Publications associées à A machine learning method for real-time numerical simulations of cardiac electromechanics

Publications associées (26)

Uncertainty in Modern Deep Learning

In this thesis, we address a fundamental challenge in modern deep learning: uncertainty estimation. While deep neural networks have achieved remarkable success across diverse and impactful applications-from robotics to large language models and advanced in ...

EPFL2025

Towards Unifying Evaluation of Counterfactual Explanations: Leveraging Large Language Models for Human-Centric Assessments

Rasmus Moorits Veski

As machine learning models evolve, maintaining transparency demands more human-centric explainable AI techniques. Counterfactual explanations, with roots in human reasoning, identify the minimal input changes needed to obtain a given output, and hence, are ...

Association for the Advancement of Artificial Intelligence2025

Integrating AI across the Chemistry Discovery Cycle: Advancing Sustainable Chemistry through Digital Methods

Oliver Tobias Schilter

The chemical industry faces mounting pressure to develop more sustainable processes while accelerating innovation to address climate challenges. Traditional discovery approaches in chemistry follow an iterative cycle: forming hypotheses about new molecules ...

EPFL2025

Uncertainty analysis of field trials of low-cost Bi-TiO_2 -P25 solar photocatalyst for sustainable water treatment

Paolo Perona, Giulio Calvani

Solar photocatalysis has the potential to reduce chemical and microbial contaminants in water and make it safer for consumption in an effective and sustainable manner. This has been studied and proven well under laboratoryscale conditions. In our previous ...

2025

Structural equation models for multivariate extremes

Mario Krali

This thesis contributes to the modelling of multivariate rare events by combining extreme value theory and graphical modelling. The central theme involves structural equation models (SEMs), which are powerful tools used to express causal extremal dependenc ...

EPFL2025

Emo-film: a Multimodal Dataset for Affective Neuroscience Using Naturalistic Stimuli

Marina Almato Bellavista, Dimitri Nestor Alice Van De Ville, Elenor Morgenroth, Stefano Moia, Yam Vilaclara, Maria Ploumitsakou

The Emo-FilM dataset stands for Emotion research using Films and fMRI in healthy participants. This dataset includes emotion annotations by 44 raters for 14 short films with a combined duration of over 2 1/2 hours and recordings of respiration, heart rate, ...

NATURE PORTFOLIO2025

Partial wave analysis of $ \psi (3686)\to \Lambda {\overline{\Sigma}}^0{\pi}^0 $ + c.c

Yunxuan Song

Based on a sample of (2712 . 4 ± 14 . 3) × 10 6 ψ (3686) events collected with the BESIII detector, a partial wave analysis of the decay

\psi (3686)\to \Lambda {\overline{\Sigma}}^0{\pi}^0

+ c . c . is performed to investigate Λ * and Σ * resonances in ...

Springer2025

Sequential Representation Learning via Static-Dynamic Conditional Disentanglement

Pascal Frossard

This paper explores self-supervised disentangled representation learning within sequential data, focusing on separating time-indep- endent and time-varying factors in videos. We propose a new model that breaks the usual independence assumption between thos ...

Springer Science and Business Media Deutschland GmbH2025

Molecular characterization of an adhesion GPCR signal transduction using Graph-neural networks

Matthieu François Marfoglia

Key cellular functions depend on the transduction of extracellular mechanical signals by specialized membrane receptors, including adhesion G-protein coupled receptors (aGPCRs). While recent structural studies support aGPCR activation through shedding of t ...

EPFL2025

Enhancing sensitivity in fluorescence measurements across an ultra-wide concentration range through optimizing combined-segments strategy

Daniel Anthony Jacobs

Fluorescence measurement technology is crucial in analytical instrumentation. However, when applied in fields such as environmental science and medical research for quantification purposes, it requires prior estimation of sample concentration and sample di ...

Elsevier2025

Machine learning-aided generative molecular design

Philippe Schwaller, Jeff Guo

Machine learning has provided a means to accelerate early-stage drug discovery by combining molecule generation and filtering steps in a single architecture that leverages the experience and design preferences of medicinal chemists. However, designing mach ...

Nature Portfolio2024

Supplementary files for Machine learning for histological annotation and quantification of cortical layers

Olivier Burri, Julie Meystre

Creators Meystre Julie Olivier Burri Contributors Jean Jacquemier Description This dataset contains 7 QuPath projects. The raw data images linked to these projects and located in other Zenodo datasets need to be downloaded as well. The raw data contains im ...

Zenodo2024

An Efficient Machine Learning Model for Lightning Localization via Lightning-Induced Voltages on Transmission Lines

Farhad Rachidi-Haeri, Marcos Rubinstein, Hamidreza Karami

In this paper, three machine learning (ML)-based approaches—XGBoost, Artificial Neural Network (ANN), and Random Forest algorithms—are compared for the localization of lightning through the analysis of lightninginduced voltages on power transmission lines. ...

URSI – International Union of Radio Science2024

Image sensitive spectral response of semiconductor random network lasers

Kirsten Emilie Moselund

We experimentally study the spectral lasing response of on-chip InP network random lasers under illumination of different input image shapes. Deep-learning models have become increasingly omipresent throughout society. However, they are blighted by exponen ...

SPIE-INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING2024

TEE4EHR: Transformer event encoder for better representation learning in electronic health records

David Atienza Alonso, Anisoara Ionescu, Hojjat Karami

Irregular sampling of time series in electronic health records (EHRs) is one of the main challenges for developing machine learning models. Additionally, the pattern of missing values in certain clinical variables is not at random but depends on the decisi ...

2024

Newton Bases and Event-Triggered Adaptive Control in Native Spaces

Nathan Russell Powell

This paper extends recent methods of native space embedding for adaptive control by deriving event-driven controllers that modify the basis used for approximation of the functional uncertainty. Using the power function for the native space, trigger conditi ...

Institute of Electrical and Electronics Engineers Inc.2024

AMCG: a graph dual atomic-molecular conditional molecular generator

Andrea Cavalli

Drug design is both a time consuming and expensive endeavour. Computational strategies offer viable options to address this task; deep learning approaches in particular are indeed gaining traction for their capability of dealing with chemical structures. A ...

IOP Publishing2024

Floorplan Isovist Dataset

Mikhael Johanes

This dataset was created as part of the PhD project Machine Understanding of Architectural Space: From Analytical to Generative Applications at EPFL Media and Design Lab (LDM). It was derived from the Cubicasa5K dataset. The dataset contains preprocessed d ...

Zenodo2024

Quasi-linear homogenization for large-inertia laminar transport across permeable membranes

Giuseppe Antonio Zampogna, Kevin Wittkowski

Porous membranes are thin solid structures that allow the flow to pass through their tiny openings, called pores. Flow inertia may play a significant role in several filtration flows of natural and engineering interest. Here, we develop a predictive macros ...

Cambridge University Press2024

High-dimensional non-convex landscapes and gradient descent dynamics

Davide Ghio

In these lecture notes we present different methods and concepts developed in statistical physics to analyze gradient descent dynamics in high-dimensional non-convex landscapes. Our aim is to show how approaches developed in physics, mainly statistical phy ...

2024