291 research outputs found
Keep Moving Forward
No full textKeep Moving Forward is a story written in the inspiration of author Charles Bukowski, specifically in spirit of Ham On Rye. Similarly to that novel, this story is written in the form of dirty realism, with an intended audience of young adults. This coming of age story follows the youth of Lily Torres, who is simply looking for happiness. Or at least some means of satisfaction. But things don't always work out that way in life, especially not for Lily. Throughout this story, readers follow the everyday struggles Lily is brought up against. Abandoned by her friends, mistreated by her lovers and forgotten about by her family, Lily is forced to learn the lessons of friendship, love and trust. It's through these learning lessons that she finally accepts that she can only keep moving forward
Erik Cinthios bibliografi 1946-2011
Get PDFA bibliograhy of Erik Cinthio, professor in medieval archaeology at Lund University. The bibliography covers the period 1946-2011. In his festschrift, "Medeltiden och arkeologin. Festskrift till Erik Cinthio" (1986) a bibliograhy by the same author covered the period 1946-86
EB-NeRD a large-scale dataset for news recommendation
Get PDFPersonalized content recommendations have been pivotal to the content experience in digital media from video streaming to social networks. However, several domain specific challenges have held back adoption of recommender systems in news publishing. To address these challenges, we introduce the Ekstra Bladet News Recommendation Dataset (EB-NeRD). The dataset encompasses data from over a million unique users and more than 37 million impression logs from Ekstra Bladet. It also includes a collection of over 125, 000 Danish news articles, complete with titles, abstracts, bodies, and metadata, such as categories. EB-NeRD served as the benchmark dataset for the RecSys ’24 Challenge, where it was demonstrated how the dataset can be used to address both technical and normative challenges in designing effective and responsible recommender systems for news publishing. The dataset is available at: https://recsys.eb.dk
RecSys Challenge 2024: Balancing Accuracy and Editorial Values in News Recommendations
No full textThe RecSys Challenge 2024 aims to advance news recommendation by addressing both the technical and normative challenges inherent in designing effective and responsible recommender systems for news publishing. This paper describes the challenge, including its objectives, problem setting, and the dataset provided by the Danish news publishers Ekstra Bladet and JP/Politikens Media Group (“Ek- stra Bladet”). The challenge explores the unique aspects of news recommendation, such as modeling user preferences based on be- havior, accounting for the influence of the news agenda on user interests, and managing the rapid decay of news items. Additionally, the challenge embraces normative complexities, investigating the effects of recommender systems on news flow and their alignment with editorial values. We summarize the challenge setup, dataset characteristics, and evaluation metrics. Finally, we announce the winners and highlight their contributions. The dataset is available at: https://recsys.eb.dk
Spherical convolutions and their application in molecular modelling
Get PDFConvolutional neural networks are increasingly used outside the domain of image analysis, in particular in various areas of the natural sciences concerned with spatial data. Such networks often work out-of-the box, and in some cases entire model architectures from image analysis can be carried over to other problem domains almost unaltered. Unfortunately, this convenience does not trivially extend to data in non-euclidean spaces, such as spherical data. In this paper, we introduce two strategies for conducting convolutions on the sphere, using either a spherical-polar grid or a grid based on the cubed-sphere representation. We investigate the challenges that arise in this setting, and extend our discussion to include scenarios of spherical volumes, with several strategies for parameterizing the radial dimension. As a proof of concept, we conclude with an assessment of the performance of spherical convolutions in the context of molecular modelling, by considering structural environments within proteins. We show that the models are capable of learning non-trivial functions in these molecular environments, and that our spherical convolutions generally outperform standard 3D convolutions in this setting. In particular, despite the lack of any domain specific feature-engineering, we demonstrate performance comparable to state-of-the-art methods in the field, which build on decades of domain-specific knowledge
MIWAE: Deep Generative Modelling and Imputation of Incomplete Data
Get PDFWe consider the problem of handling missing data with deep latent variable models (DLVMs). First, we present a simple technique to train DLVMs when the training set contains missing-at-random data. Our approach, called MIWAE, is based on the importance-weighted autoencoder (IWAE), and maximises a potentially tight lower bound of the log-likelihood of the observed data. Compared to the original IWAE, our algorithm does not induce any additional computational overhead due to the missing data. We also develop Monte Carlo techniques for single and multiple imputation using a DLVM trained on an incomplete data set. We illustrate our approach by training a convolutional DLVM on incomplete static binarisations of MNIST. Moreover, on various continuous data sets, we show that MIWAE provides extremely accurate single imputations, and is highly competitive with state-of-the-art methods
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