Lasso

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HackerNoon Reporter: Please tell us briefly about your  background. I am a Finnish American software engineer. What's your startup called? And in a sentence or two, what does it do? is a Google and Facebook authenticated bidding-fee-auction app for Amazon items. Exhibia.com What is the origin story? The original Yale University 1971 dollar auction game was digitized in the early 2000, however it was digitized incorrectly and commonsense auction initiation criteria was ignore. What do you love about your team, and why are you the ones to solve this problem? We launched one of the first penny auction sites in the USA and learned really early on the game what was fundamentally wrong and how to fix it. The “precommitment mechanism” in penny auction method is not formal and it doesn’t check that every live auction has the “minimum required number of bidders”. If you weren’t building your startup, what would you be doing? building software At the moment, how do you measure success? What are your core metrics? Signups What’s most exciting about your traction to date? Granted patents and we are generating users daily. 90% of software startups fail because no one wants to use them. What technologies are you currently most excited about, and most worried about? And why? Cryptocurrencies. The original inventor from Yale University Dr.Martin Shubik compares the dollar auction game to a coin-toss, or token-toss game. The auction method can be used with any crypto currency and we plan to launch our own Bidcoin. What drew you to get published on HackerNoon? What do you like most about our platform? Being a software-engineer myself, HackerNoon is an important source of information. What advice would you give to the 21-year-old version of yourself? Save money. What is something surprising you've learned this year that your contemporaries would benefit from knowing? That the auction operator needs to make sure before auction is started, that “minimum required number of bidders is present“. Exhibia was nominated as in Startups of the Year hosted by HackerNoon. one of the best startups in Miami

"90% of software startups fail because no one wants to use them" according to Exhibia CEO Miko Lasso

Ted Lasso’s leadership style of servant leadership is rare to see on the screen. I fully expected Ted Lasso just be a bunch of skits with Jason Sudeikis be a bumbling fool. The hook of an American Football coach leading an English football team was a great fake out. The actual show turned out to contain numerous… Heartwarming moments. Despite having zero knowledge of English Football, Ted Lasso makes A.F.C. Richmond a better team. I love that Ted doesn’t have a magical skill or tool that helped him achieve this. Instead, Ted Lasso’s leadership style starts with a core philosophy: Ted will do everything he can to . help others achieve their goals Ted’s leadership style struck a chord with me because I approach leading teams in the same way. Ted’s philosophy is why I’m here with you to explore the five leadership lessons from Ted Lasso that made me a successful leader. Read on to the end to hear about one more mind-blowing detail I noticed when re-watching Ted Lasso for this essay. Spoilers ahead if you have not watched Season 1. Disclosure: This article includes affiliate links to any books I reference. I will receive a commission for any purchases. Ted Lasso’s Leadership Lesson 1: Ignore the Haters with Clear Goals In first half of the season, Ted Lasso goes through a gauntlet of negative criticism. doubts him. Rebbeca consistently sets him up to fail. The players think he is a joke. His first game has him walk out to a . How does this environment affect Ted? Everyone stadium of people calling him a wanker It doesn’t phase him at all. His enthusiasm doesn’t waver. He ignores being called a wanker. He doesn’t even care if A.F.C. Richmond loses! Why does Ted not care what others think? Ted Lasso ignores the haters because he has a clear goal. Ted explains his goal when talking with Trent Crimm, played by James Lance, in Episode 3: For me, success is not about the wins and losses. It’s about helping those young fellas be the best versions of themselves on and off the field. And it ain’t always easy, Trent, but neither is growing up without someone believing in you. “Trent Crimm: The Independent.” Ted Lasso, episode 3, 2020. Apple TV+, This goal gives Ted clarity. He knows that helping others become the best version of themselves takes a long time. It requires focus, understanding, and incremental steps. The outside world doesn’t see the emotional work Ted puts in for the team, so he disregard them. Just as important though, are the people Ted Lasso does not ignore. These are the people that understand his goal. He listens to their feedback and puts it into action. Ted’s relationship with the community is a great example of . You’ll achieve more by understanding your goals, finding allies that support you, and focusing on the domain you have complete influence over. changing what you can control Ted Lasso’s Leadership Lesson 2: Each Person Matters Great leaders believe that everyone on the team matters. From the owners to the equipment manager, the team succeeds with everyone’s contribution. Since the start, Ted values everyone’s contribution to the team: Ted builds trust with Nathan by giving Nathan space to voice his opinion. Ted sees Higgins as a partner and regularly gets . lunch with him to build a bond Ted understands how important A.F.C. Richmond is to the community. He reciprocates this care by learning all he can about the town. Ted manages up to Rebecca, played by Hannah Waddingham, by bonding with her via “Biscuits with the Boss.” As the season goes on, Ted leverages these relationships to achieve his goals: Nathan contributes innovative new plays, which helps the team win games. Higgins supports Ted by advocating for Ted to Rebecca and tracking Dani Rojas’ health. Mae, the pub owner, provides Ted space to meet with the team to resolve “The Curse”. Rebecca mentors Ted in the season finale by showing that his lack of experience is a strength. Each of these moments depended on the relationships that Ted developed. He showed up. He paid attention. He supported each person. These relationships paid off such that when Ted needed help, each of these people aided him in turn. Remember that each person on your team and broader life matters. When you support them in their lives, they will help you in your times of need. Lesson 3: Teach Others to Believe in Themselves My favorite storyline in Season 1 is the growth of Nathan Shelley, played by Nick Mohammed. Nathan’s doubt in himself with how he’s astounded that Ted remembers his name in episode one. Nathan is the representation of someone who doesn’t believe in himself. He gets bullied, is intimidated by everyone, and feels that his ideas aren’t valuable. Ted changes Nathan Shelley with mentorship and advocacy. Ted builds Nathan’s confidence by . It starts with remembering Nate’s name, but Ted does so much more: consistently supporting Nathan with positive feedback Ted shows excitement about each donation box Nathan brings to the team. Ted welcomes Nathan’s ideas for new plays, which shows that great ideas come from anyone. Ted gets the team to stop bullying Nathan while also teaching leadership lessons to Roy Kent. Ted gives Nathan feedback that his suit doesn’t fit and then buys a new one for him. Ted cares deeply about helping everyone around him achieve their full potential. He doesn’t care about titles, skills, or background – everyone has room to grow. So, what does Nathan do with newfound belief in himself? Nathan Shelley roasts all of A.F.C. Richmond. Nathan’s words, something he didn’t believe was valuable, were the reason A.F.C. Richmond one a critical match. Everything snowballed for Nathan from here, resulting in my favorite moment of the show. Nathan being promoted to Assistance Coach. Sometimes, the main thing holding people back is believing in themselves. Great leaders, like Ted, build that belief by supporting their team everyday. Lesson 4: Surround Yourself with Experts We all know the hook of Ted Lasso – an American Football coach is asked to coach an English football team. Ted does not have the skills to succeed, and he’s aware of it! This knowledge is one of the reasons why he invests so much in the people around him. Ted surrounds himself with experts in English football to make up for his weaknesses. How does this play out? , we see Coach Beard, played by Brendan Hunt, reading and . Beard builds on this knowledge to own the tactical plays for the team. In the first episode Coaching Soccer for Dummies Inverting the Pyramid Ted pushes Nathan for feedback, which is often spot on. As the kit manager, Nate observes how the team behaves and has a deep knowledge of their weaknesses. Ted builds a relationship with Higgins, the Director of Communications, played by Jeremy Swift. Higgins understands how to navigate the team’s back office. Higgins’ experience is important when Ted manages up to Rebbeca. As the season progresses, we see Ted develop the specific skills to effectively coach A.F.C. Richmond. He even utilizes his weakness as a strength to create Trick Plays in the season finale. Turns out this approach is not too far from how I go about . What we learned from Ted is: managing existing engineering teams Surround yourself with experts in the domain you wish to lead. Stay curious and learn as much as you can by asking lots of questions. Become comfortable with your ignorance of the team’s environment. Your new perspective can be a strength. Lesson 5: Success Requires Teamwork The philosophical conflict of Season 1 is between Ted Lasso and A.F.C. Richmond’s ace player, Jamie Tartt. Ted prioritizes the personal development of the entire team. Jamie, played by Phil Dunster, only cares about being number one. Jamie is right to feel he’s the top player on A.F.C. Richmond. He’s loaned from a higher-performing team. Jamie is the only one scoring goals. Both Jamie and the community feels he’s carrying the team. Without him, the team will lose. Yet, even with him there, the team still loses. Football is a team sport. Ted knows this and understands that A.F.C. Richmond won’t win if he only focuses on Jamie. Ted works on the personal development in two ways: Working with individual players to elevate their specific skills. Creating an environment where the team trusts and supports one another. Some ways Ted develops the skills of individuals are: Building up the morale of Sam Obisanya, played by Toheeb Jimoh, by teaching him to “think like a Goldfish”. Teaching Roy Kent, played by Brett Goldstein, to be a compassionate leader. Ted pushes Roy to defend Nathan and mak him read a Wrinkle in Time. Ted Lasso’s Line: Prioritizing Only Yourself All of this effort is the reason why Ted gets angry at someone in the show. . Unsurprisingly, it’s Jamie Tartt Fortunately, Ted’s hard work pays off as trust is built within the team: Ted gets everyone to chip in for a birthday present for Sam to build his morale. Ted organizes a “Reverse the Curse” ceremony. In it, each player must sacrifice an emotionally significant item to the team. Ted has the entire team watch The Iron Giant to create an emotional experience for the team. It’s not until the team finally trusts one another that they start winning. The trust enables A.F.C. Richmond to beat Liverpool for the first time in 60 years. The players rely upon, support, and elevate one another throughout the journey. You can still Succeed when Losing Seeing everyone elevate one another is why I love the final moments of the season. In the game’s final moments, when success is in their hands, the team loses to Jamie and Manchester City. It’s not because the team failed, but that Ted’s definition of success won. To win the game, Jamie couldn’t go for the final goal himself. There was too much defense in front of him. So instead, he passed the ball and trusted his teammate to score the final goal. In the end, while A.F.C. Richmond lost, Ted still won. In the end, true success is the result of helping everyone around you achieve their goals. ⭐BONUS⭐: The Secret Principles of Ted Lasso’s Leadership Style When watching the first episode, there’s a scene where Ted and Coach Beard are setting up their office. It’s a simple montage, but Beard hangs up one picture that caught my eye: The Pyramid of Success. I had never heard of the Pyramid of Success before, but I had to learn more given the context. created the pyramid over his career. It contains 25 behaviors that build-up to create a successful life. Coach John Wooden Seeing this was mind-blowing to me. Not only does it define the foundation of Ted Lasso’s character, but there is also so much to learn from it. I look forward to reading more about in the future. Coach Wooden’s Pyramid of Success Conclusion I love the show Ted Lasso and can’t wait for Season 2. While I learned a number of lessons from Ted Lasso, there are many more in Season 1. What’s one lesson you learned from Ted Lasso about how to be a successful leader? Let me know on or . Twitter LinkedIn Before you go The purpose of Build the Stage is to . teach YOU how to be a successful leader If you learned something new from this essay, : I’d appreciate it if you’d complete one of the following actions : By sharing this essay, you help others grow their skills and spark discussion. Share this essay : What’s one thing you learned from this essay? Let me know on . Join the Discussion Twitter ! If you’re not a part of the Build the Stage community, it’s never too late. to never miss new ways to become more successful. Subscribe Sign up to the newsletter Further Reading Change What You Control How to Manage Existing Engineering Teams Creativity, Inc.: Overcoming the Unseen Forces That Stand in the Way of True Inspiration Amazon Bookshop Coach Wooden’s Pyramid of Success Amazon Bookshop

