50% over the past six months and the price heading for the ninth consecutive red weekly candle. And while Terraform Labs successfully airdropped new Luna tokens to previous holders, the market sentiment continues to be bearish, with the remaining in the doldrums. Despite the pessimism, Banking giant recently that despite the crypto crash, its estimate of Bitcoin's fair value is $38,000. “The past month's crypto market correction looks more like capitulation relative to last January/February and going forward we see upside for bitcoin and crypto markets more generally," the bank’s strategists said. “Historically, buying into extreme fear levels in bitcoin has been a profitable strategy,” Sam Kopelman, a manager at the cryptocurrency wallet Luno, reported in . “That said, bitcoin has also seen continued sell-offs following a fearful market sentiment. Investors must remember that past performance is not indicative of future price movements. Therefore, as investors begin to re-enter the market, they must remain wary.” Fear and Greed Index JPMorgan said Yahoo Finance One way to avoid panic selling when the price of cryptocurrencies drop significantly is to remind yourself of the fundamentals.  Of course, that’s not so easy to say when you’ve lost money in Luna and seen your Bitcoin investments go down in value. “When the market is topping out, the stories are about internet-age Horatio Algers. When the market is bottoming, it’s about how the repo man took Mr. Alger’s Lambo so he could pay back loans he took out to run a carry trade on the Anchor protocol,” quipped Arthur Hayes, Co-Founder of 100x in a recent . blog It’s only relatively recently that Bitcoin has become correlated to the wider financial markets as more institutional money came flooding in with the easy money doled out by the Fed to alleviate COVID. “With the Fed in a difficult place, at least here in the United States, it's very difficult for a lot of these institutional shops not to treat it like a risk asset,” said top crypto influencer Anthony Pompliano. “That's exactly what they're doing right there. It's trading downward with stocks, it's trading down with other risk assets are selling across,” . he added The Mass Adoption of Crypto Assets Cryptocurrencies led by Bitcoin reached a tipping point in 2021 as they evolved from what many consider a niche investment to be a global, established asset class as venture capitalists poured money into the cryptocurrency market.  Overall, VCs invested more than $30 billion in crypto-assets and blockchain startups, with more than $10.5 billion invested in the fourth quarter of 2021 alone. With an estimated $10 globally in the first quarter of 2022, reportedly the largest amount to date, double the level for the same quarter in 2021. Looking at 2021 Galaxy Digital’s Head of Firmwide Research Alex Thorn said: “This was mainstreaming of crypto VC investing in a way we haven’t seen before. You also have a real diversification in the space. Yes, a lot went to trading and investing in the space, but you have other big categories that didn’t even exist in 2020 or 2019. That added a lot to the total capital invested,” he noted. Investment levels have continued despite Bitcoin price decline - as well as during another decline last summer. "This decoupling is demonstrative of investors' disbelief that a prolonged bear market in digital assets is forthcoming, as well as the significant amount of dry powder held by funds seeking to allocate to the sector," . he concluded Bitcoin is the Cryptocurrency of the Future One of the appeals of Bitcoin and other cryptocurrencies is that it removes friction in terms of costs and transaction speeds from payments, especially international transfers.  Indeed, according to Ark Invest, cumulative Bitcoin transfers have grown by more than 463% in the last year.  ARK analyst wrote in the report ‘Big Ideas 2022’ that Bitcoin will settle $13.1 trillion in 2021, a figure that even exceeds Visa’s payment volume. Ark Invest’s research also highlighted several areas where Bitcoin could take market share from traditional activities. These include international remittances, emerging market currencies, institutional investment and acting as a form of digital gold. Yassine Elmandjra ’ Source: ARK Invest s Yassine Elmandjra , Jan 25, 2022 tweet Innovation drives Bitcoin Although Bitcoin is not run by a centralized organization, it continues to grow along decentralized lines.  There is a small core group of developers working on improving the network, fixing bugs and security issues, and improving functionality. For example, last year Bitcoin implemented a major upgrade called Taproot to improve privacy, scalability, and security.  Another potentially significant move is the development of the Lightning Network, a layer 2 solution to Bitcoin that reduces costs and increases speed.  As reported in on May 30, “Bitcoin Lightning Network capacity attained an all-time high of 3915.776 BTC, as evidenced by data from Bitcoin Visuals, displaying a commitment to the cause of improving BTC transaction speeds and reducing fees over the layer-2 protocol.” This follows from the CEO of Strike, Jack Mallers, at the Bitcoin 2022 conference, that the company plans to collaborate with point-of-sale behemoths Shopify, NCR, and Blackhawk Network to revolutionize the payments industry. Cointelegraph news While there are good reasons to remain optimistic about Bitcoin, there are also still many things that investors and traders need to be careful about when investing in Bitcoin and cryptocurrencies. Cryptocurrencies are still a relatively new sector compared to traditional investments like stocks and funds, and while we don’t have certainty exactly how it will develop in the long term the potential is clear to see. “I believe Bitcoin is a viable long-term investment as a store of value looking to the future, especially with the price trending significantly upwards after each halving event,” said BigONE Chairman Anndy Lian. “It’s important for investors to recognize the fundamentals that it remains a hedge against inflation in the short term and a long-term asset that will continue to beat traditional financial assets.”

