Abstract Knowledge of the present-day crustal in-situ stress field is a key for the
understanding of geodynamic processes such as global plate tectonics and earthquakes. It …

Recently, hyperbolic deep neural networks (HDNNs) have been gaining momentum as the
deep representations in the hyperbolic space provide high fidelity embeddings with few …

Precipitation whiplash, including abrupt shifts between wet and dry extremes, can cause
large adverse impacts on human and natural systems. Here we quantify observed and …

We present a novel approach to perform object-independent grasp synthesis from depth
images via deep neural networks. Our generative gras** convolutional neural network …

A warming climate is expected to have an impact on the magnitude and timing of river
floods; however, no consistent large-scale climate change signal in observed flood …

The Variational Auto-Encoder (VAE) is one of the most used unsupervised machine learning
models. But although the default choice of a Gaussian distribution for both the prior and …

A modern introduction to synchronization phenomena, this text presents recent discoveries
and the current state of research in the field, from low-dimensional systems to complex …

This chapter considers tests of fit based on the empirical distribution function (EDF). The
EDF is a step function, calculated from the sample, which estimates the population …

Actigraphy, a method for inferring sleep/wake patterns based on movement data gathered
using actigraphs, is increasingly used in population-based epidemiologic studies because …

Öz Bu çalışmanın amacı; istatistiksel analiz süreçleri içeren eğitim araştırmalarında normallik
varsayımlarının, kullanılacak istatistiksel yöntemin belirlenmesinde ve uygulanmasında …