Ted Lasso’s Leadership Lessons Made Me a Successful Leader

Regression analysis is a statistical technique that models and approximates the relationship between a dependent and one or more independent variables. This article will quickly introduce three commonly used regression models using R and the Boston housing data-set: Ridge, Lasso, and Elastic Net. First we need to understand the basics of regression and what parameters of the equation are changed when using a specific model. Simple linear regression, also known as ordinary least squares (OLS) attempts to minimize the sum of error squared. The error in this case is the difference between the actual data point and its predicted value. Visualization of the squared error (from Setosa.io) The equation for this model is referred to as the cost function and is a way to find the optimal error by minimizing and measuring it. The gradient descent algorithm is used to find the optimal cost function by going over a number of iterations. But the data we need to define and analyze is not always so easy to characterize with the base OLS model. Equation for least ordinary squares One situation is the data showing multi-collinearity, this is when predictor variables are correlated to each other and to the response variable. To picture this let’s say we’re doing a study that looks at a response variable — patient weight, and our predictor variables would be height, sex, and diet. The problem here is that height and sex are also correlated and can inflate the standard error of their coefficients which may make them seem statistically insignificant. To produce a more accurate model of complex data we can add a penalty term to the OLS equation. A penalty adds a towards certain values. These are known as L1 regularization(Lasso regression) and L2 regularization(ridge regression). bias The best model we can hope to come up with minimizes both the and the : bias variance Variance/bias trade off (KDnuggets.com) Ridge Regression Ridge regression uses L2 regularization which adds the following penalty term to the OLS equation. L2 regularization penalty term The L2 term is equal to the square of the magnitude of the coefficients. In this case if lambda( is zero then the equation is the basic OLS but if it is greater than zero then we add a constraint to the coefficients. This constraint results in minimized coefficients (aka shrinkage) that trend towards zero the larger the value of lambda. Shrinking the coefficients leads to a lower variance and in turn a lower error value. Therefore Ridge regression decreases the complexity of a model but does not reduce the number of variables, it rather just shrinks their effect. λ) Lasso regression Lasso regression uses the L1 penalty term and stands for . The penalty applied for L2 is equal to the absolute value of the magnitude of the coefficients: Least Absolute Shrinkage and Selection Operator L1 regularization penalty term Similar to ridge regression, a lambda value of zero spits out the basic OLS equation, however given a suitable lambda value lasso regression can drive some coefficients to zero. The larger the value of lambda the more features are shrunk to zero. This can eliminate some features entirely and give us a subset of predictors that helps mitigate multi-collinearity and model complexity. Predictors not shrunk towards zero signify that they are important and thus L1 regularization allows for feature selection (sparse selection). Elastic Net A third commonly used model of regression is the Elastic Net which incorporates penalties from both L1 and L2 regularization: Elastic net regularization In addition to setting and choosing a lambda value elastic net also allows us to tune the alpha parameter where 𝞪 = 0 corresponds to ridge and 𝞪 = 1 to lasso. Simply put, if you plug in 0 for alpha, the penalty function reduces to the L1 (ridge) term and if we set alpha to 1 we get the L2 (lasso) term. Therefore we can choose an alpha value between 0 and 1 to optimize the elastic net. Effectively this will shrink some coefficients and set some to 0 for sparse selection. Preparing the data We will be using the following packages: library(tidyverse)library(caret)library(glmnet) We’ll also be using R’s built in Boston housing market data set as it has many predictor variables data(“Boston”, package = “MASS”) #set a seed so you can reproduce the resultsset.seed(1212) #split the data into training and test datasample_size <- floor(0.75 * nrow(Boston)) training_index <- sample(seq_len(nrow(Boston)), size = sample_size) train <- Boston[training_index, ] test <- Boston[-training_index, ] We also should create two objects to store predictor (x) and response variables (y, median value) # Predictorx <- model.matrix(medv~., train)[,-1] # Responsey <- train$medv Performing Ridge regression As we mentioned in the previous sections, lambda values have a large effect on coefficients so now we will compute and chose a suitable one. Here we perform a cross validation and take a peek at the lambda value corresponding to the lowest prediction error before fitting the data to the model and viewing the coefficients. cv.r <- cv.glmnet(x, y, alpha = 0) cv.r$lambda.min model.ridge <- glmnet(x, y, alpha = 0, lambda = cv.r$lambda.min) coef(model.ridge) We can see here that certain coefficients have been pushed towards zero and minimized while RM (number of rooms) has a significantly higher weight than the rest Ridge regression coefficients We now look at how our model performs by using our test data on it. x.test.ridge <- model.matrix(medv ~., test)[,-1] predictions.ridge <- model.ridge%>% predict(x.test.ridge)%>% as.vector() data.frame(RMSE.r = RMSE(predictions.ridge, test$medv),Rsquare.r = R2(predictions.ridge, test$medv)) RMSE = 4.8721 and R² = 0.7205 Performing Lasso regression The steps will be identical to what we have done for ridge regression. The value of alpha is the only change here (remember 𝞪 = 1 denotes lasso) cv.l <- cv.glmnet(x, y, alpha = 1) cv.l$lambda.min model.lasso <- glmnet(x, y, alpha = 1, lambda = cv.l$lambda.min) coef(model.lasso) x.test.lasso <- model.matrix(medv ~., test)[,-1]predictions.lasso <- model.lasso %>%predict(x.test.lasso) %>%as.vector() data.frame(RMSE.l = RMSE(predictions.lasso, test$medv),Rsquare.l = R2(predictions.lasso, test$medv)) RMSE = 4.8494 and R² = 0.7223 Performing Elastic Net regression Performing Elastic Net requires us to tune parameters to identify the best alpha and lambda values and for this we need to use the package. We will tune the model by iterating over a number of alpha and lambda pairs and we can see which pair has the lowest associated error. caret model.net <- train(medv ~., data = train, method = "glmnet",trControl = trainControl("cv", number = 10),tuneLength = 10) model.net$bestTune coef(model.net$finalModel, model.net$bestTune$lambda) x.test.net <- model.matrix(medv ~., test)[,-1] predictions.net <- model.net %>% predict(x.test.net) data.frame(RMSE.net = RMSE(predictions.net, test$medv),Rsquare.net = R2(predictions.net, test$medv)) RMSE = 4.8523 and R² = 0.7219 Conclusion We can see that the R mean-squared values using all three models were very close to each other, but both did marginally perform better than ridge regression (Lasso having done best). Lasso regression also showed the highest R² value.