Why Bitcoin Remains the Financial Asset of Choice the Long Term

"All you need is ( ) AI in agriculture." – A slightly changed Beatles quote. love Modern-day farming gets smarter by the day: a symbiosis of cows and drones has never felt so natural and necessary. And surely it means that AgriTech creates more and more data while automating greenhouses through IoT, monitoring field variability in precision agriculture solutions, and so on. But have you ever felt like there must be something else this vast data collection can do? Something far bolder and way more ambitious? Well, ITRex has, and our believes that this data is dying to jump into an artificial intelligence solution and get agricultural companies more business insights. To prove our point, there’s no use in trying to dazzle you with random examples of how companies already use AI in agriculture because that won’t give you a true understanding of what this impressive piece of tech can do for agribusinesses. Instead, let’s see how basic agricultural processes in crop and animal farming can be enhanced with artificial intelligence, what benefits it brings, and what common misconceptions we need to steer clear of. AI consulting team Highlights: Benefits How AI fits agricultural processes: - In crop growing - In animal farming Common AI myths Benefits of AI in agriculture Before we start, let’s make it clear that artificial intelligence is not an annoying know-it-all tech. It only provides businesses with insights that stem from the data they already have — or automates processes that previously required human involvement. We’ll examine how it happens later on, and now let’s look at top four benefits that make agricultural AI a promising investment: Yield improvement. By analyzing operational data and highlighting process inefficiencies, artificial intelligence finds ways for agribusinesses to increase yields without using any extra resources. Automating crop and animal farming processes with drones (e.g., used for sowing or milking), eliminates human error and streamlines those processes to allow an increase in both quantity and quality of labor. Cost reduction. Crunching the numbers on how resources (water, energy, fertilizer, herbicide, etc.) are distributed and used, AI solutions spot wasteful resource consumption patterns and suggest optimization scenarios. Costs like vet services and equipment repairs are reduced by preventive practices of monitoring livestock health and equipment performance respectively. Profit increase. Given that AI maximizes yields without requiring farms to use any additional resources and cuts costs associated with various stages of agricultural processes, an increase in profits follows as a natural and pleasant consequence of employing AI in agriculture. Alignment with sustainable farming practices. The concept of sustainable agriculture revolves around finding a way to meet current food and textile needs without using up too many resources and leaving next generations with nothing. AI helps farmers find sustainable patterns of resource consumption to avoid water scarcity and land degradation. How AI use cases enhance basic agricultural processes As an intricate technology with multiple subsets and types, artificial intelligence can provide agriculture with algorithms and applications of varying levels of complexity. For example, you can use AI in agriculture for something as simple as monitoring livestock vitals to identify health problems. Or you can employ and classify tomatoes by variety using machine learning. Or you can go even further and predict weather using deep learning. If you do, AI will uncover weather patterns in historical data from satellites and sensors and spot pre-weather-change markers in real-time data to warn farmers about upcoming rains or storms. All in all, the tasks that AI carries out in agriculture can be boiled down to (using robots to carry out tasks that previously involved humans), (revealing inefficiencies by analyzing operational data), and personalization (growing sales by better adapting to demand). It may not seem like much but you can do a lot with a little. To get a better idea, let’s look at the basic processes of crop growing and livestock care. computer vision workflow automation data analytics Artificial intelligence and soil preparation By analyzing data coming from sensors placed in the soil, supplied by soil analysis drones, or sourced from smartphone cameras, an AI solution like can detect soil defects and recognize nutrient deficiencies. This helps farmers identify how much and what type of organic matter they should add to make the soil more workable and suitable for the given crop. Plantix Artificial intelligence and sowing Analyzing drone-supplied imagery, artificial intelligence can help farm workers identify places best suited for sowing particular crops based on geographical characteristics of the field, chemical composition of the soil, or any other parameter. Besides that, farmers can use AI-powered crop planning tools like to determine how much of each crop the farm needs to sow in a greenhouse per week, when