Graph Neural Networks for Electric and Hydraulic Data Fusion to Enhance Short-term Forecasting of Pumped-storage Hydroelectricity

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The present relates to a method for generating a graph representing a virtually lesioned connectome of a patient after a stroke comprising:- providing structural data of the brain of the patient after the stroke;- segmenting said structural data to delinea ...

Recently, cutting-edge brain-machine interfaces (BMIs) have revealed the potential of decoders such as recurrent neural networks (RNNs) in predicting attempted handwriting [1] or speech [2], enabling rapid communication recovery after paralysis. However, c ...

Objective. This study aims to design and implement the first deep learning (DL) model to classify subjects in the prodromic states of Alzheimer's disease (AD) based on resting-state electroencephalographic (EEG) signals. Approach. EEG recordings of 17 heal ...

Europe is currently transitioning from fossil energy sources to renewable generation of electric power. Although fundamental to reach net-zero targets, intermittent renewables are disrupting conventional methods used in operational planning and design of p ...

Objective To investigate the utility of an automatic deep learning (DL) method for segmentation of T2 maps in patients with idiopathic inflammatory myopathy (IIM) against healthy controls, and also the association of quantitative T2 values in patients with ...

Background: Healthy older adults show a decrease in motor performance and motor learning capacity as well as in working memory (WM) performance. WM has been suggested to be involved in motor learning processes, such as sequence learning. Correlational evid ...

Touch-screens have become the most common way of interaction between humans and machines. Nevertheless, the lack of rich haptic-feedback on most of the devices limits the quality and effectivity of the interaction. To develop novel haptic feedback generati ...

With ever greater computational resources and more accessible software, deep neural networks have become ubiquitous across industry and academia. Their remarkable ability to generalize to new samples defies the conventional view, which holds that complex, ...

Animal-borne data loggers today often house several sensors recording simultaneously at high frequency. This offers opportunities to gain fine-scale insights into behaviour from individual-sensor as well as integrated multi-sensor data. In the context of b ...

Apartment searching has been a hot demand all over the world. Understanding the apartment structure will significantly contribute to simplifying the searching process. In this project, the fully convolutional networks are applied to generate semantic segme ...

When making a choice with limited information, we explore new features through trial-and-error to learn how they are related. However, few studies have investigated exploratory behaviour when information is limited. In this study, we address, at both the b ...

Integration of non-dispatchable renewable energy sources such as wind and solar into the grid is challenging due to the stochastic nature of energy sources. Hence, electrical hubs (EH) and virtual power plants that combine non-dispatchable energy sources, ...

Big-Data streaming applications are used in several domains such as social media analysis, financial analysis, video annotation, surveillance, medical services and traffic prediction. These applications, running on different types of platforms from mobile ...

The European Go-Lab project aims to promote Inquiry-based Learning (IBL) with online laboratories. To support teachers and students in this endeavor, the project provides an IBL model (a sequence of inquiry phases) as well as the technological infrastructu ...

This tutorial describes methodologies to model and understand the collective behavior or different types of urban systems. It highlights under what physical properties the aggregated laws will provide reasonable description of congestion for single- and mu ...

Object classification and detection aim at recognizing and localizing objects in real-world images. They are fundamental computer vision problems and a prerequisite for full scene understanding. Their difficulty lies in the large number of possible object ...

This thesis presents possible computational mechanisms by which a humanoid robot can develop a coherent representation of the space within its reach (its peripersonal space), and use it to control its movements. Those mechanisms are inspired by current the ...

Many complex systems may be described by not one but a number of complex networks mapped on each other in a multi-layer structure. Because of the interactions and dependencies between these layers, the state of a single layer does not necessarily reflect w ...

The famous min-cut, max-flow theorem states that a source node can send a commodity through a network to a sink node at the rate determined by the flow of the min-cut separating the source and the sink. Recently it has been shown that by linear re-encoding ...

Graph Neural Networks for Electric and Hydraulic Data Fusion to Enhance Short-term Forecasting of Pumped-storage Hydroelectricity

Chattez avec Graph Search

Post stroke recovery prediction

33.3 MiBMI: A 192/512-Channel 2.46mm² Miniaturized Brain-Machine Interface Chipset Enabling 31-Class Brain-to-Text Conversion Through Distinctive Neural Codes

An attention-based deep learning approach for the classification of subjective cognitive decline and mild cognitive impairment using resting-state EEG

Enabling renewable Europe through optimal design and operation

Assessment of idiopathic inflammatory myopathy using a deep learning method for muscle T2 mapping segmentation

Targeting the frontoparietal network using bifocal transcranial alternating current stimulation during a motor sequence learning task in healthy older adults

Effect of the Impact Contact Duration on Machine Learning Models for Impact Position Detection

Evaluating and Interpreting Deep Convolutional Neural Networks via Non-negative Matrix Factorization

Behavioural compass: animal behaviour recognition using magnetometers

Apartment Structure Estimation Using Fully Convolutional Networks and Graph Model

Exploring Feature Dimensions to Learn a New Policy in an Uninformed Reinforcement Learning Task

Design optimization of electrical hubs using hybrid evolutionary algorithm

Energy-Efficient Co-Design Optimization of Many-Core Platforms for Big-Data Streaming Applications

Exploring Deviation in Inquiry Learning: Degrees of Freedom or Source of Problems?

Tutorial Session in: Large-scale hierarchical control for congested urban networks

Object Classification and Detection in High Dimensional Feature Space

Adaptive sensorimotor peripersonal space representation and motor learning for a humanoid robot

Error and Attack Tolerance of Layered Complex Networks

Information Flow Decomposition for Network Coding