An Introduction to Ridge, Lasso, and Elastic Net Regression

For many years, programmers have tried to solve extremely complex computer science problems using traditional algorithms which are based on the most basic condition statement: if this then that. For example, if the email contains the word “free!” it should be classified as spam. In recent years, with the rise of exceptional , the machine learning approach for solving complex problems has been magnificently accelerated. Machine learning is the science of providing computers the ability to learn and solve problems without being explicitly programmed. Sounds like a black magic? Maybe. In this post, I will introduce you to problems which can be solved using machine learning, as well as practical machine learning solutions for solving them. cloud computing technologies Exactly like humans learn on a daily basis, in order to let a machine to learn, you need to provide it with enough data. Once it processed the data, it can make predictions about the future. Assuming you want to classify emails by whether they are spam emails or not. In order to solve this problem using machine learning, you need to provide the machine with many emails — which are already classified in the correct classes of spam vs. not spam. The will iterate over the samples and learn what are the that define a spam email. Assuming you trained the machine learning model right, it will be able to whether a future email should be classified as spam or not, with high accuracy. In many cases, you’ll not be able to completely understand how the model predicts the class. labeled classifier features predict Machine learning hierarchy The world of machine learning can be divided into two types of problems: and . In this post, we will focus only on supervised learning, which is a subset of problems which contain data (That is, every email is labeled as spam or not spam). For cases where you have unlabeled data, unsupervised learning might be a proper solution. supervised learning unsupervised learning labeled Underneath the supervised learning problems, there is another division of problems vs. problems. In regression problems, the value you wish to predict is . For example, house price. In classification problems, on the other hand, the value you are about to predict is discrete, like spam vs. not spam. regression classification continuous The data you need to provide in order to train your model depends on the problem and the value you wish to predict. Let’s assume you want to predict a house price based on different properties. So in this case, each row in your dataset should (for example) consist of: house size, the number of rooms, floor, whether elevator exists, etc. features: house price. label: Choosing and collecting the features that best describe a house for predicting its price can be challenging. It requires market knowledge as well as access to big data sources. The features are the keys in which the prediction of the house price will be based upon. Machine learning as an optimization problem Every machine learning problem is basically an . That is, you wish to find either a maximum or a minimum of a specific function. The function that you want to optimize is usually called the (or ). The loss function is defined for each machine learning algorithm you use, and this is the main metric for evaluating the accuracy of your trained model. optimization problem loss function cost function For the house price prediction example, after the model is trained, we are able to predict new house prices based on their features. For each house price we predict, denoted as Ŷi, and the actual house price Yi we can calculate the loss by: l = ( Ŷi- Yi)2 This is the most basic form of a loss for a specific data-point, That is used mostly for algorithms. The loss function as a whole can be denoted as: linear regression L = ∑( Ŷi- Yi)2 Which simply defines that our model’s loss is the sum of distances between the house price we’ve predicted and the . This loss function, in particular, is called or . We wish to the loss function (L) as much as possible so the prediction will be as close as possible to the ground truth. ground truth quadratic loss least squares minimize If you followed me up until now, you are familiar with the basic concept of every practical machine learning problem. every machine learning algorithm defines its own loss function according to its goal in life. Remember, Linear regression Linear regression is a basic yet super powerful machine learning algorithm. As you gain more and more experience with machine learning, you’ll notice how most of the time. Linear regression is widely used in different supervised machine learning problems, and as you may guessed already, it focuses on regression problem (the value we wish the predict is continuous). It is extremely important to have a good understanding of linear regression simple is better than complex before studying more complex learning methods. Many extensions have been developed for linear regression which I will introduce later in this post. The most basic form of linear regression deals with dataset of a single feature per data point (think of it as the house size). Because we are dealing with supervised learning, each row (house) in the dataset should include the price of the house (which is the value we wish the predict). An example of our dataset: In a visual representation: In linear regression we wish to fit a function (model) in this form: Ŷ = β0+β1X Where X is the vector of features (the first column in the table below), and β0, β1 are the coefficients we wish to . learn By learning the parameters I mean executing an iterative process that updates β at every step by reducing the loss function as much as possible. Once we reach the minimum point of the loss function we can say that we completed the iterative process and learned the parameters. Just to make it even more clear, the combination of the β coefficients are our trained model — which means that we have a solution to the problem! After executing the iterative process, we can visualize the solution on the same graph: Where the trained model is: Ŷ = -0.5243+1.987X Now let’s assume we want to predict based on our trained model, what will be the price of a house of size 85. In order to predict the price, we will substitute the β values we found into the model function, including the house size, and get the predicted house price: Ŷ = 168.37 To recap what we’ve covered so far: Every machine learning problem is basically an optimization problem. That is, we want to minimize (or maximize) some function. Our dataset is consist of features (X) and a label (Y). In our case — house size is the single feature, house price is the label. In linear regression problems, we want to minimize the quadratic loss which is the sum of distances between the predictions and the actual value (ground truth). In order to minimize the loss function and find the optimal β coefficients, we will execute an iterative process. To predict the label (house price) of a new house based on its size, we will use the trained model. The iterative process for minimizing the loss function (a.k.a learning the coefficients β), will be discussed in another post. Although it can be done with one line of code, I highly recommend reading more about iterative algorithms for minimizing loss functions like . Gradient Descent Linear regression with multiple features In real world problems, you usually have more than one feature per row (house). Let’s see how linear regression can help us with multi-feature problems. Considering this dataset: So currently we have 3 features: house size number of rooms floor Therefore, we need to adapt our basic linear model to an extended one that can take into account the additional features for each house: Ŷ = β0+β1X1+β2X2+β3X3 In order to solve the multi-feature linear regression problem, we will the same iterative algorithm and minimize the loss function. The main difference will be that we will end up with four β coefficients instead of only two. Overfit in machine learning algorithms Having more features may seem like a perfect way for improving the accuracy of our trained model (reducing the loss) — because the model that will be trained will be more flexible and will take into account more parameters. On the other hand, we need to be extremely careful about the data. As we know, every dataset has noisy samples. For example, the house size wasn’t measured accurately or the price is not up to date. The inaccuracies can lead to a low-quality model if not trained carefully. The model might end up memorizing the noise instead of learning the trend of the data. overfitting A visual example of a nonlinear overfitted model: Overfit can happen in linear models as well when dealing with multiple features. If not filtered and explored up front, some features can be more destructive than helpful, repeat information that already expressed by other features and add high noise to the dataset. Overcoming overfit using regularization Because overfit is an extremely common issue in many machine learning problems, there are different approaches to solving it. The main concept behind avoiding overfit is simplifying the models as much as possible. Simple models do not (usually) overfit. On the other hand, we need to pay attention the to gentle trade-off between overfitting and underfitting a model. One of the most common mechanisms for avoiding overfit is called regularization. Regularized machine learning model, is a model that its loss function contains another element that should be minimized as well. Let’s see an example: L = ∑( Ŷi- Yi)2 + λ∑ β2 This loss function includes two elements. The first one is the one you’ve seen before — the sum of distances between each prediction and its ground truth. The second element though, a.k.a the regularization term, might seem a bit bizarre. It sums over squared β values and multiplies it by another parameter λ. The reason for doing that is to “punish” the loss function for high values of the coefficients β. As aforesaid, simple models are better than complex models and usually do not overfit. Therefore, we need to try and simplify the model as much as possible. Remember that our goal of the iterative process is to minimize the loss function. By punishing the β values we add a constraint to minimize them as much as possible. There is a gentle trade-off between fitting the model, but not overfitting it. This approach is called . Ridge regression Ridge regression Ridge regression is an extension for linear regression. It’s basically a regularized linear regression model. The λ parameter is a scalar that should be learned as well, using a method called that will be discussed in another post. cross validation A super important fact we need to notice about ridge regression is that it enforces the β coefficients to be lower, but it enforce them to be zero. That is, it will not get rid of irrelevant features but rather . does not minimize their impact on the trained model Lasso method Lasso is another extension built on regularized linear regression, but with a small twist. The loss function of Lasso is in the form: L = ∑( Ŷi- Yi)2 + λ∑ |β| The only difference from Ridge regression is that the regularization term is in absolute value. But this difference has a huge impact on the trade-off we’ve discussed before. Lasso method overcomes the disadvantage of Ridge regression by not only punishing high values of the coefficients β but actually setting them to zero if they are not relevant. Therefore, you might end up with fewer features included in the model than you started with, which is a huge advantage. Conclusions Machine learning is getting more and more practical and powerful. With zero knowledge in programming, you can train a model to predict house prices in no time. We’ve covered the basics of machine learning, loss function, linear regression, ridge and lasso extensions. There is more Math involved from what I’ve covered in this post, I tried to keep it as practical and, on the other hand, high-level as possible (Someone said trade-off?). I encourage you to take a deep dive into this amazing world. Check out my blog for more posts to come — — coders with entrepreneurial mindset. https://codingStartups.com

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A Sparse Bayesian Learning for Diagnosis of Nonstationary and Spatially Correlated Faults:References

This paper is available on arxiv under CC 4.0 license. Authors: (1) Ajay Krishnan T. K., School of Digital Sciences; (2) V. S. Anoop, School of Digital Sciences. Table of Links Abstract & Introduction Related Studies Materials and Methods Proposed Approach Results and Discussions Conclusions and References 2 Related Studies This section provides an overview of recent and influential research papers in machine learning and natural language processing, specifically on climate change analysis. It also discusses relevant studies that explore sentiment text classification approaches that are pertinent to the proposed project. The reviewed studies have demonstrated the effectiveness of sentiment analysis and named entity recognition techniques in the context of climate change analysis. NLP models such as BERT and attention mechanisms have shown promising results in capturing contextual information and improving performance. These studies provide valuable insights and methodologies to guide our approach in implementing sentiment analysis and named entity recognition on climate change-related tweets and texts using the ClimateBERT pre-trained model. A study was conducted to assess the effectiveness of ML algorithms in predicting long-term global warming. The research examined algorithms such as LR, SVR, lasso, and ElasticNet to connect average annual temperature and greenhouse gas factors. By analyzing a dataset spanning 100-150 years, the study found that carbon dioxide (CO2) had the most significant impact on temperature changes, followed by CH4, N2O, and SF6. Using this information, the researchers were able to forecast temperature trends and greenhouse gas levels for the next decade, providing valuable insights for mitigating the consequences of global warming. The research analyzes public sentiments regarding climate change by studying Twitter data. The study aims to tackle the problems of polarization and misinformation that often arise during climate change discussions on social media platforms. To achieve this, the researchers introduce a multitask model named MEMOCLiC, which combines stance detection with additional tasks like emotion recognition and offensive language identification. By employing various embedding techniques and attention mechanisms, the proposed framework effectively captures specific characteristics and interactions related to different modalities. Experimental findings highlight the superior performance of the MEMOCLiC model in enhancing stance detection accuracy compared to baseline methods. This research paper examines the issue of polarization and belief systems prevalent in climate change discussions on Twitter. The paper proposes a framework that aims to identify statements denying climate change and classify tweets into two categories: denier or believer stances. [Sham and Mohamed, 2022]The framework focuses on two interconnected tasks: stance detection and sentiment analysis. Combining these tasks, the multi-task model utilizes feature-specific and shared-specific attention frameworks to acquire comprehensive features. Experimental results demonstrate that the proposed framework enhances stance detection accuracy by leveraging sentiment analysis, outperforming uni-modal and single-task approaches. This research paper utilizes the BERT model and convolutional neural network (CNN).[Lydiri et al., 2022] The study analyzes public opinions on climate change by examining Twitter data. The results indicate that the proposed model surpasses conventional machine learning methods, accurately identifying climate change believers and deniers. The authors suggest this model has significant potential for monitoring and governance purposes, particularly in smart city contexts. Additionally, future work involves investigating alternative deep learning algorithms and expanding the analysis to encompass other social media platforms. This research paper [Ceylan, 2022]investigates the application of AI and NLP models to analyze extensive unstructured data concerning climate change. The study primarily aims to develop an information management system capable of extracting pertinent information from diverse data sources, particularly technical design documentation. By utilizing pre-trained AI-based NLP models trained on textual data and integrating non-textual graphical data, the researchers showcase the system’s effectiveness in swiftly and efficiently retrieving precise information. The ultimate objective is to promote knowledge democratization and ensure the accessibility of information to a broad user base. This research paper examines people’s emotions and opinions concerning the conflict between Russia and Ukraine by employing ML and DL techniques. [Sirisha and Bolem, 2022]The study introduces a novel hybrid model combining sequence and transformer models, namely ROBERTa, ABSA, and LSTM. To conduct the analysis, a large dataset of geographically tagged tweets related to the Ukraine-Russia war is collected from Twitter, and sentiment analysis is performed using the proposed model. The findings indicate that the hybrid model achieves a remarkable accuracy of 94.7, surpassing existing approaches in sentiment analysis. The study underscores the significance of social media platforms such as Twitter in gaining insights into public sentiment and opinions regarding global events. This research paper aims to overcome the limitations of general language models in effectively representing climaterelated texts. The authors introduce CLIMATEBERT, a transformer-based language model that undergoes pretraining on a vast corpus of climate-related paragraphs extracted from diverse sources such as news articles, research papers, and corporate disclosures.[Webersinke et al., 2021] Comparative evaluations reveal that CLIMATEBERT surpasses commonly used language models, exhibiting a substantial 48 enhancement in a masked language model objective. The improved performance of CLIMATEBERT contributes to lower error rates in various climate-related downstream tasks. To encourage further research at the intersection of climate change and natural language processing, the authors provide public access to the training code and weights of CLIMATEBERT. This research paper uses ML algorithms to analyze and predict climate change. The authors emphasize the significance of comprehending and adapting to the impacts of climate change on both human society and the environment. The study discusses the application of ML methods in analyzing historical temperature data and carbon dioxide concentrations dating back to the 18th century. It emphasizes the potential advantages of employing machine learning and artificial intelligence in interpreting and harnessing climate data for simulations and predictions. Multiple machine learning algorithms, such as DT, RF, and ANN, are examined for climate change risk assessment and prediction. The authors conclude that integrating machine learning techniques can enhance climate modeling, enabling informed decision-making concerning climate change mitigation and adaptation strategies. This research paper introduces the ClimaText dataset[Varini et al., 2020], specifically developed to detect sentence-level climate change topics within textual sources. The authors emphasize the significance of automating the extraction of climate change information from media and other text-based materials to facilitate various applications, including content filtering, sentiment analysis, and fact-checking. Through a comparative analysis of different approaches for identifying climate change topics, they find that context-based algorithms like BERT outperform simple keyword-based models. However, the authors also identify areas that require improvement, particularly in capturing the discussion surrounding the indirect effects of climate change. The authors anticipate this dataset will be a valuable resource for further research in natural language understanding and climate change communication. [Upadhyaya et al., 2022]It underscores the importance of comprehending public perception and acceptance of climate change policies. The study examines diverse data sources, such as social media, scientific papers, and news articles, to perform sentiment analysis. ML techniques, specifically SVM, are evaluated for extracting valuable insights from these data sources. The paper concludes that supervised machine learning techniques exhibit effectiveness in sentiment analysis, highlighting that ensemble and hybrid approaches yield superior outcomes compared to individual classifiers.