the crops should be transplanted in the field, etc. Although a comprehensive crop plan goes way beyond the sowing process alone, it is still nice to know that by using such tools farmers can reduce herbicide and fertilizer consumption by 25-35% and increase yield by 3-4%. Before sowing begins, AI can also examine seed photos and compare them to images of healthy seeds; a supervised machine learning algorithm will determine seed quality and figure out what crops they are seeing. Using this data, AI algorithms can instruct farming robots (like a developed by the Agricultural Research Service of the US Department of Agriculture) to filter out subpar seeds or sort seeds if they get mixed. eAgronom computer-vision seed sorter Artificial intelligence and fertilizing Artificial intelligence systems — especially smart applications running on devices rather than in the cloud — require powerful chips. Unfortunately, for decades, the lion's share of computer chips have been manufactured in With the help of data from precision agriculture software, soil sensors, soil analysis drones, or simply smartphone images in case of Spacenus’s , agriculture artificial intelligence solutions can continuously monitor nutrition levels in the soil and, if needed, cross-check them with the levels that historically brought the best yields on the given crop. To make farming more sustainable, AI can use third-party datasets to check the environmental effects of using various amounts and types of fertilizer to find such a dosage that will minimize negative impact while maximizing yield. If the soil needs fertilizer, the artificial intelligence solution can recommend the best-suited fertilizer that the agribusiness has in stock at the moment using sensors in storage rooms and dispatch, say, a to spray it on the field. Agricultural Nutrient Assistant PrecisionHawk drone Artificial intelligence and irrigation The concept of sustainable farming requires smart usage of freshwater. And this is where AI tools like , , and come in handy. Some of them simply monitor water usage productivity, others automate irrigation workflows. But you can go further and use AI capabilities to analyze historical irrigation data, map it against crop health and yield stats, and find the best water consumption patterns that fulfill all required conditions. Using weather prediction capability provided by solutions like Fasal, farms can alter their irrigation plans making use of free rain water (checking its quality is also a possibility for AI in agriculture). Together with adjusted irrigation plans based on soil moisture monitoring, it can save farms up to 50% of water resources. Not bad, right? Similar mechanisms can be applied to energy consumption optimization as well. This way, an agricultural company can reduce water and energy-related costs without sacrificing productivity. Heliopas CultYvate WaPOR Artificial intelligence and crop protection This is the area where AI offers perhaps the most use cases for an agriculture business to enjoy. For example: Herbicide and pesticide consumption optimization is aimed at making farms sustainable and efficient while securing food safety. AI solutions detect current weed and pest activity and adjust herbicide and pesticide spraying activities to it instead of running these processes on a fixed schedule. This is enabled by companies like and that focus on weed control, as well as companies like and mobile apps like (delivered by the Food and Agriculture Organization of the United Nations and Pennsylvania State University), which take over pest control. Blue River Technologies PyTorch FarmSense Nuru Pest attack prediction is achieved by analyzing satellite or drone imagery, uncovering patterns in pest activity, and watching new incoming data to notice pre-attack signs. With this data at hand, farmworkers can prevent attacks without affecting crop health or using pesticides. For example, an early pest warning system by is used in India for cotton crop protection. Wadhwani AI Crop health monitoring is enabled by soil and plant sensors, as well as multispectral images sourced from satellites or drones. By using this data, AI solutions identify or, if more intricate unsupervised machine learning algorithms are applied, predict diseases in crops. This helps cut crop loss and increase yield. is an example of an app used for monitoring crop health on vineyards (however, it also covers harvesting and irrigation use cases). VineView Intruder identification is focused on securing field territory. AI solutions can monitor drone imagery or surveillance camera footage to detect wild animals and birds or unauthorized humans who can damage crops. For example, , a leader in the smart surveillance domain, enables farms to reduce crime and theft. Twenty20 Solutions Weather prediction can help farmers watch out for extreme weather conditions to save crops from damage before heavy rains or winds start. The for AI-powered precision agriculture allows doing just that. OneSoil app Artificial intelligence and harvesting By comparing current field footage to how this crop looked at