Real-time neural network prediction for handling two-hands mutual occlusions

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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 ...

Virtual reality (VR) environments simulating height offer a unique platform to investigate neural adaptations to emotionally salient contexts during locomotion. These simulations allow for controlled analysis of motor‐cognitive interactions under perceived ...

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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 ...

Embodied intelligence could grant soft, robotic manipulators with increased performance in adaptability and dexterity. However, we currently have a lack of tools for the forward design of finger behaviours with high-dimensional morphology. We take an open- ...

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 ...

The Euclid Wide Survey (EWS) is predicted to find approximately 170 000 galaxy-galaxy strong lenses from its lifetime observation of 14 000 deg2 of the sky. Detecting this many lenses by visual inspection with professional astronomers and citizen scientist ...

Restoring natural sensation via neuroprosthetics relies on the possibility of encoding complex and nuanced information. For example, an ideal brain–machine interface with sensory feedback would provide the user with sensation about movement, pressure, curv ...

Haptic interfaces enhance the virtual reality experience by emulating real‐world touch‐based interactions and delivering tactile feedback, while also supporting rehabilitation through interactive systems that speed up recovery. However, current haptic tech ...

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 ...

Teravoxel-scale, cellular-resolution images of cleared rodent brains acquired with light-sheet fluorescence microscopy have transformed the way we study the brain. Realizing the potential of this technology requires computational pipelines that generalize ...

Fluent human-robot interactions are essential for robots to be accepted and used in social scenarios including education, healthcare, or entertainment. Foundation models and Large Language Models (LLMs) in particular facilitate natural generation of text i ...

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 ...

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 ...

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 ...

Understanding the brain requires studying its multiscale interactions from molecules to networks. The increasing availability of large-scale datasets detailing brain circuit composition, connectivity, and activity is transforming neuroscience. However, int ...

Accurately predicting the power conversion efficiency (PCE) in dye-sensitized solar cells (DSSCs) represents a crucial challenge, one that is pivotal for the high throughput rational design and screening of promising dye sensitizers. This study presents pr ...

The social learning paradigm for distributed hypothesis testing involves a collection of agents interacting over a graph, each observing streaming data that provide evidence about the state of the environment. These agents perform a series of local Bayesia ...

Stochastic Nonlinear Optimal Control (SNOC) involves minimizing a cost function that averages out the random uncertainties affecting the dynamics of nonlinear systems. For tractability reasons, this problem is typically addressed by minimizing an empirical ...

Real-time neural network prediction for handling two-hands mutual occlusions

Chattez avec Graph Search

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

Walking on the Edge: Brain Connectivity Changes in Response to Virtual Height Challenges

Ion Sieving in Two-Dimensional Membranes from First Principles

Combinatorial Aspects of Continuous Optimization: Graph Polynomials and Neural Networks

Behaviour Range Optimization of the Dexterous Robotic Open Parametric Hand

Masked Diffusion Strategy for Privacy-Preserving Distributed Learning

Euclid: Searches for strong gravitational lenses using convolutional neural nets in Early Release Observations of the Perseus field

Optimization frameworks for bespoke sensory encoding in neuroprosthetics

Reconfigurable Haptic Feedback: Comprehensive Design and Control Framework

Learning Hand State Estimation for a Light Exoskeleton

A deep learning pipeline for three-dimensional brain-wide mapping of local neuronal ensembles in teravoxel light-sheet microscopy

Improving the Fluency of Robot-Human Interaction Through Embodied Expression of Emotion via Foundation Models

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

Rehabilitation exoskeleton system with bidirectional virtual reality feedback training strategy

Simultaneous Quantification of Multiple Drugs by Machine Learning on Electrochemical Sensors

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

Large-Scale Mechanistic Models of Brain Circuits with Biophysically and Morphologically Detailed Neurons

Advanced High-Throughput Rational Design of Porphyrin-Sensitized Solar Cells Using Interpretable Machine Learning

Inverse Graph Learning from Social Interactions

A PAC-Bayesian Framework for Optimal Control with Stability Guarantees