ClimateNLP: Analyzing Public Sentiment Towards Climate Change:  Related Studies 

This paper is under CC 4.0 license. available on arxiv Authors: (1) Marcin W ˛atorek, Faculty of Computer Science and Telecommunications, Cracow University of Technology, ul. Warszawska 24, 31-155 Kraków, Poland and marcin.watorek@pk.edu.pl (M.W.); (2) Jarosław Kwapie ´n, Complex Systems Theory Department, Institute of Nuclear Physics, Polish Academy of Sciences, Radzikowskiego 152, 31-342 Kraków, Poland and jaroslaw.kwapien@ifj.edu.pl; (3)  Stanisław Drozd˙ z, Faculty of Computer Science and Telecommunications, Cracow University of Technology, ul. Warszawska 24, 31-155 Kraków, Poland, Complex Systems Theory Department, Institute of Nuclear Physics, Polish Academy of Sciences, Radzikowskiego 152, 31-342 Kraków, Poland and stanislaw.drozdz@ifj.edu.pl (S.D.). Table of Links Abstract and Introduction Data and Methodology Results and Discussion Conclusion and References 4. Conclusions Based on the multiscale cross-correlation analysis performed for the data covering almost the last three years, it can be concluded that the cryptocurrency market dynamics is substantially tied to the traditional financial markets. Consistently, the most liquid cryptocurrencies, BTC and ETH, cannot serve as a hedge or safe haven for the stock market investments, not only during the turbulent periods like the Covid-19 panic, where this effect is particularly strong, but also during the recent bear market period on tech stocks, which has been accompanied by the parallel bear market on cryptocurrencies. Many observations show that the Covid-19 pandemic may have changed the paradigm that the cryptocurrency market is a largely autonomous market. The recent market developments and the strong US dollar have additionally increased the strength of the cross-correlations between BTC and ETH on the one side and the US tech stocks on the other side. These observations support some earlier findings on the same subject (e.g., [22,70]). In contrast, as the cryptocurrency market was weakly correlated with other markets during 2021, our results cannot support directly a recent hypothesis that the quantitative easing could actually be responsible for these correlations [22]. The existence of links between the global economy and the cryptocurrency market are further strengthened by the reaction of the price changes of BTC and ETH to economic data, such as CPI inflation, in a similar way to traditional financial instruments. These results are able to remove or, at least, to suppress the uncertainty that recent literature on this topic has brought to the cryptocurrency investors. Now it is more clear that the cryptocurrencies can no longer serve as a convenient hedging target for the investors whose purpose is to diversify the risk. Our study brings a strong indication that the cryptocurrency market has finally become a connected part of the global financial markets after 12 years of the maturation process. Whether such a direction of this market evolution remains in agreement with the early vision of the cryptocurrency creators can be disputed, however. We also face a related question: does the fact that we have got “just another part of the global financial market” deserve devoting so huge amounts of energy to it? Sooner or later this question must be addressed by the policy makers. Nevertheless, what becomes evident now is that it allows the market participants to broaden the spectrum of their investment possibilities. Among the limitations that might have influenced our study and, subsequently, our conclusions, we have to mention that only two principal cryptocurrencies were studied. Although they are the most influential, the most frequently traded, and widely discussed cryptocurrencies, they by no means define the entire market. It is possible that an analysis that included some less important cryptocurrencies would bring different outcomes. This is especially likely for the marginal cryptocurrencies without any thinkable “fundamental” value, whose dynamics is driven predominantly by extreme speculation. However, as the cryptocurrency market is looked at by the most investors through the lens of BTC and ETH (as their capitalization indicates), this particular limitation does not seem discouraging to us. Currently, these two assets shape the whole cryptocurrency market and we expect them to continue doing it in the nearest future. Another limitation of our analysis is the particular selection of the traditional financial instruments. Indeed, they constitute only a small fraction of the available ones. We are convinced, though, that they are among the most observed and the most influential ones in the context of the global economy, which fully justifies our choice. A more general observation that the cryptocurrency market has spontaneously coupled to the technological sector of the stock markets by reacting to some trigger provided by the external data inflow resembles analogous effects of the spontaneous emergence of order among the so-far independent degrees of freedom in the various complex systems. However, as complexity allows for flexible reacting of a system to both the external perturbations and internal processes, such effects of ordering in the financial markets have to be eventually counterbalanced by the opposite processes of disordering. Therefore, the market participants must be aware that the inter-market couplings may not last forever and they can significantly be weakened or even removed completely at some point in future. This is why the in-depth studies of the cross-market dependencies have to remain among the principal directions of the cryptocurrency research. Our future work will also deal with energy consumption of the cryptocurrency market. Conceptualisation, S.D., J.K. and M.W.; methodology, S.D., J.K. and M.W.; software, M.W.; validation, S.D., J.K. and M.W.; formal analysis, M.W.; investigation, S.D., J.K. and M.W.; resources, M.W.; data curation, M.W.; writing—original draft preparation, M.W.; writing review and editing, S.D., J.K. and M.W.; visualisation, M.W.; supervision, S.D. All authors have read and agreed to the published version of the manuscript. Author Contributions: This research received no external funding. Funding: Data available freely from Dukascopy [58]. Data Availability Statement: The authors declare no conflict of interest. Conflicts of Interest: References 1. Kwapie ´n, J.; Drozd˙ z, S. Physical approach to complex systems. ˙ Phys. Rep. 2012, 515, 115–226. 2. 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Integration of Cryptocurrency into the Global Financial Market: Conclusion and References

Machine Learning: Your Ultimate Feature Selection Guide Part 2 - Select the Real Best

240 Stories To Learn About Startups Of The Year

Welcome to the complex yet fascinating world of feature optimization – a critical element in predictive analytics that could be the difference between your model's success or failure. Why does feature optimization matter, especially when it comes to predicting prices? The right features serve as the engine of a predictive model and directly affect its speed, accuracy, and overall efficiency. Nowadays we often have access to more data than we can deal with, so knowing which data to focus on becomes a critical point. My name is Pavel Ianonis, and I am a seasoned software engineer with over 10 years of experience in fintech, banking, and retail, developing complex products for data analytics, sales planning, and risk assessment. In this short essay, I will expose the science and strategy behind effective feature optimization for price prediction. We will examine proven methodologies to select impactful variables, identify common mistakes to avoid, and discuss innovative approaches that are shaping the field. The Basics of Price Prediction Historical Data and Its Significance Let's get something straight: predictive models aren't magicians pulling forecasts out of a hat. Their power stems from historical data—data that has been collected, analyzed, and mined for patterns. When it comes to price prediction, historical data can include past sales figures, seasonal trends, and market conditions. This is the raw material from which predictive models are sculpted. Without adequate historical data, your model's predictions are essentially shots in the dark. And I don’t mean just the volume of data – its quality and relevance are the primary points to consider. For instance, when creating a pricing model for an e-commerce platform, you take into account variables like click-through rates, average time spent on product pages, and even inventory levels. These features make the data robust, offering an all-around view of what drives prices. Challenges in Achieving Accurate Predictions Accurate prediction is the Holy Grail in this quest, but it's easier said than done. Let's review the big three challenges: Data quality issues A model is only as good as its data. Missing values, outliers, or simply irrelevant features can misguide the model, leading to inaccurate predictions. Before you dive into feature optimization, you have to go through the data cleaning process. Overfitting and underfitting is when your model becomes a perfectionist, fitting the training data so closely that it fails to generalize for new data. , on the other hand, is when the model is too lax, missing the intricacies in the data. Both are roadblocks to achieving a well-balanced model. Overfitting Underfitting The curse of dimensionality This is when your model has to consider a high number of features, making the computational cost steep and the model less interpretable. Hence, the need for feature selection and optimization, topics we'll get deeper into as we progress. The Art of Feature Selection Feature selection is the process of choosing a subset of variables, or "features," from a larger set to use in constructing a predictive model. The goal is to find and retain only the features that contribute meaningfully to the model's performance, eliminating redundant or irrelevant variables. Feature selection is an essential step for two main reasons: By eliminating irrelevant or redundant features, you decrease the number of variables the model has to consider. This simplification makes your model computationally less expensive to train and deploy. Reducing Model Complexity: A leaner model with fewer, more relevant features is generally easier to understand and interpret. This is particularly beneficial for stakeholders who may lack technical expertise, or for diagnosing issues with the model. Enhancing Model Interpretability: Here are some commonly used methods of future selection: like assess the relevance of individual features based on statistical properties. Essentially, you quantitatively measure the relationship between each feature and the target variable to filter out features that show minimal or no correlation with what you're trying to predict. Filter Methods or Chi-Square tests correlation coefficients operate in a more iterative and computationally intensive manner and incorporate the model's performance as a key criterion. Methods like allow you to start with a set of features and experiment with different combinations of features, measuring their performance and iteratively updating the feature set. Wrapper Methods and forward selection backward elimination offer a middle ground between filter and wrapper methods, combining the model creation and feature selection processes into a single step. Algorithms like and have feature selection built into their training algorithms, determining the importance of each variable while the model is being constructed. On the one hand, this reduces the number of additional steps, thereby improving computational efficiency; on the other hand, embedded methods produce a more harmonized selection of variables, tuned specifically for the chosen algorithm Embedded Methods Lasso regression Decision Trees Feature Engineering: The Creative Process The Role of Feature Engineering in Optimization While lays the groundwork by identifying the right variables, goes a step further to amplify their impact. This involves two primary actions: feature selection feature engineering Sometimes, the existing variables may not capture all the elements influencing price prediction. In such cases, you generate new features like, for example, weekend sales or average customer ratings. These new variables can provide additional facets to the existing data, augmenting its predictive power. Creating New Features: Aside from simply adding new variables, feature engineering also means enhancing the ones you already have. For example, transforming raw sales figures into a seasonal index could offer a more insightful view of pricing trends. Transforming Existing Features: Feature Engineering Techniques Now that we understand the importance of feature engineering, let's equip you with some key techniques to implement in your predictive model: Feature Scaling and Normalization Think of this as leveling the playing field. Feature scaling harmonizes variables that operate on different scales or units to a common scale. This ensures that each variable contributes equitably to the model's predictions. Handling Missing Data Models are often hamstrung by gaps in the data. To rectify this, you can employ methods like or even utilize smaller predictive models to estimate what's missing and improve your model's integrity. mean imputation One-Hot Encoding and Categorical Variables When dealing with categorical variables like , numerical transformations are needed for the model to make sense of them. , a popular method for this, essentially assigns a binary feature for each unique category in a variable. By doing this, you expand the model's ability to understand and work with such variables. or color brand One-hot encoding Feature engineering is essentially a refinement process. It makes the selected features even more informative and effective, allowing for a model that can produce richer, more nuanced price predictions. Evaluating Model Performance Choosing Appropriate Evaluation Metrics The metrics you choose for evaluation can make or break the credibility of your model. For price prediction models, the objective is clear – precision is king. Here are some trusted, time-tested metrics: Mean Absolute Error The intuitive first step in model evaluation, MAE calculates the average of the absolute differences between the predicted and actual values. By keeping the errors in their original units, MAE offers a transparent lens through which to view a model's performance. It's simple to understand and good for an initial evaluation. Mean Squared Error MSE squares the individual error terms, essentially magnifying the impact of larger errors in your model's predictions. By squaring the errors before averaging them, MSE offers a harsher, more sensitive metric that is instrumental when large errors are undesirable, such as financial forecasting or supply chain optimization. Root Mean Squared Error RMSE is closely related to MSE and is used to evaluate the average squared differences between predicted and observed values. The main difference is the final step: MSE gives you the average of the squared errors, while RMSE takes the square root of this average. RMSE intensifies the effect of MSE due to the square root transformation, and it has an edge over MSE in terms of interpretability: as its units are the same as those of the predicted variable, it makes it easier to relate to the real-world context. Cross-Validation Techniques for Robustness It's not enough to evaluate your model once and call it a day. The real world isn’t static, and your model shouldn’t be either. Here’s how to make sure your model stands the test of time: k-Fold Cross-Validation This technique divides your data into 'k' subsets. The model is trained on 'k-1' of these subsets and tested on the remaining one, cycling through all subsets for a comprehensive evaluation. Leave-One-Out Cross-Validation This technique takes robustness to the extreme by using each individual data point as a test set, while the remaining points form the training set. This process is repeated until every single data point has served as the test set exactly once. While computationally expensive, LOO provides a highly granular view of your model's performance, making it particularly valuable when you can't afford to make any assumptions or when your dataset is small. Monitoring model performance over time Model Drift Detection Markets evolve, consumer preferences shift, and features that were once significant may lose their potency. By regularly tracking metrics like MAE and RMSE, you can detect drifts in your model's performance and revisit feature optimization as needed. Revisiting Feature Optimization It is crucial to periodically re-examine the features driving your model. This involves revisiting your feature selection and engineering strategies to account for new patterns or shifts in the data landscape. A proactive approach to feature optimization ensures that your model remains attuned to the evolving nuances of the market. Conclusion One article is not enough to map the landscape of feature optimization in its entirety. Advanced techniques such as feature extraction and deep learning approaches can further refine price-prediction models. Moreover, the toolkit for feature optimization is ever-expanding, warranting a deep dive of their own. And let's not forget the ethical dimensions that come with selecting features, a subject ripe for extensive exploration. With such a rich subject as price prediction, there's always more to delve into. So, as we close this chapter, remember that feature optimization is not a static endeavor but a dynamic, evolving field. Your models will always be works in progress, requiring you to keep pace with emerging methods and technologies. Innovation and curiosity will be your greatest allies in this pursuit. Now, are you poised to turn insight into action? Remember, the future rewards the ready!

