Publications associées à Simultaneous Quantification of Multiple Drugs by Machine Learning on Electrochemical Sensors

Publications associées (27)

Ion Sieving in Two-Dimensional Membranes from First Principles

Nicola Marzari, Nicéphore Arthur François Bonnet

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 ...

2025

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

Julia Schmale, Andrea Baccarini, Roman Pohorsky

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 ...

COPERNICUS GESELLSCHAFT MBH2025

Masked Diffusion Strategy for Privacy-Preserving Distributed Learning

Ali H. Sayed

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 ...

2025

Learning Hand State Estimation for a Light Exoskeleton

Luca Randazzo

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 ...

Springer Nature2025

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

Georges Meylan, Benjamin Yvan Alexandre Clement, Olivia Mueller

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 ...

2025

Combinatorial Aspects of Continuous Optimization: Graph Polynomials and Neural Networks

Amire Bendjeddou

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 ...

EPFL2025

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

Mohamed Bouri

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 ...

Wiley2025

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

Florent Gérard Krzakala, Bruno Loureiro, Ludovic Théo Stephan, Yatin Dandi, Luca Pesce

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 ...

2024

Rehabilitation exoskeleton system with bidirectional virtual reality feedback training strategy

Rui Wu

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 ...

2024

A qualitative AI security risk assessment of autonomous vehicles

Alexandre Massoud Alahi, Kathrin Grosse

This paper systematically analyzes the security risks associated with artificial intelligence (AI) components in autonomous vehicles (AVs). Given the increasing reliance on AI for various AV functions, from perception to control, the potential for security ...

Elsevier BV2024

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

Michael Graetzel, Shaik Mohammed Zakeeruddin, Aswani Yella

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 ...

2024

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

Frédéric Courbin, Georges Meylan, Georgios Vernardos, Benjamin Yvan Alexandre Clement

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 ...

Edp Sciences S A2024

Inverse Graph Learning from Social Interactions

Valentina Shumovskaia

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 ...

EPFL2024

A PAC-Bayesian Framework for Optimal Control with Stability Guarantees

Mahrokh Ghoddousiboroujeni, Giancarlo Ferrari Trecate, Clara Lucía Galimberti

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 ...

IEEE2024

HyperMixer: An MLP-based Low Cost Alternative to Transformers

Fabio James Fehr, François Fleuret, Arnaud Pannatier, Haolin Chen

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 ...

Assoc Computational Linguistics-Acl2023

On-chip artificial neural network for PET source position reconstruction

Edoardo Charbon, Claudio Bruschini, Emanuele Ripiccini

This work presents the characterization results and methods used to determine the reconstruction of a 22Na point source placed in a coincidence setup. For this purpose, a novel, fully-integrated, reconfigurable feed-forward artificial neural network was im ...

IEEE2023

Overcoming the Domain Gap in Neural Action Representations

Pascal Fua, Pavan P Ramdya, Semih Günel, Sina Honari, Florian Aymanns

Relating behavior to brain activity in animals is a fundamental goal in neuroscience, with practical applications in building robust brain-machine interfaces. However, the domain gap between individuals is a major issue that prevents the training of genera ...

SPRINGER2022

The dynamics of representation learning in shallow, non-linear autoencoders

Maria Refinetti

Autoencoders are the simplest neural network for unsupervised learning, and thus an ideal framework for studying feature learning. While a detailed understanding of the dynamics of linear autoencoders has recently been obtained, the study of non-linear aut ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2022

Physics-based representations for machine learning properties of chemical reactions

Anne-Clémence Corminboeuf, Matthew Wodrich, Alberto Fabrizio, Puck Elisabeth van Gerwen

Physics-based representations constructed using only atomic positions and nuclear charges (also known as quantum machine learning, QML) allow for the reliable and efficient inference of molecular properties from training data. Chemistry is a science rooted ...

2022

UQGAN: A Unified Model for Uncertainty Quantification of Deep Classifiers trained via Conditional GANs

Matthias Rottmann

We present an approach to quantifying both aleatoric and epistemic uncertainty for deep neural networks in image classification, based on generative adversarial networks (GANs). While most works in the literature that use GANs to generate out-of-distributi ...

2022