Short-term energy use prediction of solar-assisted water heating system: Application case of combined attention-based LSTM and time-series decomposition

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This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SV ...

Automatic speech recognition (ASR) systems are well known to perform poorly on dysarthric speech. Previous works have addressed this by speaking rate modification to reduce the mismatch with typical speech. Unfortunately, these approaches rely on transcrib ...

Many feedforward neural networks (NNs) generate continuous and piecewise-linear (CPWL) mappings. Specifically, they partition the input domain into regions on which the mapping is affine. The number of these so-called linear regions offers a natural metric ...

MammAlps is a multimodal and multi-view dataset of wildlife behavior monitoring. Nine camera-traps were placed at three monitoring sites in the Swiss National Park, from which we curated over 14 hours of video with audio, 2D segmentation maps and 8.5 hours ...

Here we provide the neural data, activation and predictions for the best models and result dataframes of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the behavioral and neural experimental data (cu ...

A robust anomaly detection mechanism should possess the capability to effectively remediate anomalies, restoring them to a healthy state, while preserving essential healthy information. Despite the efficacy of existing generative models in learning the und ...

In the realm of point cloud scene understanding, particularly in indoor scenes, objects are arranged following human habits, resulting in objects of certain semantics being closely positioned and displaying notable inter-object correlations. This can creat ...

The recent geopolitical conflicts in Europe have underscored the vulnerability of the current energy system to the volatility of energy carrier prices. In the prospect of defining robust energy systems ensuring sustainable energy supply in the future, the ...

Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving differential equations by integrating physical laws into the learning process. This work leverages PINNs to simulate gravitational collapse, a critical phenomenon in astro ...

Dynamic aperture is an important concept for the study of non-linear beam dynamics in circular accelerators. It describes the extent of the phase-space region where a particle's motion remains bounded over a given number of turns. Understanding the feature ...

The Birman-Williams theorem gives a connection between the collection of unstable periodic orbits (UPOs) contained within a chaotic attractor and the topology of that attractor, for three-dimensional systems. In certain cases, the fractal dimension of a ch ...

Accurate forecasting of photovoltaic (PV) power production is crucial for the integration of more renewable energy sources into the power grid. PV power production is highly intermittent, due to the stochastic cloud behaviour and cloud dynamics. Previous w ...

Sonic Human-Robot Interaction aims at equipping robots with the ability to convey emotions and intentions via sounds. Such sounds are typically handcrafted by human experts, which results in expensive, small sound sets with limited expressivity. To overcom ...

Buildings play a pivotal role in the ongoing worldwide energy transition, accounting for 30% of the global energy consumption. With traditional engineering solutions reaching their limits to tackle such large-scale problems, data-driven methods and Machine ...

To date, galaxy image simulations for weak lensing surveys usually approximate the light profiles of all galaxies as a single or double Sérsic profile, neglecting the influence of galaxy substructures and morphologies deviating from such a simplified param ...

Microcantilevers are popular transducers thanks to their simple design and a high sensitivity. In many applications, the cantilevers need to be actuated, either to induce a controlled deflection or to excite the resonance. This can be achieved with integra ...

Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence. Altho ...

Machine learning is increasingly integrated with decision making by powering automated systems and informing human decision makers in applications such as CV screening, portfolio allocation, loan risk assessment, and insurance approval. In this thesis, I e ...

Transformer models such as GPT generate human-like language and are predictive of human brain responses to language. Here, using functional-MRI-measured brain responses to 1,000 diverse sentences, we first show that a GPT-based encoding model can predict t ...

Short-term energy use prediction of solar-assisted water heating system: Application case of combined attention-based LSTM and time-series decomposition

Chattez avec Graph Search

Partial discharge localization in power transformer tanks using machine learning methods

Unsupervised Rhythm and Voice Conversion of Dysarthric to Healthy Speech for ASR

On the number of regions of piecewise linear neural networks

MammAlps: A multi-view video behavior monitoring dataset of wild mammals in the Swiss Alps

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Self-Supervised Anomaly Segmentation via Diffusion Models with Dynamic Transformer UNet

Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange

Identification of typical district configurations: A two-step global sensitivity analysis framework

SPINN: Advancing Cosmological Simulations of Fuzzy Dark Matter with Physics Informed Neural Networks

Optimizing Dynamic Aperture Studies with Active Learning

The topology of a chaotic attractor in the Kuramoto-Sivashinsky equation

Network time series forecasting in photovoltaics power production

Automated Generation of Emotion-Conveying Sounds for Robots

Fusing Pre-existing Knowledge and Machine Learning for Enhanced Building Thermal Modeling and Control

Euclid preparation: LXVII. Deep learning true galaxy morphologies for weak lensing shear bias calibration

Fabrication of fluid-compatible trilayer cantilevers with integrated piezoelectric actuators

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Machine Learning in Decision-Making Systems: Fairness, Robustness, and Data Bias

Driving and suppressing the human language network using large language models