On the modelling of the fatigue-induced damage in Francis turbines start-up sequences

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We propose a method for unsupervised reconstruction of a temporally-consistent sequence of surfaces from a sequence of time-evolving point clouds. It yields dense and semantically meaningful correspondences between frames. We represent the reconstructed su ...

Dynamic hand gesture recognition (HGR) is critical for real-time prosthetics and neural robotics applications, where high-density surface electromyography (HD-sEMG) signals provide valuable insights into muscle activity. However, adapting machine learning ...

From hydropower to wind power, renewable energy is converted to electricity, putting the electric grid at the crossroads of the green energy generation and consumption. The grid must also adapt and scale up to an increased capacity and to the stochastic na ...

This thesis focuses on the development of advanced algorithmic techniques, primarily Markov Chain Monte Carlo (MCMC) methods, and message passing algorithms, to tackle high-dimensional optimization and inference problems. The algorithms used have a probabi ...

However, their use in imaging applications has been limited by their relatively large pixel size (≥ 55 μm) and high-energy (>80 keV) electrons scattering over multiple pixels in the sensor layer. To realize the full potential of fast, radiation-hard HPDs a ...

Objective. Many psychiatric disorders involve excessive avoidant or defensive behavior, such as avoidance in anxiety and trauma disorders or defensive rituals in obsessive-compulsive disorders. Developing algorithms to predict these behaviors from local fi ...

Simulations of chemical reactivity in condensed phase systems represent an ongoing challenge in computational chemistry, where traditional quantum chemical approaches typically struggle with both the size of the system and the potential complexity of the r ...

In this paper, we present a model for the analytical computation of the power flow sensitivity coefficients (SCs) for hybrid AC/DC networks. The SCs are defined as the partial derivates of the nodal voltages with respect to the active and reactive power in ...

It is widely believed that the success of deep networks lies in their ability to learn a meaningful representation of the features of the data. Yet, understanding when and how this feature learning improves performance remains a challenge. For example, it ...

Despite a lack of scientific consensus on the definition of emotions, they are generally considered to involve several modifications in the mind, body, and behavior. Although psychology theories emphasized multi-componential characteristics of emotions, li ...

The majority of uncertainty quantification methods for deep object detectors are based on the network output, such as sampling strategies like Monte-Carlo dropout or deep ensembles with straight-forward transfers to object detection. Here, we study gradien ...

Accurate forecasting of solar power generation with fine temporal and spatial resolution is vital for the operation of the power grid. However, state-of-the-art approaches that combine machinelearning with numerical weather predictions (NWP) have coarse re ...

Persistent Homology (PH) has been successfully used to train networks to detect curvilinear structures and to improve the topological quality of their results. However, existing methods are very global and ignore the location of topological features. In th ...

This paper presents a novel deep learning-based travel behaviour choice model. Our proposed Residual Logit (ResLogit) model formulation seamlessly integrates a Deep Neural Network (DNN) architecture into a multinomial logit model. Recently, DNN models such ...

We propose BlockProp, a neural network training algorithm. Unlike backpropagation, it does not rely on direct top-to-bottom propagation of an error signal. Rather, by interpreting backpropagation as a constrained optimization problem we split the neural ne ...

On the modelling of the fatigue-induced damage in Francis turbines start-up sequences

Chattez avec Graph Search

Temporally-Consistent Surface Reconstruction Using Metrically-Consistent Atlases

Low-Rank Adaptation for Transformers in Dynamic Hand Gesture Recognition Using High-Density Surface Electromyography

On the Features and Performances of Converters in Hybrid AC/DC Microgrids

Advances in Algorithms for Sampling, Optimization and Inference in Disordered Systems

Enhancing spatial resolution in MÖNCH for electron microscopy via deep learning

Neural decoding and feature selection methods for closed-loop control of avoidance behavior

Capturing Dichotomic Solvent Behavior in Solute−Solvent Reactions with Neural Network Potentials

Analytical Computation of the Sensitivity Coefficients in Hybrid AC/DC Networks

Learning sparse features can lead to overfitting in neural networks

Brain networks subserving functional core processes of emotions identified with componential modeling

Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors

Spatio-Temporal Graph Neural Networks for Multi-Site PV Power Forecasting

Persistent Homology with Improved Locality Information for more Effective Delineation

Data-driven control of room temperature and bidirectional EV charging using deep reinforcement learning: Simulations and experiments

ResLogit: A residual neural network logit model for data-driven choice modelling

Decoupling Backpropagation using Constrained Optimization Methods