Feature Optimization for Price Prediction

148 Stories To Learn About Team Management

Business English: A Practice Book by Rose Buhlig is part of the HackerNoon Books Series. You can jump to any chapter in this book . SPELLING RULES here SPELLING RULES Exercise 29—Plurals of Nouns ( ) a dress, dresses ( ) b chair, chairs splash, splashes wave, waves business, businesses book, books church, churches pencil, pencils fox, foxes paper, papers The usual way of forming the plural of English nouns is illustrated by the words in column ( ) above. What is it? b If you add to the singular form , could you distinguish the pronunciation of the plural from the pronunciation of the singular? Does this suggest a reason for adding to form the plural? s dress es How many syllables must you use to pronounce the plural of fox? Does this suggest another reason for adding to form the plural? es Every word that ends in a sibilant or hissing sound ( , , , , , ) forms its plural like . Give several illustrations. ch s sh ss x z fox Rule 1.—Nouns regularly form the plural by adding , but those ending in a sibilant must add .\Exercise 30 s es ( ) a lady, ladies ( ) b valley, valleys ally, allies alley, alleys soliloquy, soliloquies journey, journeys Name five words belonging to group ( ) above. Does a vowel or a consonant precede the in each case? a y Name other words belonging to the group ( ) above. Does a vowel or a consonant precede the in each case? b y **Rule 2.—Nouns ending in preceded by a consonant (and nouns ending in ) form the plural by changing to and adding .\Exercise 31—Words ending in o y quy y i es ( ) a potato, potatoes hero, heroes mulatto, mulattoes tomato, tomatoes buffalo, buffaloes cargo, cargoes negro, negroes echo, echoes motto, mottoes ( ) b solo, solos piano, pianos memento, mementos halo, halos lasso, lassos canto, cantos zero, zeros quarto, quartos soprano, sopranos stilletto, stillettos The older English words ending in form the plural by adding , as in potatoes; those more recently taken into the language form the plural by adding , as in quartos. o es s Exercise 32—Nouns in f and fe leaf, leaves calf, calves wife, wives loaf, loaves sheaf, sheaves shelf, shelves half, halves wolf, wolves elf, elves life, lives beef, beeves wharf, wharves (or wharfs) self, selves knife, knives ith the exception of the words given above, nouns ending in an sound form the plural in the regular way; as, f hoof, hoofs scarf, scarfs beliefs, beliefs chief, chiefs reef, reefs grief, griefs Exercise 33—Irregular Plurals Some nouns form their plural by a change of vowel; as, man men foot feet woman women tooth teeth goose geese mouse mice A few words retain the old time plural as, en; brother    brethren child children ox oxen A few words are the same in both singular and plural; as, sheep, trout, deer Some nouns have two plurals which differ in meaning; as, Singular Plural brother brothers brethren penny pennies pence pea peas pease die dies dice Consult a dictionary for the difference in meaning between the two plurals of each word. **Exercise 34—Compound Nouns Singular Plural brother-in-law brothers-in-law father-in-law fathers-in-law court-martial courts-martial commander-in-chief commanders-in-chief man-of-war men-of-war major general major generals goose quill goose quills bill of fare bills of fare spoonful spoonfuls cupful cupfuls Rule 3.—Compound nouns usually add the sign of the plural to the fundamental part of the word. Note.—In the thought is of one spoon many times full. spoonfuls Plural of Letters and Figures Rule 4.—Letters and figures form the plural by adding the apostrophe (') and as, s; a a's 3 3's w w's 5 5's The same rule applies to the plural of words which ordinarily have no plural; as, Don't use so many and . and's if's Exercise 35—Foreign Plurals Some nouns derived from foreign languages retain their original plural. The following are in common use. Consult a dictionary for their pronunciation and definition. Singular Plural Singular Plural crisis crises stratum strata thesis theses radius radii hypothesis hypotheses parenthesis parentheses focus foci synopsis synopses datum data basis bases alumnus alumni automaton automata alumna alumnae analysis analyses oasis oases nucleus nuclei axis axes phenomenon phenomena genus genera Some words admit of two plurals, one the foreign plural, and one the regular English plural; as, Singular Plural beau beaux beaus formula formulae formulas vertex vertices vertexes index indices indexes cherub cherubim cherubs seraph seraphim seraphs bandit banditti bandits Consult a dictionary to see whether there is any difference of meaning between the two plurals of these words. **Exercise 36—The Formation of Participles , , Rap rapping rapped , , Reap reaping reaped is a monosyllable ending in a single consonant preceded by a single vowel. The final consonant in such words is doubled before a suffix beginning with a vowel is added. Rap In the final consonant is not doubled because it is preceded by two vowels. reap Make the participles of the following verbs: chat lap suit step cheat leap sit steep rot train sop trot root trim soap treat , , Trap trapping trapped , , Track tracking tracked Why is the final consonant in doubled before or is added? trap ing ed The final consonant in is not doubled because ends with two consonants. track track , Pin pinning , Pine pining drops the silent because the tendency in English is to drop endings that are not needed for pronunciation before adding a suffix beginning with a vowel. Pine e Form the participles of the following verbs: knot rob flop note robe elope deal swim quit ( is not here a vowel) u clap strike crawl ( is here a vowel) w stop oil wax ( equals ) x cks peal rush bow ( is here a vowel) w Exercise 37 Exercise 36 applies also to words of more than one syllable accented on the last syllable, if they retain the accent on the same syllable after the suffix is added. Thus we have Rule 5.—Monosyllables or words accented on the last syllable, ending in a single consonant preceded by a single vowel, double the final consonant before adding a suffix beginning with a vowel. Form participles from the following words that are accented on the last syllable: prefer intervene escape expel refer reveal acquire contain occur repeal secure forbid permit pursue conceal incur interfere erase arrange forget retain control acquit repel Form participles from the following words not accented on the last syllable: benefit travel marvel shelter revel answer exhibit render quarrel profit shovel limit Words in which the accent changes do not double the final consonant before adding a suffix beginning with a vowel; as, confer conference infer inference refer reference prefer preferable Explain why the final consonant is doubled in each of the following words: not neglect neglecting lean leaning prefer preference select selecting creep creeping receipt receipting wonder wondering answer answering Exercise 38 Rule 6.—In forming the present participle of verbs ending in , retain the before adding as, y y ing; study studying obey obeying carry carrying convey conveying pity pitying In forming the perfect participle, if in the present tense the is preceded by a consonant, the is changed to and added; if the is preceded by a vowel, the is retained; as, y y i ed y y study studied carry carried pity pitied but obey obeyed convey conveyed Compare with . Rule 2 Exercise 39 Rule 7.—In words containing a long sound spelled either or , follows follows one of the other consonants; as, e ie ei ei c; ie ei ie deceive relieve siege perceive believe yield receive belief grief conceive chief field conceit priest piece receipt niece wield reprieve lien —Either, neither, weird, seize, leisure. Exceptions. The following couplet may help in remembering when to write and when to write ie ei: When the letter you spy, Put the before the . c e i Exercise 40—The Pronunciation of and c g The letter is pronounced sometimes like and sometimes like . c s k What sound does have before Illustrate. c a? Before Illustrate. e? Before Illustrate. i? Before Illustrate. o? Before Illustrate. u? Before Illustrate. y? If is pronounced like , it is called hard and is marked . c k [\c] If is pronounced like , it is called soft and is marked . The mark used to indicate the soft is called the . c s ç c cedilla Make a statement telling when is hard and when it is soft. c What sound does have before each of the vowels, as in , , , , g game gone gymnasium Gunther gentle? Rule 8.— and usually are soft before , , and .\Exercise 41 C g e i y Words ending in silent , according to Rule 5, drop the before a suffix beginning with a vowel. Exceptions occur when the is needed to preserve the soft sound of and . Tell why is dropped in and retained in . e e e c g e encouraging courageous In words containing , as in and , the gives the the soft sound, and there is no need to retain the before adding a suffix, as in . dg judge lodge d g e judgment Rule 9.—Words ending in silent usually drop the before adding a suffix beginning with a vowel, unless the is needed to preserve the pronunciation; as after soft and , when the suffix begins with or . e e e c g a o Tell why the is retained before the suffix in the following: e noticeable damageable pronounceable outrageous courageous peaceable serviceable manageable Tell why the is dropped before adding the suffix in the following: e managing curable erasure besieging admirable realization receiving obliging precedence perseverance The fact that has two different sounds causes a slight peculiarity in words ending in . Final has the sound of . When words end in , the letter is usually added before a suffix beginning with either , , or , to show that is not pronounced like as, c c c k c k e i y c s; frolic       frolicked       frolicking If the is not added, the changes its pronunciation; as, k c public       publicity Exercise 42 It follows by inference from Rule 9 that words ending in silent retain the before a suffix beginning with a consonant; as, e e move movement disgrace disgraceful defense defenseless fate fateful arrange arrangement fierce fiercely noise noiseless manage management severe severely rude rudeness —Truly, duly, wisdom, awful, wholly. Exceptions. Bring to class a list of twenty words that retain the final before a suffix beginning with a consonant. e Exercise 43 What spelling rule does each of the following words illustrate? advantageous gigantic boxes admittance mimicking piece libraries occurrence arrangement receipt keys acquittal Exercise 44—Abbreviations Write abbreviations for the months of the year. Are there any that should not be abbreviated? The abbreviations for the states and territories are: Alabama, Ala. Maryland, Md. Arizona, Ariz. Massachusetts, Mass. Arkansas, Ark. Michigan, Mich. California, Cal. Minnesota, Minn. Colorado, Colo. Mississippi, Miss. Connecticut, Conn. Missouri, Mo. Delaware, Del. Montana, Mont. District of Columbia, D.C. Nebraska, Nebr. Florida, Fla. Nevada, Nev. Georgia, Ga. New Hampshire, N.H. Idaho, Idaho New Mexico, N. Mex. Illinois, Ill. New York, N.Y. Indiana, Ind. New Jersey, N.J. Iowa, Ia. North Carolina, N.C. Kansas, Kans. North Dakota, N. Dak. Kentucky, Ky. Ohio, O. Louisiana, La. Oklahoma, Okla. Maine, Me. Oregon, Ore. Pennsylvania, Pa. Utah, Utah Philippine Islands, P.I. Vermont, Vt. Porto Rico, P.R. Virginia, Va. South Carolina, S.C. Washington, Wash. South Dakota, S.D. Wisconsin, Wis. Tennessee, Tenn. West Virginia, W. Va. Texas, Tex. Wyoming, Wyo. Note.—It is much better to write the full name rather than the abbreviation whenever the former would make the address clearer, especially as regards similar abbreviations, such as Cal. and Colo. Exercise 45—Abbreviations of Commercial Terms A 1, first class doz., dozen @, at E. & O.E., errors and omissions excepted acct., account ea., each adv., advertisement e.g., for example agt., agent etc., and so forth a.m., forenoon exch., exchange amt., amount ft., foot app., appendix f.o.b., free on board atty., attorney gal., gallon av., average i.e., that is avoir., avoirdupois imp., imported bal., balance in., inches bbl., barrel inst., this month (instant) B/L, bill of lading Jr., junior bldg., building kg., keg B/S, bill of sale lb., pound bu., bushel ltd., limited C.B., cash book mdse., merchandise C., hundred mem., memorandum coll., collection, collector mo., month Co., company M.S. (MSS)., manuscript C.O.D., cash on delivery mtg., mortgage cr., creditor N.B., take notice cwt., hundredweight no., number D., five hundred O.K., all right dept., department per, by disc., discount p.m., afternoon do., ditto %, per cent dr., debtor, debit St., street pkg., package str., steamer pp., pages ult., last month pr., pair U.S.M., United States Mail pc., piece viz., namely pk., peck vol., volume prox., next month W/B, way bill pt., pint wt., weight Sr., senior About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Rose Buhlig (2011). Business English: A Practice Book. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/38046/pg38046-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