this point in the growing cycle during the previous season, an artificial intelligence solution can accurately predict when the crop will be ready for harvest. And as soon as harvesting time comes, robots can start removing crops from the field. , for instance, offers a robotic harvesting solution to pick strawberries that minimizes waste, enhances food safety, and reduces CO2 emissions by 96% compared to traditional harvesting methods. If an agribusiness knows where the crops are transported to and sold afterwards, the solution can identify different harvesting points for same-variety crops that are planned for various locations based on how much time different crop batches will have to spend in transit. Harvest CROO AI in agriculture: animal farming Animal farming processes vary a lot based on what the end product is (milk, meat, eggs), so the simplified process below is not linear, some steps can occur in a different order or can be skipped altogether: Animal purchase/birth → Housing → Grooming and cleaning → Feeding and grazing → Health monitoring → Breeding → Harvesting Artificial intelligence and animal birth or purchase Analyzing data from previous reproduction cycles, AI farming solutions can predict how many animals are going to be born and calculate related care costs to enable tweaking reproduction plans if needed. If a farm buys animals, artificial intelligence technology can calculate how many animals the company needs. Such calculations can target the highest yield while balancing it with how many animals the farm can sustainably care for under given farming conditions. Artificial intelligence and housing Tapping into historical data on livestock diseases and correlating it with various housing conditions (humidity, temperature, animal density, etc.), artificial intelligence helps farmers establish the best conditions to keep animals in while also decreasing animal treatment costs. is a great example of a company that leverages AI to deliver solutions for smart cow housing. Lely Artificial intelligence and grooming and cleaning Using correlation capabilities and livestock sensors, AI can help select and schedule grooming and cleaning procedures so that farms don’t waste resources on excessive maintenance or badly affect livestock health in case of insufficient grooming and cleaning. Barn hygiene is a major factor in livestock health. That’s why companies like Lely and offer barn cleaning robots and agribusinesses like an Icelandic farm owned by the . By using cleaning workflow automation solutions in tandem with milking and feeding robots, the farm saw a 30% increase in milk outputs and slashed their vet costs by four times. GEA Hallgrímsson brothers Artificial intelligence and feeding and grazing It can be difficult to find the best balance between feeding and grazing, which is where artificial intelligence can help farmers. Besides that, AI can reveal: What foods work better for different types of livestock How much salt cows need to stay healthy based on breed, age, etc. How to schedule grazing around weather predictions for better livestock health How to manage pastures for better grazing quality (say, through paddock rotation or soil health monitoring) by Cainthus is an example of an AI farming solution that enables smart feed management, improving animal health and increasing milk outputs. ALUS Artificial intelligence and health monitoring Since disease management is the prime source of costs for animal farmers, this is the area where AI can bring the most value and allow animals to recover two to four times faster while getting less antibiotics in the treatment process. Artificial intelligence not only cuts disease management costs but also improves food safety. Besides all activities mentioned in other animal farming steps that lead to improving livestock health, AI can continuously monitor livestock’s vitals, body weight, metabolism, and other parameters to alert a vet if an animal’s health is worsening. AI solutions can also continuously look for new correlations between care conditions and livestock health to prevent diseases. Solutions like , , and ALUS are used for monitoring cows’ health, tracks bees’ wellbeing, and keeps an eye out for changes in the health and wellbeing of pigs Rex Ida ApisProtect Piguard Artificial intelligence and breeding Tools like Fertile-Eyez by are focused specifically on smart breeding and can identify livestock’s reproductive quality and help with fertility management. Solutions like Ida, meanwhile, give relevant breeding advice to farmers and cover AI use cases beyond animal reproduction. Another AI use case in this area covers breeding aimed at product quality improvement. If a farm tries to breed a new variety of cows to achieve better milk or meat quality, AI can crunch the data on relevant meat/milk parameters and reproductive qualities of available animals and suggest a ‘smart breeding recipe Verility Artificial intelligence and harvesting Most harvesting procedures are already automated by machines like milking, meat finishing, and even egg picking robots. Besides enabling and facilitating harvesting, AI uncovers ways to maximize output quality. For example, AI can use of chickens, pigs, and beef cattle to predict best harvesting points and cut over-finishing costs, as well as estimate body condition scoring, liveweight, milking traits, and lameness in dairy cows to maximize milk outputs. 