Turns out, 'once in a blue moon' is right now. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. A'Super Blue moon' rose in the skies of the Dutch city of Nijmegen during the night from August 31st to September 1st, 2023. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

Many practical data analysis tasks reduce to learning, from observed samples, how a collection of variables depend on each other. A widely used approach is to fit a Gaussian graphical model, which represents the dependence structure as a graph connecting the variables. In a number of important applications, such as financial returns, gene co-expression, and climate or network analysis, the dependencies tend to be positive: variables move together rather than offset each other. Encoding this positivity through the constraint of multivariate total positivity of order two (MTP2) yields an attractive estimator that produces accurate fits with no tuning required. The resulting graphs are, however, typically much denser than the underlying ground-truth model, which makes them hard to interpret and slow to use in any downstream task that operates on the graph. In this work, we propose a novel highly-scalable approach for learning Gaussian graphical models from data using spectral sparsification; we call it Spectral-MTP2. Spectral graph sparsification is a fundamental method which aims to preserve meaningful properties of a dense graph with a sparser subgraph. We theoretically and empirically investigate and validate our method, and show that learning Gaussian Graphical Models under MTP2 using spectral sparsification preserves MTP2 and approximates well the original model in terms of Kullback-Leibler divergence and Gaussian log-likelihood. In simulations and applications to equity returns and gene expression, we observe that Spectral-MTP2 retains most of the fit quality of the denser MTP2 baseline, while producing substantially sparser and more interpretable graphs.

MV Hondius, the Dutch cruise ship hit by a deadly hantavirus outbreak, has docked at its final destination in Rotterdam. Only the ship's crew were aboard for the last leg of the journey, as all passengers docked off the ship in the Canary Islands between 10 and 11 May. Rotterdam port harbour master René de Vries said 25 mobile homes kitted out with catering and satellite communications would be available for the crew to self-isolate in. Three people - a Dutch couple and a German woman - died after travelling on the ship, with two of them confirmed to have had the virus. The World Health Organization has so far reported 10 cases in total, eight confirmed and two suspected.

When you purchase through links in our articles, we may earn a small commission. The Dutch Fish Doorbell mixes livestreams, crowdsourcing, and conservation in all of the best ways. Every spring in the Dutch city of Utrecht, thousands of fish attempt to migrate through the city's canals to reach spawning grounds, but locked flood gates stay shut for long stretches to manage water levels. So the city came up with a weirdly charming solution: a fish doorbell. The site, called Visdeurbel --or Fish Doorbell--lets anyone in the world help the fish out.

Leiden researchers Professor Daniela Kraft and Mengshi Wei have created microscopic robots that move without sensors, software, or external control. Instead, their behaviour emerges entirely from their shape and the way they interact with their environment. This class of robots opens up entirely new possibilities for biomedical applications. Inspiration to build these robots came from nature. Kraft: "Animals like worms and snakes constantly adapt their shape as they move, which helps them to navigate their environments. Macroscopic robots similarly use flexibility for their function. However, until now, microrobots were either small and rigid, or large and flexible. We wondered if we could realize small and flexible microrobots in our lab."

This paper introduces hyperspherical prototype networks, which unify classification and regression with prototypes on hyperspherical output spaces. For classification, a common approach is to define prototypes as the mean output vector over training examples per class. Here, we propose to use hyperspheres as output spaces, with class prototypes defined a priori with large margin separation. We position prototypes through data-independent optimization, with an extension to incorporate priors from class semantics. By doing so, we do not require any prototype updating, we can handle any training size, and the output dimensionality is no longer constrained to the number of classes. Furthermore, we generalize to regression, by optimizing outputs as an interpolation between two prototypes on the hypersphere. Since both tasks are now defined by the same loss function, they can be jointly trained for multi-task problems. Experimentally, we show the benefit of hyperspherical prototype networks for classification, regression, and their combination over other prototype methods, softmax cross-entropy, and mean squared error approaches.

Combining test statistics from independent trials or experiments is a popular method of meta-analysis. However, there is very limited theoretical understanding of the power of the combined test, especially in high-dimensional models considering composite hypotheses tests. We derive a mathematical framework to study standard meta-analysis testing approaches in the context of the many normal means model, which serves as the platform to investigate more complex models. We introduce a natural and mild restriction on the meta-level combination functions of the local trials. This allows us to mathematically quantify the cost of compressing m trials into real-valued test statistics and combining these. We then derive minimax lower and matching upper bounds for the separation rates of standard combination methods for e.g.

The task of tuning regularization coefficients in regularized regression models with provable guarantees across problem instances still poses a significant challenge in the literature. This paper investigates the sample complexity of tuning regularization parameters in linear and logistic regressions under ℓ1 and ℓ2-constraints in the data-driven setting. For the linear regression problem, by more carefully exploiting the structure of the dual function class, we provide a new upper bound for the pseudo-dimension of the validation loss function class, which significantly improves the best-known results on the problem. Remarkably, we also instantiate the first matching lower bound, proving our results are tight. For tuning the regularization parameters of logistic regression, we introduce a new approach to studying the learning guarantee via an approximation of the validation loss function class. We examine the pseudo-dimension of the approximation class and construct a uniform error bound between the validation loss function class and its approximation, which allows us to instantiate the first learning guarantee for the problem of tuning logistic regression regularization coefficients.

More precisely, we use batches of size 2. Each batch contains one patch with the foreground oversampled. Furthermore, we split each silo's data into training and validation data with 80% and 20% split, respectively. All this pre-processing and patching is done using the nnU-Net library [IJK+21]. Loss function We use the same loss function as proposed by nnU-Net [IJK+21] for the KiTS19 dataset which is based on DICE [Dic45] and on the Cross Entropy loss.