A semi-Lagrangian scheme for the game pp-Laplacian via pp-averaging

We present and analyze an approximation scheme for the two-dimensional game $p$-Laplacian in the framework of viscosity solutions. The approximation is based on a semi-Lagrangian scheme which exploits the idea of $p$-averages. We study the properties of the scheme and prove that it converges, in particular cases, to the viscosity solution of the game $p$-Laplacian. We also present a numerical implementation of the scheme for different values of $p$; the numerical tests show that the scheme is accurate.Comment: 34 pages, 3 figures. To appear on Applied Numerical Mathematic

A multimodal turn in Digital Humanities. Using contrastive machine learning models to explore, enrich, and analyze digital visual historical collections

Until recently, most research in the Digital Humanities (DH) was monomodal, meaning that the object of analysis was either textual or visual. Seeking to integrate multimodality theory into the DH, this article demonstrates that recently developed multimodal deep learning models, such as Contrastive Language Image Pre-training (CLIP), offer new possibilities to explore and analyze image–text combinations at scale. These models, which are trained on image and text pairs, can be applied to a wide range of text-to-image, image-to-image, and image-to-text prediction tasks. Moreover, multimodal models show high accuracy in zero-shot classification, i.e. predicting unseen categories across heterogeneous datasets. Based on three exploratory case studies, we argue that this zero-shot capability opens up the way for a multimodal turn in DH research. Moreover, multimodal models allow scholars to move past the artificial separation of text and images that was dominant in the field and analyze multimodal meaning at scale. However, we also need to be aware of the specific (historical) bias of multimodal deep learning that stems from biases in the training data used to train these models.</p

Practical experiences with the reduction of prevalence of Salmonella infections in pig herds

Salmonella reduction is an issue with still increasing importance for the swine production. PIC in Germany has already started with specific interventions to reduce Salmonella prevalence in their breeding herds in 2001 – just one year before the “QS Qualität und Sicherheit GmbH” began to set up the serological Salmonella monitoring in German finishing pigs

The expanding bipolar shell of the helium nova V445 Puppis

From multi-epoch adaptive optics imaging and integral field unit spectroscopy, we report the discovery of an expanding and narrowly confined bipolar shell surrounding the helium nova V445 Puppis (Nova Puppis 2000). An equatorial dust disc obscures the nova remnant, and the outflow is characterized by a large polar outflow velocity of 6720 +/- 650 km s(-1) and knots moving at even larger velocities of 8450 +/- 570 km s(-1). We derive an expansion parallax distance of 8.2 +/- 0.5 kpc and deduce a pre-outburst luminosity of the underlying binary of log L/L-circle dot = 4.34 +/- 0.36. The derived luminosity suggests that V445 Puppis probably contains a massive white dwarf accreting at high rate from a helium star companion making it part of a population of binary stars that potentially lead to supernova Ia explosions due to accumulation of helium-rich material on the surface of a massive white dwarf

Detecting treatment-subgroup interactions in clustered data with generalized linear mixed-effects model trees

Identification of subgroups of patients for whom treatment A is more effective than treatment B, and vice versa, is of key importance to the development of personalized medicine. Tree-based algorithms are helpful tools for the detection of such interactions, but none of the available algorithms allow for taking into account clustered or nested dataset structures, which are particularly common in psychological research. Therefore, we propose the generalized linear mixed-effects model tree (GLMM tree) algorithm, which allows for the detection of treatment-subgroup interactions, while accounting for the clustered structure of a dataset. The algorithm uses model-based recursive partitioning to detect treatment-subgroup interactions, and a GLMM to estimate the random-effects parameters. In a simulation study, GLMM trees show higher accuracy in recovering treatment-subgroup interactions, higher predictive accuracy, and lower type II error rates than linear-model-based recursive partitioning and mixed-effects regression trees. Also, GLMM trees show somewhat higher predictive accuracy than linear mixed-effects models with pre-specified interaction effects, on average. We illustrate the application of GLMM trees on an individual patient-level data meta-analysis on treatments for depression. We conclude that GLMM trees are a promising exploratory tool for the detection of treatment-subgroup interactions in clustered datasets.Article / Letter to editorInstituut Psychologi

Computer Vision for the Humanities: An Introduction to Deep Learning for Image Classification (Part 1)

This is the first of a two-part lesson introducing deep learning based computer vision methods for humanities research. Using a dataset of historical newspaper advertisements and the fastai Python library, the lesson walks through the pipeline of training a computer vision model to perform image classification