3D imaging to estimate liveweight What are the most important uses of artificial intelligence in farming? With all the capabilities of AI in agriculture available today, farmers can tweak a lot of details about how they run their agribusiness: from automating workflows that previously were much more time-consuming to uncovering optimization opportunities for bits of their day-to-day agricultural activities. But however cool these tweaks may seem on their own, all of that is just a means to an end. The most important task that AI farming performs is predicting yield and optimizing agricultural conditions to sustainably push it up. What else does AI do for farming? Outside the described processes, there are some more AI use cases that can be relevant for agricultural companies, for example: Variety selection. Ahead of the season start, farmers can choose which crop variety to plant in order to satisfy demand. AI can help farmers achieve that with market analysis and calculations of potential costs and profits. Pricing strategy development. Based on the predicted costs of farming each crop or animal that are likely to pile up in the upcoming year, agribusinesses can adjust their pricing strategies to maximize revenue and still be able to offer competitive prices. Storage and transit conditions monitoring. To decrease product loss that results from poorly storing and transporting harvested goods, agribusinesses can use artificial intelligence to monitor and adjust temperatures, humidity levels, and other parameters in stock rooms and transit vehicles. The latter applies only if farms deliver goods to customers or farmers’ markets on their own. Predictive maintenance. Farms have relied on a wide range of farming equipment for a long time now and that range has recently been extended with numerous drones and robots. To reduce downtimes in case of farming equipment failures, agribusinesses can use AI to monitor equipment performance and notify maintenance teams about upcoming failures. What’s stopping AI and agriculture from being together? With all the abundance of artificial intelligence use cases at hand, you’d think that the popularity of AI in agriculture is at its peak. And while it is indeed growing, it’s not quite there yet. It seems like AI’s reputation of being a risky tech affair precedes it across industries and somewhat curbs its growth. Let’s see what causes an air of caution around artificial intelligence: The first reason would be concerns around AI costs. Obviously, this is a high-end technology that requires skilled workers and time. But we’re ready to bust the myth about its prohibitive cost. You can check in our blog post with examples drawn from our own projects, and you’ll see that it’s not all that scary. how much AI costs in 2021 It also doesn’t help to learn the staggering failure rate where 47% of AI projects don’t reach production. But once you see what’s under the hood of those failures, you understand that there’s simply a way to be smart about and not fall into the pit. The rule of thumb here is to know what you’re doing and start small – with a POC – to test the AI waters and prove their benefits in practice from the very beginning AI implementation challenges Thinking of exploring the powers of AI for your agricultural needs? Let’s think together! and their AI consulting team will locate the best way for AI to fit into your tech plans. Drop ITRex a line

All You Need is AI in Agriculture

I trained a “document classifier” to guess facts about encyclopedia articles, using: that links 4.7 million Wikipedia articles to 18.58 million facts from a new dataset from Google WikiData Facebook’s speedy text classification tool FastText Then I ran it over articles from , the biggest Lord of the Rings wiki, and got surprising results. Let’s dive in! Tolkien Gateway First off, what do I mean by facts from WikiData? is like Wikipedia, but with the knowledge structured in such a way that can use it well. Here’s an example: WikiData computers Here’s a more controversial one that clearly needed a reference: A few months ago Google did the dirty work of wrangling the data from Wikipedia and WikiData into one place, and put it in a massive zip file , and released about trying to use it to guess facts about Wikipedia articles. You can read about if you’re in the mood. on the internet a paper my initial exploration of it Meanwhile, Facebook have a tool called FastText that can learn associations between the phrases in a document and facts about said document. So in the above example, it can notice that the article about Shigeru Miyamoto uses the word “he” a lot more than it uses the word “it”, and contains the phrase “was born” instead of “was constructed”. So the next time it sees an article with those features, it might guess that the article