SPELLING RULES

Christian Science by Mark Twain, is part of the HackerNoon Books Series. You can jump to any chapter in this book . THE CREED here THE CREED It is copyrighted. I do not know why, but I suppose it is to keep adventurers from some day claiming that they invented it, and not Mrs. Eddy and that “strange Providence” that has suggested so many clever things to her. No Change. It is forbidden to change the Creed. That is important, at any rate. COPYRIGHT I can understand why Mrs. Eddy copyrighted the early editions and revisions of Science and Health, and why she had a mania for copyrighting every scrap of every sort that came from her pen in those jejune days when to be in print probably seemed a wonderful distinction to her in her provincial obscurity, but why she should continue this delirium in these days of her godship and her far-spread fame, I cannot explain to myself. And particularly as regards Science and Health. She knows, now, that that Annex is going to live for many centuries; and so, what good is a fleeting forty-two-year copyright going to do it? Now a perpetual copyright would be quite another matter. I would like to give her a hint. Let her strike for a perpetual copyright on that book. There is precedent for it. There is one book in the world which bears the charmed life of perpetual copyright (a fact not known to twenty people in the world). By a hardy perversion of privilege on the part of the lawmaking power the Bible has perpetual copyright in Great Britain. There is no justification for it in fairness, and no explanation of it except that the Church is strong enough there to have its way, right or wrong. The recent Revised Version enjoys perpetual copyright, too—a stronger precedent, even, than the other one. Now, then, what is the Annex but a Revised Version itself? Which of course it is—Lord's Prayer and all. With that pair of formidable British precedents to proceed upon, what Congress of ours— But how short-sighted I am. Mrs. Eddy has thought of it long ago. She thinks of everything. She knows she has only to keep her copyright of 1902 alive through its first stage of twenty-eight years, and perpetuity is assured. A Christian Science Congress will reign in the Capitol then. She probably attaches small value to the first edition (1875). Although it was a Revelation from on high, it was slim, lank, incomplete, padded with bales of refuse rags, and puffs from lassoed celebrities to fill it out, an uncreditable book, a book easily sparable, a book not to be mentioned in the same year with the sleek, fat, concise, compact, compressed, and competent Annex of to-day, in its dainty flexible covers, gilt—edges, rounded corners, twin screw, spiral twist, compensation balance, Testament-counterfeit, and all that; a book just born to curl up on the hymn-book-shelf in church and look just too sweet and holy for anything. Yes, I see now what she was copyrighting that child for. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Mark Twain (2004). Christian Science. Urbana, Illinois: Project Gutenberg. Retrieved October 2022 https://www.gutenberg.org/cache/epub/3187/pg3187-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

