A PAC-Bayesian Framework for Optimal Control with Stability Guarantees

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To protect both local gradients and estimated parameters in distributed learning, this paper introduces a masked diffusion (MD) strategy, leading to two algorithms: the MD stochastic gradient (MD-SG) and the MD primal-dual stochastic gradient (MPD-SG). The ...

We propose a machine learning-based estimator of the hand state for rehabilitation purposes, using light exoskeletons. These devices are easy to use and useful for delivering domestic and frequent therapies. We build a supervised approach using information ...

Electrochemical gas sensors (EGSs) have been used to measure the surface distributions and vertical profiles of trace gases in the wintertime Arctic boundary layer during the Alaskan Layered Pollution and Chemical Analysis (ALPACA) field experiment in Fair ...

A first-principles approach for calculating ion separation in solution through two-dimensional (2D) membranes is proposed and applied. Ionic energy profiles across the membrane are obtained first, where solvation effects are simulated explicitly with machi ...

In this thesis, we study how combinatorial structures and symmetries emerge from analytic properties of graph polynomials and neural networks. Graph polynomials encode combinatorial aspects of the underlying graph in their coefficients and are remarkably u ...

This study introduces a novel frequency-based data-driven controller for adaptive optics, using power spectral density for optimization while ensuring stability criteria. It addresses disturbance rejection, command amplitude constraints and system transfer ...

In the field of model predictive control, Dataenabled Predictive Control (DeePC) offers direct predictive control, bypassing traditional modeling. However, challenges emerge with increased computational demand due to recursive data updates. This paper intr ...

For high-dimensional Gaussian data, we investigate theoretically how the features of a two-layer neural network adapt to the structure of the target function through a few large batch gradient descent steps, leading to an improvement in the approximation c ...

Virtual reality (VR) technology revitalises rehabilitation training by creating rich, interactive virtual rehabilitation scenes and tasks that deeply engage patients. Robotics with immersive VR environments have the potential to significantly enhance the s ...

Forthcoming imaging surveys will increase the number of known galaxy-scale strong lenses by several orders of magnitude. For this to happen, images of billions of galaxies will have to be inspected to identify potential candidates. In this context, deep-le ...

Electrochemical devices and systems are significant for the development of therapeutic drug monitoring (TDM) and personalized therapy. However, electrochemical sensors are usually not that much selective. Therefore, innovative machine learning (ML) approac ...

Learning from Demonstration (LfD) stands as an efficient framework for imparting human-like skills to robots. Nevertheless, designing an LfD framework capable of seamlessly imitating, generalizing, and reacting to disturbances for long-horizon manipulation ...

Low-altitude aircraft is being developed as a new mode of urban transport. This will give rise to new urban air transport systems, called low-altitude air city transport (LAAT) systems. Recent works show that the Macroscopic Fundamental Diagram (MFD) is a ...

Transformer-based architectures are the model of choice for natural language understanding, but they come at a significant cost, as they have quadratic complexity in the input length, require a lot of training data, and can be difficult to tune. In the pur ...

Three different J-integral formulations to derive the experimental translaminar toughness of composites from compact tension tests with a large-scale fracture process zone are implemented and discussed. They improve the existing approaches by taking advant ...

A PAC-Bayesian Framework for Optimal Control with Stability Guarantees

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Masked Diffusion Strategy for Privacy-Preserving Distributed Learning

Learning Hand State Estimation for a Light Exoskeleton

Surface distributions and vertical profiles of trace gases (CO, O3, NO, NO2) in the Arctic wintertime boundary layer using low-cost sensors during ALPACA-2022

Ion Sieving in Two-Dimensional Membranes from First Principles

Combinatorial Aspects of Continuous Optimization: Graph Polynomials and Neural Networks

Upgrading SPHERE with the second stage AO system SAXO+: frequency-based data-driven controller for adaptive optics

Efficient Recursive Data-enabled Predictive Control

How Two-Layer Neural Networks Learn, One (Giant) Step at a Time

Rehabilitation exoskeleton system with bidirectional virtual reality feedback training strategy

Euclid preparation XXXIII. Characterization of convolutional neural networks for the identification of galaxy-galaxy strong-lensing events

Simultaneous Quantification of Multiple Drugs by Machine Learning on Electrochemical Sensors

Logic Learning From Demonstrations for Multi-Step Manipulation Tasks in Dynamic Environments

Aircraft Departures Management for Low Altitude Air City Transport based on Macroscopic Fundamental Diagram

HyperMixer: An MLP-based Low Cost Alternative to Transformers

Implementation and parametric study of J-integral data reduction methods for the translaminar toughness of hierarchical thin-ply composites