is talking about a human instead of a bowling alley. (It also does this quicker than a lot of other methods, which is useful for people like me who don’t have access to Google’s supercomputers.) Once I was getting good results guessing facts about unseen Wikipedia articles, I decided to try it out on articles from Tolkien Gateway, my go-to resource for resolving arguments about whether Isildur’s dad is named or . Elendil Eärendil Arguments plural. People seem to really like Gandalf: So let’s start there. Gandalf What did FastText say were the most likely facts about Gandalf? instance_of= =maleoccupation= =United_Kingdommanner_of_death= =murdermovement= =blessedreligion= =World_War_IIposition_played_on_team___speciality===defender humansex_or_gender judgecountry_of_citizenship natural_causesconvicted_of Baroquecanonization_status Catholic_Churchconflict On the fact of it, these might seem a little silly. But keep in mind: Tolkien was a British man who fought in a World War, who was deeply Catholic. Maybe Gandalf is a subconscious ? self-insertion Re. the canonisation, Gandalf is one of the , which Tolkien Gateway describes as “angelic beings” Ainur Gandalf totally murdered a Balrog If anyone’s looking for a PhD topic, “ ” is a surefire winner. Gandalf as Goalkeeper, Sauron as Ronaldo: Post-Colonialism in Middle-Earth Baroque, eh? Did you know that the Lord of the Rings is partly based on ? an opera Legolas Rare holiday snap of Orlando Bloom sex_or_gender= =humaninstance_of= =Ancient_Romemanner_of_death= =Roman_Republicoccupation= =Ancient_Roman_senatorcanonization_status= =Islaminstance_of===Eunuch maleinstance_of mythical_charactercountry_of_citizenship capital_punishmentperiod militaryposition_held saintreligion Let’s go: Legolas is years old ( ). The Roman Senate was first founded years ago. Coincidence? 2931 disputed, see here 2771 He never had children, and had a fair singing voice. Is he a eunuch? . You tell me The Islam thing is left as an extremely difficult exercise for the reader. I’m not touching that. The Shire Describe by Tolkien as “more or less a Warwickshire village of about the period of [Queen Victoria’s] Diamond Jubilee” in 1897” country= =former_countryheritage_status= =Tolkien_s_legendariumdescribed_by_source= =UTC______IUCN_conservation_status===vulnerable United_Kingdominstance_of scheduled_monumentfrom_fictional_universe Nordisk_familjeboklocated_in_time_zone If you accept the theory that Middle-Earth is just our world a long time ago, then the Shire is definitely a former country inside the UK The Shire is a bastion of natural beauty, peace and elevensies, . It would’ve been desecrated ages ago if it wasn’t defended by star player Gandalf (and decades later by King Aragorn). It definitely deserves conservation status. under siege by greedy men The One Ring The next target for Google Translate conflict= =British_Armydescribed_by_source= =United_Kingdomsport= =midfieldermanner_of_death= =_____Summer_Olympics World_War_IImilitary_branch Dictionary_of_National_Biographycountry_of_citizenship association_footballposition_played_on_team___speciality natural_causesparticipant_of While The Ring was obviously a participant in the most important conflict of Middle-Earth, he was on the opposite side to Gandalf and co. But can we really blame my nascent AI for being fooled? Even Boromir son of Denethor got confused in the end about which side it was playing on. Little is known about The Ring’s career at the summer games, except that it bent all five Olypmic Rings to its will. Sam Cropped from a Gondor’s most wanted poster sex_or_gender= =fictional_characterinstance_of= =United_States_of_Americafrom_fictional_universe= =drug_traffickerconvicted_of= =natural_causesmember_of_political_party===Democratic_Party maleinstance_of humancountry_of_origin Marvel_Universeoccupation murdermanner_of_death I find it fascinating that Sam is one of the only people labelled as American in the whole legendarium (Éowyn being another). Please tell me your theories. As for the drug trafficker thing: The Ring is super addictive. Remember Bilbo’s face when he sees it again at Rivendell? Sam helps sneaks it across international borders in a dangerous manner He’s a gardener with herb lore and a fondness for the finest weed in the South Farthing extensive Éowyn instance_of= =fictional_charactersex_or_gender= =Marvel_Universecountry_of_citizenship= =sovereignoccupation= =Republican_Party humaninstance_of femalefrom_fictional_universe United_States_of_Americaoccupation lady_in_waitingmember_of_political_party My kingdom to anyone who can come up with a convincing theory for why Éowyn is a Republican while Sam is a Democrat, and why they’re the only two American mutants. Gimli instance_of= =malecountry_of_citizenship= =Australian_rules_footballercause_of_death= =mutantnative_language===French humansex_or_gender Japanoccupation heart_attackinstance_of I give up. Conclusion Further study is needed. (If you want to try reproducing my results, let me know, and I’ll put the FastText model up in a torrent for you.)

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