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  1. Gifting Information
  2. Die Amerikanerin: Thriller by Deon Meyer | NOOK Book (eBook) | Barnes & Noble®
  3. List of films set in Berlin
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A Fantastical Librarian called "Bingo" an "effective blend of horror and fascination It is a sneakily powerful tale. You can hear a free podcast of this story , beautifully read by Kim Lakin-Smith. A Fantastical Librarian highlighted our story for special praise: "I loved S. I found it tragic and quite a deft social commentary. A great contemporary, entirely readable combination of funny, frightening and clever science fiction.

Click here for more information, and details on where to get it. Our flash fiction, "Still" , appeared in the beautiful photography-fiction collection and Saboteur-award runner-up, Still: Short Stories Inspired by Photographs of Vacated Places , put together by photographer Roelof Bakker at Negative Press.

It was released in a limited edition in and is now a prized collector's item. You can still read about it here , though. Reaction to The Apartment. Ona jest niczym magnes. Assolutamente consigliato. Da non perdere! Che lascia un segno. Grey er lige til at tisse i bukserne over. Jeg kan kun anbefale 'Lejligheden'. This one will keep you up all night. Reaction to Under Ground. Reviews: May — Partie d'une liste des "livres complexes que j'adore". Je vous le recommande vivement. Heel goed geschreven en ook nog spannend en verrassend.

It's just good fun, wonderfully tense and in places, perversely funny. I did. Een echte pageturner! A highly recommended read. Underground is a book I would strongly recommend you seek out. It is a very thought provoking read. That ending! Quite excellent. Grey novel being written. Reaction to The New Girl. Questi due tipi sono in gamba. A dark and scary read.

Reaction to The Mall. Non ve ne pentirete. All in all, The Mall isn't Grey is horror on steroids with a PhD in psychology. Thanks to pitch-perfect pacing, The Mall is a lunatic thrill ride.

Gifting Information

Chilling, spooky and original. Stage 2 of Learning to Place happens during inference i. First, pairwise preferences compute using the binary classification model. For each new test book k , we obtain. Books voters from the training data are sorted by sales, dividing the sales axis into intervals.

See Fig.

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We observe that most features we explored are heavy-tail distributed, and so are the one year sales. Therefore, we take the logarithm of our dependent and independent variables, obtaining the model:. The target variable is predicted by local interpolation of the targets associated with the nearest neighbors in the training set.

The features are preprocessed in the same fashion as in Linear Regression. Neural Network The above two baselines do not capture nonlinear relationship between features, therefore we use a simple Multilayer Perceptron with one layer of neurons as another baseline. The features are preprocessed in the same fashion as Linear Regression. We apply an evaluation method for each fold of the test sample. The performance measure are as follows:. We consider the true value of each train instance as a threshold and we binarize any predicted value and target value depending on this threshold.

Having these two binarized lists, we compute the true positive rate TPR and the false positive rate FPR for a given threshold. Since book sales follow a heavy tailed distribution, we calculate the RMSE based on the log values of the predicted and the actual sales. We evaluated Learning to Place and other baseline algorithms on hardcover books published in , aiming to predict the one year sales of each book. In our experiments, we train models and evaluate their performance on leave-out fraction of the 5-fold cross-validation.

If we use Linear Regression see Fig. However, as shown in Fig. Model results of one year sales for fiction and nonfiction books. A Actual vs prediction scatter plots.

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All three baseline methods systematically underpredict at high-end to a certain extent while this underprediction is absent in Learning to Place. B Quantile-Quantile plot. For fiction, on the high-end sales, neural network and Learning to Place are the closest to the ground truth 45 degree line.

Die Amerikanerin: Thriller by Deon Meyer | NOOK Book (eBook) | Barnes & Noble®

For nonfiction, Learning to Place is the closest to the ground truth. KNN and Linear Regression at the high-end systematically have lower predictive power for both fiction and nonfiction. To see this more clearly, we use a Quantile-quantile plot Fig. We find that for fiction, Learning to Place and Neural Network provide the closest output to the ground truth 45 degree line while for nonfiction, Learning to Place offers the closest output to the ground truth.

KNN and Linear Regression , however, fail to predict high values for books at the high-end, leading to a significant deviation from the 45 degree line at high quantiles. We see that the curves for Learning to Place are almost always above the curves for the other methods, indicating that Learning to Place outperforms the other approaches. ROC curve and measurement table for one year sales. ROC curve for A fiction books and B nonfiction books.

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Neural Network achieves comparable ROC curves. Band around the curve represents the standard deviation of the score across 5-fold cross validation. We see that Learning to Place outperforms in every measure. Table in Fig. To identify the relative importance of specific feature groups, we plot the AUC score using each feature group for fiction and nonfiction, shown in Fig. It is remarkable how similar the curves are, suggesting that the driving forces determining book sales are rather universal. We can also see that for both fiction and nonfiction, Imprint is the most important feature group.

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However, fiction relies slightly more on previous sales and visibility than nonfiction, while nonfiction relies slightly more on the imprint prestige. Feature group importance radar plot. A Radar plot of feature group importance for fiction and nonfiction books. Imprint is the most important feature group for both fiction and nonfiction. B Radar plot of feature group importance for fiction sub-genres. Imprint is still the most important feature group.

C Radar plot of feature group importance for nonfiction sub-genres. Similarly, imprint is the most important feature group for all sub-genres. We also apply Learning to Place on selected genres and look at the feature importance difference between different genres. We select the five largest genres under fiction Mystery, Thriller, Fantasy, Historical, Literacy and nonfiction Biography, Business, Cooking, History, Religion respectively and obtain the feature importance for each genre.

We find that across all genres, Imprint is the most important feature group, followed by previous sales and visibility; with all other feature groups having limited importance. We do observe, however, small but insightful differences between genres. For nonfiction genres, we see that for all genres Imprint is the most important feature group.

Since we have features in three main categories: author, book and publisher, we can also look at the importance for each of these categories. To achieve this, we train three models, each including only one feature category. Finally, we use a ternary plot to inspect the source of errors for different books. To help interpret the plot, we color the books based on their actual sales. We observe that for all genres, the top corner has the highest density, meaning that if we rely only on the book feature category, we have the largest prediction error, showing that imprint and author feature are very important sales predictors.