IT IS FORBIDDEN TO CHANGE THE CREED

Let's learn about via these 49 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Environment /Learn Repo 1. EOS: Blockchain Without Hurting The Environment EOSIO is a very quick blockchain that is capable of three thousand nine hundred ninety-six transactions per second. 2. Electric Vehicles: to Buy or Not to Buy Electric Vehicles are Popular, but Should YOU Get One? 3. "Is This How Our Story is Due to End?" #COP26 Are we investing in the right things or failing to see the bigger picture in pursuit of short-term goals? 4. Analysis of Blockchain Technology Adoption for ESG Initiatives in Business The importance of ESG in these times cannot be overstated, as there is a need to raise awareness of global social and economic inequality. 5. Top 6 Integrated Development Environments (IDE) for Python Programmers An integrated development environment (IDE) allows you to run code written in different programming languages. It is important to have an IDE for running Python 6. The Downside of The Work From Home Movement 2021 has brought a lot of change into the lives of people from all walks of life. Most of us have been bound to our homes until it’s deemed safe to return to our usual way of life. One of the areas that have been affected by this change is how and where we work. 7. How the UN’s Sustainable Development Global Goals Align With the ÀLA.HAUSSE Mission The UN's Sustainable Development Goals initiative was a step towards global reconciliation, and we at ÀLA.HAUSSE share those foundational goals. 8. Is Bitcoin Really That Bad For The Environment? Questions about the environmental friendliness of the cryptocurrency business appeared 2 years ago. Is the current situation really that bad as many say? 9. How This Open Litter Database Will Save the Planet The Litterati app has been around for a couple years on iOS + Android. Over that time, well over 100k people have downloaded the app and been a part of a global team that is 'crowdsource cleaning the Earth'. Over that time, people only had access to the data that they themselves generated. 10. The Best Thing You Will See Today: Animals Interrupting Photographers Joaquim Campa creates a thread of animals interrupting photographers. 11. The Microfiber Problem: How Washing our Clothes is Polluting the Ocean When we think about ocean pollution, we often imagine shores filled with plastic water bottles, bags, fishing nets, and various other forms of plastic waste. 12. Carina Ayden On Being an Environmentalist Founder Building Resilient Products Carina Ayden, the founder of Unimaginable foods, shares her perspective on the sustainable lifestyle and becoming an environmentalist. 13. A-Z Of DevOps: Managing Multiple Environments With The Help Of These Tools In most DevOps settings you’ll find that there are multiple environments in the pipeline. You might have conditions that change the environment based on which branch was merged or when a branch is tagged for release. There are a number of reasons you want to have more than just a production environment, the biggest reason being testing. 14. The year 2020- From the Eyes of Environment and Climate Change Norvergence LLC team has listed down both the positive and negative things that happened in 2020 w.r.t environment and climate change. 15. Polyester: A Sustainable Option? Polyester is the most widely used modern fabric in the world, yet raises a question between the boundaries of sustainability and convenience. 16. How the E-Waste Industry is Ramping Up the Circular Economy E-waste is no longer an isolated problem. When other connected industries and groups come forward, read how the circular economy has evolved around e-waste. 17. How Biodiversity is Harmfully Affected by the Overlapping Effects of Fast Fashion Biodiversity is crucial for the sustainability and fruition of Earth, however, the effects of fast fashion are acting as a major detriment. 18. Hacking Clean Water for Your Health A look at how and why you can improve your quality of life. 19. Paperless Office: What Steps You Can Take Today? What can we do to save money and help the environment at the same time? Becoming a paperless office is one of the options. 20. Black Ink Rivers: A Ghastly Consequence of the Dye Industry The fashion industry's pollution is a major detriment to the environment, turning once beautiful and flowing exotic rivers toxic, and black as ink. 21. 3 Million Face Masks Per Second: Where Do They End Up? As per recent studies, humankind is going through 129 billion face masks a month, which works out to 3,000,000 a second 22. Micro Electric Vehicles and their Massive Advantages Over Standard EVs Companies around the world are developing smaller, more efficient electric cars that provide solutions to growing concerns within the standard EV market. 23. The Benefits of Renewable Energy: Why Clean Energy Is Good for Our Health, Environment, and Economy Renewable energy is becoming more and more popular, and for good reason. Not only does renewable energy have a minimal environmental impact, it also provides a number of benefits to our health, environment, and economy. In this page, we will explore the many benefits of renewable energy technologies like wind turbines and solar panels. So read on to learn why clean energy is good for all of us! 24. How Blockchain Technology Improves Environmental Sustainability We are seeing blockchain reach a wider breadth of applications than it seems at first glance. That includes its role in increasing sustainability. 25. What Retailers Can Do with Excessive Inventory Pile Up from the Pandemic Are retailers focusing on sustainable options to move excessive inventory? 26. Apple Co-Founder Steve Wozniak is Developing Energy Efficiency NFTs The Genesis NFTs are a resource for making the necessary energy efficiency upgrades to businesses as part of the Energy Efficiency NFT initiative. 27. 2100: A Global Warming Odyssey The carbon legacy of our fossil fuel based ways of life is granting an environmental crisis to billions of individuals in the future. 28. From “Feature branches” to “Feature environments” with Terraform “The advantage of feature branching is that each developer can work on their own feature and be isolated from changes going on elsewhere.” (FeatureBranch) 29. Energy Ideas For A New Decade Ever since I got my first solar-powered LCD watch in the late 1970s, I’ve been a fan of solar power, but what has amazed me in the 40’ish years since then is the rather pathetic advances we’ve made in solar panel efficiency. In 1954 Bell Labs achieved an energy conversion efficiency rate of 6%, then 25 years later we about doubled that, but in the 40 years since, we’ve only really seen another 50% increase to about 19% although more expensive panels can now get you 25%-30% with the super expensive ones that go into space getting nearly 50%. Another problem with solar panels and “green energy” in general is that the ecological impact of the manufacture and disposal of the components has a devastating environmental impact. 30. IKEA Partners with Ombori to Eliminate Retail Paper Waste The recent collab between Ombori and IKEA implements tech to eliminate paper waste, elevate the customer experience, and streamline operations for IKEA 31. Sustainable Clothes Made From Recycled Plastic Fashion brands across the globe are striving to reduce the industry’s negative environmental impact by creating new clothing using only plastic materials. 32. Why We Awarded a  £10k R&D Grant to Consciously Aware for Development of a New Recycling Technology Environmentalist start-up Consciously Aware won a  £10,000 R&D grant from leading UX design agency Creative Navy. Together, the partner companies plan to create a consumer facing mobile app which would vastly improve the efficiency of the recycling process. 33. Regression Analysis on Life Expectancy Models used: Linear, Ridge, LASSO, Polynomial Regression
Python codes are available on my GitHub 34. What if a 3D Printed Steak was on the Menu? In this slogging thread, our random channel discussed the news of a new 3D printed steak that is good for the environment as well as it is delicious. 35. How To Setup a Python Virtual Environment on Windows 10 Creating a Python Virtual environment will allow you to work on an isolated copy of Python for specific projects without interfering or impacting the working of other ongoing projects. 36. About Carbon Capture, Usage, and Storage's Impact on The Climate Crisis With the climate crisis continuously getting worse, businesses and governments need to find solutions to reduce the amount of carbon going into the atmosphere. To beat the crisis, the world can't simply rely on renewable energy, governments will need to include carbon capture, usage and storage (CCUS) into the mix if they want to hit their climate targets. 37. 8 Unsolved Carbon Markets' Problems That Engineers Need To Get Behind And Solve I think about this conversation I had with Paul a lot. It kicked off what has now been over three years of imaginative, grueling, wonderful, painful, mind-blowing, and confounding work-- all of which took place alongside the minds of amazing co-founders, teammates, and advisors. 38. How Programming, AI, and Big Data is Giving Google A Chance to Save the World Big business and saving the planet often do not go hand in hand, however in some cases they do. Take a look at how Google plans on saving the future with tech. 39. Building a Global Environmental Datastack for Climate Action Blue Sky Analytics is a big data and AI start-up that uses geospatial data to monitor environmental parameters. Our goal is to become the Bloomberg of Environmental data for environmental monitoring, ESG (environment, social, and governance) due-diligence and climate risk assessment. 40. The Way to Building Integrated Data Platforms with IoT Edge Devices and AI Algorithms "At the Edge" There is growing demand in various industries, such as energy, agriculture, transportation, and also in scientific research fields, particularly environmental studies, for not only more intelligent and efficient data collection, but also for distributed and localised manipulation of the data being collected, in real-time. 41. Not Even the EPA Can Say What's Polluting the Air In 2016, Boeing’s Portland plant told the EPA it had released 1,954 pounds of hexavalent chromium into the air - a 1,000% increase from the year before. 42. Writing Proper Code Is Vital: Here's Why November of last year, OVO Energy sponsored a study finding that “If every Brit sent one less thank you email a day, we would save 16,433 tonnes of carbon a year — the same as 81,152 flights [from the UK] to Madrid.” 43. What Does It Mean When Google Says It’s Carbon Neutral? Consider some estimations by Google: 44. Are Fish Robots Going to Save the Oceans? In this slogging thread, our community discusses the new robo-fish that comes to help us save the oceans from microplastic waste. 45. Can Remote Work Lead to a Greener Environment? “Is remote work more environmentally friendly? 46. Hydrogen Fuel Cell Vehicles are Not a Solution to Climate Change How is it that fuel cell vehicles using hydrogen as a fuel are touted as climate-friendly vehicles? California even subsidizes the purchase of a hydrogen fuel cell vehicle. The advocates never reveal the source of the hydrogen would most likely be coming as a byproduct of fracking in pursuit of natural gas. 47. You Need to Know What's Going on In North Birmingham With the doors damaged, the toxic chemicals they were supposed to contain within the ovens leaked out at an accelerated rate. 48. WTF is Carbon Neutrality and How Companies Can Achieve It Despite so much power consumption, Google remains Carbon Neutral with their best in the class data centres and AI and ML. Google says their data centres consume the minimum power possible. 49. How AI Can Spot Wildfires Faster Than Humans Here's how artificial intelligence can be used to reduce fire detection time from an average of 40 minutes to less than five minutes! Thank you for checking out the 49 most read stories about Environment on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

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I explained the concepts and theory behind Data Residency in a . It's time to get our hands dirty and implement it in a simple demo. previous post The sample architecture In the last section of the previous post, I proposed a sample architecture where location-based routing happened at two different stages: The API Gateway checks for an existing header. Depending on its value, it forwards the request to the computed upstream; If no value is found or no value matches, it forwards it to a default upstream. X-Country The application uses to route again depending on the data. If the value computed by the API Gateway is correct, the flow stays "in its lane"; if not, it's routed to the correct database, but with a performance penalty as it's outside its lane. Apache Shardingsphre I simplified some aspects: The theory uses two API Gateway instances. For the demo, I used only one. Remember that the location isn't set client-side on the first request. It should be returned along the first response, stored, and reused by the client on subsequent calls. I didn't bother with implementing the client. I like my demos to be self-contained, so I didn't use any Cloud Provider. Here's the final component diagram: The data model is simple: We insert location-specific data on each database: INSERT INTO europe.owner VALUES ('dujardin', 'fr', 'Jean Dujardin');
INSERT INTO europe.thingy VALUES (1, 'Croissant', 'dujardin');

INSERT INTO usa.owner VALUES ('wayne', 'us', 'John Wayne');
INSERT INTO usa.thingy VALUES (2, 'Lasso', 'wayne'); Finally, we develop a straightforward RESTful API to fetch thingies: GET /thingies/ GET /thingies/{id} Now that we have set the stage, let's see how we can implement routing at the two levels. Routing on Apache ShardingSphere Apache ShardingSphere offers two approaches: as a library inside the application, ShardingSphere-JDBC, or as a full-fledged deployable component, ShardingSphere-Proxy. You can also combine both. I chose the former because it's the easiest to set up. For a comparison between them, please check . this table The first step is to add the dependency to the POM: <dependency>
	<groupId>org.apache.shardingsphere</groupId>
	<artifactId>shardingsphere-jdbc-core</artifactId>
	<version>5.3.2</version>
</dependency> ShardingSphere-JDBC acts as an indirect layer between the application and the data sources. We must configure the framework to use it. For Spring Boot, it looks like the following: spring:
  datasource:
    driver-class-name: org.apache.shardingsphere.driver.ShardingSphereDriver     #1
    url: jdbc:shardingsphere:absolutepath:/etc/sharding.yml                      #2-3 JDBC-compatible ShardingSphere driver Configuration file Opposite to what the documentation tells, the full prefix is . I've opened a to fix the documentation. jdbc:shardingsphere:absolutepath PR The next step is to configure ShardingSphere itself with the data sources: dataSources:                                                                  #1
  europe:
    dataSourceClassName: com.zaxxer.hikari.HikariDataSource
    driverClassName: org.postgresql.Driver
    jdbcUrl: "jdbc:postgresql://dbeurope:5432/postgres?currentSchema=europe"
    username: postgres
    password: root
  usa:
    dataSourceClassName: com.zaxxer.hikari.HikariDataSource
    driverClassName: org.postgresql.Driver
    jdbcUrl: "jdbc:postgresql://dbusa:5432/postgres?currentSchema=usa"
    username: postgres
    password: root
rules:                                                                       #2
  - !SHARDING
    tables:
      owner:                                                                 #3
        actualDataNodes: europe.owner,usa.owner                              #4
        tableStrategy:
          standard:
            shardingColumn: country                                          #3
            shardingAlgorithmName: by_country                                #5
    shardingAlgorithms:
      by_country:
        type: CLASS_BASED                                                    #6
        props:
          strategy: STANDARD
          algorithmClassName: ch.frankel.blog.dataresidency.LocationBasedSharding #7 Define the two data sources, and europe usa Define rules. Many rules are available; we will only use sharding to split data between Europe and USA locations Sharding happens on the column of the table country owner Actual shards Algorithm to use. ShardingSphere offers a couple of algorithms out-of-the-box, which generally try to balance data equally between the sources.
As we want a particular split, we define our own Set the algorithm type Reference the custom algorithm class The final step is to provide the algorithm's code: class LocationBasedSharding : StandardShardingAlgorithm<String> {    //1

  override fun doSharding(targetNames: MutableCollection<String>, shardingValue: PreciseShardingValue<String>) =
    when (shardingValue.value) {                                     //2
      "fr" -> "europe"
      "us" -> "usa"
      else -> throw IllegalArgumentException("No sharding over ${shardingValue.value} defined")
    }
} Inherit from , where is the data type of the sharding column. Here, it's StandardShardingAlgorithm<T> T country Based on the sharding column's value, return the name of the data source to use With all of the above, the application will fetch thingies in the relevant data source based on the owner's country. Routing on Apache APISIX We should route as early as possible to avoid an application instance in Europe fetching US data. In our case, it translates to routing at the API Gateway stage. I'll use APISIX for configuration. standalone mode Let's define the two upstreams: upstreams:
  - id: 1
    nodes:
      "appeurope:8080": 1
  - id: 2
    nodes:
      "appusa:8080": 1 Now, we shall define the routes where the magic happens: routes:
  - uri: /thingies*                          #1
    name: Europe
    upstream_id: 1
    vars: [["http_x-country", "==", "fr"]]   #2
    priority: 3                              #3
  - uri: /thingies*                          #4
    name: USA
    upstream_id: 2
    vars: [["http_x-country", "==", "us"]]
    priority: 2                              #3
  - uri: /thingies*                          #5
    name: default
    upstream_id: 1
    priority: 1                              #3 Define the route to the Europe-located app APISIX matches the HTTP methods, the URI conditions. Here, the condition is that the header has the value and X-Country fr APISIX evaluates matching in priority order, starting with the highest priority. If the request doesn't match, , because the header doesn't have the set value, it evaluates the next route in the priority list. e.g. Define the route to the USA-located app Define a default route The first request carries no header; APISIX forwards it to the default route, where ShardingSphere finds data in the relevant data source. Subsequent requests set the header because the response to the first request carries the information, and the client has stored it. Remember that it's outside the scope of the demo. In most cases, it's set to the correct location; hence, the request will stay "in its lane". If not, the configured routing will still find the data in the appropriate location at the cost of increased latency to fetch data in the other lane. X-Country Observing the flow in practice It's always a good idea to check that the design behaves as expected. We can use OpenTelemetry for this. For more information on how to set up OpenTelemetry in such an architecture, please refer to . End-to-end tracing with OpenTelemetry Note that Apache ShardingSphere supports OpenTelemetry but doesn't provide the binary agent. You need to build it from source. I admit I was too lazy to do it. Let's start with a headerless request: curl localhost:9080/thingies/1 It uses the default route defined in APISIX and returns the correct data, thanks to ShardingSphere. Now, let's set the country to , which is correct. fr curl -H 'X-Country: fr' localhost:9080/thingies/1 APISIX correctly forwards the request to the Europe-located app. Finally, imagine a malicious actor changing the header to get their hands on data that are located in the US. curl -H 'X-Country: us' localhost:9080/thingies/1 APISIX forwards it to the USA-located app according to the header. However, Shardingsphere still fetches data from Europe. Conclusion In the I explained the concepts behind Data Residency. In this post, I implemented it within a simple architecture, thanks to Apache APISIX and Apache ShardingSphere. The demo simplifies reality but should be an excellent foundation for building your production-grade Data Residency architecture. previous post, The complete source code for this post can be found on . GitHub Originally published at A Java Geek The featured image for this article was generated with Kadinsky 2. Prompt: Data warehouse.

How to Manage Data Residency

Our Humble Helpers: Familiar Talks on the Domestic Animals by Jean-Henri Fabre, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . THE HORSE (Continued) CHAPTER XXXVIII. THE HORSE (Continued) “Now let us say a few words about the horse’s coat, the growth of hair that covers its body. This may be of uniform color or of two or more different colors. Coats of uniform color are the white, the black, and the chestnut. The two first do not need any explanation. A horse is chestnut when its coat is of a reddish or yellowish tint. “Among the composite coats, the following are distinguished. The horse is piebald if the coloring is in large splashes, some white, others black or red. It is flee-bitten gray if the coat is a mixture of white, black, and red, over the whole body, legs and all; but if the legs are black while the body presents a combination of the three tints, the horse is roan. Bay horses have a chestnut-colored coat, that is to say reddish or yellowish, with the legs, the mane, and the tail brown or black. The coat is dappled when it is thickly sprinkled with light spots on a darker background of uniform color. Dappled gray is common. It is dun when the color is yellowish with a brown stripe on the back, a peculiarity rather common in the donkey and mule. A number of other terms are used in describing a horse’s coat in detail. Thus the term white-foot is applied to the white marking sometimes found just above the hoof. A white spot in the middle of the forehead is called a blaze if it is round, a star if angular. “The horse’s mode of progress is called its gait, and may be either natural or artificial, depending on whether the animal is untrained or trained. The natural gaits are the walk, the trot, and the gallop. In the walk the legs move in what may be termed a diagonal sequence, as follows: the right fore leg, the left hind leg, the left fore leg, the right hind leg. If the horse is well formed the hind foot steps exactly into the track left by the fore foot on the same side. “In the trot the feet are lifted and put down two by two in diagonal pairs, the right fore foot with the left hind foot, and the left fore foot with the right hind foot. This gait is more rapid than the preceding, but is also harder for the rider as well as for the horse, because of the shock sustained when two feet strike the ground at the same time. “The gallop is of several kinds, the simplest and swiftest consisting of a succession of forward bounds. The two fore feet are lifted at the same time, then the two hind feet, which push the animal with a sudden spring. That is the racer’s gait. “Among the artificial gaits I will mention the amble, in which the legs move in pairs on the same side, the two left at the same time, then the two right, alternately. The horse thus maintains a sort of oscillation, furnishing a gentle and easy motion for [ ]the rider. The amble is, however, rapid, for as there is no support on the side of the two uplifted legs the animal keeps from falling only by the rapidity of its motion. 364 “In galloping a horse covers ten meters a second, or at most fifteen when going at top speed. In trotting it covers from three to four meters, and in walking, from one to two.” “Let me reckon up what that would be in an hour,” Emile interposed. “I will take the highest figures.” With his pencil he wrote some figures on a piece of paper, and then said: “That would make, by the hour, thirteen leagues of four kilometers each for the horse at its fastest gallop; only three leagues for the horse when trotting; and a league and a half when walking.” “I must inform you, my friend,” rejoined his uncle, “that though a horse can keep up a trot for whole hours at a time, it is impossible for it to gallop even one hour without stopping. The speed that would give the enormous distance of thirteen leagues an hour lasts fifteen minutes at the most in racing, after which the animal is exhausted. Note in passing the superiority of the railway engine, the locomotive, in regard to rapidity. This speed of thirteen leagues an hour, which blows a horse in a quarter of an hour’s race, the locomotive keeps up and even exceeds as long as may be desired. No comparison, you see, is possible between the iron steed and the steed of flesh and blood. “Let us turn to the subject of the horse’s strength. [ ]A riding horse carries on an average from 100 to 175 kilograms at a slow gait. If the load is a rider of 80 kilograms, the horse can travel seven hours and cover ten leagues of four kilometers each. But its strength is much better employed if instead of carrying the weight on its back the animal draws it in a vehicle. Then an expenditure of energy represented by the weight of five kilograms is sufficient to move a load of 1000 kilograms if the wheels of the vehicle run on a railway track. For the same load on a smooth, level road an expenditure of energy represented by 33 kilograms is needed; finally, if the road is paved with stone the required energy will be 70 kilograms. On an excellent road, stagecoach horses draw each a load of 800 kilograms and cover six leagues in two hours, after which they are replaced by others. 365 “Let us compare these figures once more with those relating to the steam engine. A passenger locomotive draws with a speed of a dozen leagues an hour a train having a total weight of as much as 150,000 kilograms. A freight locomotive draws at the rate of seven leagues an hour a total weight of 650,000 kilograms. More than 1300 horses would be needed to take the place of the first locomotive, and more than 2000 for the second, if they were used to transport similar loads the same distance at the same rate of speed, using cars running on rails. How many more would be needed with wagons on ordinary roads, where the surface inequalities cause such waste of energy![ ] 366 “The domestication of the horse goes back to the first communities of the East. After the herd they must soon have had, first, the ass to carry the baggage of the nomadic tribe, then the horse, man’s valiant comrade in the chase and in war. What is still to be observed to-day shows us how easily this valuable animal submitted to man’s domination. The grassy plains of Tartary abound in wild horses, and probably the species originated in these Asiatic regions. The pampas of South America feed innumerable herds of them, mingled with the wild cattle that I have told you about. Both descend from domestic animals brought to the New World by Europeans. Each herd follows a leader of tried strength and courage. If danger arises, if there is menace from some ferocious wild beast, such as a wolf, panther, or jaguar, the horses crowd together and press against one another for their common defense. Their haughty look and their kicking are generally sufficient to put the aggressor to flight. But if the enemy charges them, counting on an easy prey, the leader of the herd rears and falls on the beast with all its weight, crushing the assailant with its fore hoofs; then with its powerful jaws seizes the shattered body and throws it to the colts, which finish it and caracole on its body.” “An animal that defends itself like that,” remarked Jules, “must be rather hard to tame into a docile servant.” “No, the difficulty is not, after all, very great. What happens to-day on the pampas when it is desired [ ]to master a wild horse is of a nature to show us how the ancient horse-tamers accomplished the same object. A herd of horses, skilfully turned aside from its feeding ground and surrounded little by little on all sides, is driven without suspecting the ruse into a large enclosure called a corral. There those of finest appearance are selected. Immediately a dexterous hand throws the lasso, the long leather thong weighted with balls of lead, which catches them round the neck and legs and prevents their moving. A halter is quickly put on the captive. A practised horseman wearing sharp spurs mounts the animal, the fettering lasso is removed by helpers, and there stands the animal, free, but trembling after its misadventure.” 367 “Now the horseman had better look out,” said Jules. “Certainly, the first moment is not without danger. The indignant animal rears, kicks, bounds, and tries to roll on the ground to get rid of its burden; but the horseman masters this rage with the bleeding prick of the spur; he keeps his seat as if he were one with his mount. Then the gate of the enclosure is opened, and the horse darts out and gallops away at breakneck speed until utterly winded. This unbridled run suffices to tame the animal, after which the horseman rides it back, unresisting and already obedient to bit and spur, to the corral. Henceforth it can be left with the domesticated horses without fear of its trying to escape. “Horses are classed, according to the rearing and [ ]training they have received, in two chief groups—saddle horses and draft horses. The first serve as mounts for riders, the second draw loads in vehicles. Among saddle horses the most celebrated are the Arabian, remarkable for their mettle, intelligence, docility, fleetness of foot, and ability to endure long abstinence from food and drink. The Arab steed is medium-sized and has a delicate skin, small head, slender frame, a spirited bearing, finely modeled legs, stomach little developed, and small, polished, very hard hoofs. 368 “Draft horses, whose function it is to draw heavy loads in wheeled vehicles at a walking pace, have quite opposite characteristics. They lack lightness and mettle, but patiently exert their strength, which is considerable, as might be inferred from their more massive build and from the great quantity of feed that their maintenance demands. They have a stout body, heavy walk, thick skin, large head, wide chest, broad rump, capacious stomach, strong legs, and hoofs of no delicate proportions. France possesses in the Boulogne breed the most highly prized of draft horses. This vigorous animal, usually dapple-gray, plays the laborious part of shaft-horse. Having its position next to the cart or wagon, it is placed between the two shafts. It is the one to pull the hardest on up-grades, the one that eases with its enormous weight the jolts on street pavements and checks the dangerous momentum of the vehicle on down-grades. Compare these two pictures that I [ ]show you here, and you can easily see in the first the horse made for speed; in the second, the horse intended for hard work.” 369 About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Jean-Henri Fabre (2022). Our Humble Helpers: Familiar Talks on the Domestic Animals. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/67073/pg67073-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html .

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