Publications associées à Isotropic Resolution in Fluorescence Imaging by Single Particle Reconstruction

Live-cell imaging powered by computation

The proliferation of microscopy methods for live-cell imaging offers many new possibilities for users but can also be challenging to navigate. The prevailing challenge in live-cell fluorescence microscopy is capturing intra-cellular dynamics while preservi ...

Nature Portfolio2024

A Combination Technique for Optimal Control Problems Constrained by Random PDEs

Fabio Nobile, Tommaso Vanzan

We present a combination technique based on mixed differences of both spatial approximations and quadrature formulae for the stochastic variables to solve efficiently a class of optimal control problems (OCPs) constrained by random partial differential equ ...

2024

A Streamline Upwind Petrov-Galerkin Reduced Order Method for Advection-Dominated Partial Differential Equations Under Optimal Control

Gianluigi Rozza, Fabio Zoccolan

In this paper we will consider distributed Linear-Quadratic Optimal Control Problems dealing with Advection-Diffusion PDEs for high values of the Peclet number. In this situation, computational instabilities occur, both for steady and unsteady cases. A Str ...

Walter De Gruyter Gmbh2024

Characterization of Transverse Instabilities Driven by Electron Cloud

Sofia Carolina Johannesson

Electron cloud continues to be one of the main limiting factors of the Large Hadron Collider (LHC), the biggest accelerator at CERN. These clouds form in the beam chamber when positively charged particles are passing through and cause unwanted effects in b ...

EPFL2024

A Softening Constitutive Law and Gradient-Inelastic Fiber-Based Element for 3-Dimensional Frame Simulations Under Seismic Excitations

Diego Isidoro Heredia Rosa

Steel frame structures are essential components of modern infrastructure. Understanding their behavior under seismic loading is critical for ensuring public safety and minimizing damage that occurs during earthquakes. To accurately predict the response of ...

EPFL2024

FETCH: A Fast and Efficient Technique for Channel Selection in EEG Wearable Systems

David Atienza Alonso, Amir Aminifar, Alireza Amirshahi, José Angel Miranda Calero, Jonathan Dan

The rapid development of wearable biomedical systems now enables real-time monitoring of electroencephalography (EEG) signals. Acquisition of these signals relies on electrodes. These systems must meet the design challenge of selecting an optimal set of el ...

2024

Computation of sensitivity coefficients in fixed source simulations with SERPENT2

Andreas Pautz, Mathieu Hursin

Within the scope of the implementation of a nuclear data pipeline aiming at producing the best possible evaluated nuclear data files, a major point is the production of relevant sensitivity coefficients when including integral benchmark information. Thanks ...

Elsevier Science Sa2024

Optimizing Dynamic Aperture Studies with Active Learning

Tatiana Pieloni, Massimo Giovannozzi, Ekaterina Krymova, Mike Seidel, Davide Di Croce

Dynamic aperture is an important concept for the study of non-linear beam dynamics in circular accelerators. It describes the extent of the phase-space region where a particle's motion remains bounded over a given number of turns. Understanding the feature ...

Iop Publishing Ltd2024

Thermal conductivity of Li 3 PS 4 solid electrolytes with ab initio accuracy

Michele Ceriotti, Federico Grasselli

The vast amount of computational studies on electrical conduction in solid-state electrolytes is not mirrored by comparable efforts addressing thermal conduction, which has been scarcely investigated despite its relevance to thermal management and (over)he ...

Amer Physical Soc2024

Adaptive Finite Elements with Large Aspect Ratio. Application to Aluminium Electrolysis

Paride Passelli

The goal of this work is to use anisotropic adaptive finite elements for the numerical simulation of aluminium electrolysis. The anisotropic adaptive criteria are based on a posteriori error estimates derived for simplified problems. First, we consider an ...

EPFL2024

The role of computational methods in cardiovascular medicine: a narrative review

Alfio Quarteroni, Luca Dede', Christian Vergara, Stefano Pagani

Background and Objective: Computational models of the cardiovascular system allow for a detailed and quantitative investigation of both physiological and pathological conditions, thanks to their ability to combine clinical-possibly patient-specific-data wi ...

Ame Publishing Company2024

Efficient approximation of cardiac mechanics through reduced-order modeling with deep learning-based operator approximation

Alfio Quarteroni, Andrea Manzoni

Reducing the computational time required by high-fidelity, full-order models (FOMs) for the solution of problems in cardiac mechanics is crucial to allow the translation of patient-specific simulations into clinical practice. Indeed, while FOMs, such as th ...

Wiley2023

PROBING INTRACELLULAR ELASTICITY WITH MINIMAL-HESSIAN REGISTRATION

Michaël Unser

We propose an image-based elastography method to measure the heterogeneous stiffness inside a cell and its nucleus. It uses a widely accessible setup consisting of plate compression imaged with fluorescence microscopy. Our framework recovers a spatial map ...

IEEE2023

A Study on Gradient-based Meta-learning for Robust Deep Digital Twins

Olga Fink, Raffael Pascal Theiler, Michele Viscione

Deep-learning-based digital twins (DDT) are a promising tool for data-driven system health management because they can be trained directly on operational data. A major challenge for efficient training however is that industrial datasets remain unlabeled. T ...

Research Publishing2023

Fast in vivo assay of creatine kinase activity in the human brain by P-31 magnetic resonance fingerprinting

Lijing Xin, Songi Lim, Daniel Wenz, Mark Stephan Widmaier

A new and efficient magnetisation transfer P-31 magnetic resonance fingerprinting (MT-P-31-MRF) approach is introduced to measure the creatine kinase metabolic rate kCK

{k}_{\mathrm{CK}}

between phosphocreatine (PCr) and adenosine triphosphate (ATP) i ...

WILEY2023

Hardware-Software co-design Methodologies for Edge AI Optimization

Flavio Ponzina

ML-based edge devices may face memory and computational errors that affect applications' reliability and performance. These errors can be the result of particular working conditions (e.g., radiation areas in physical experiments or avionics) or could be th ...

EPFL2023

Modeling and simulation of neocortical micro- and mesocircuitry. Part II: Physiology and experimentation

Henry Markram, Felix Schürmann, James Gonzalo King, Rajnish Ranjan, Srikanth Ramaswamy, Alexis Arnaudon, Eilif Benjamin Muller, Daniel Keller, Michael Reimann, Vishal Sood, Jean-Denis Georges Emile Courcol, Werner Alfons Hilda Van Geit, Giuseppe Chindemi, Michael Emiel Gevaert, Pramod Shivaji Kumbhar, Armando Romani, Christian Andreas Rössert, Judit Planas Carbonell, Genrich Ivaska, András Ecker, Daniela Egas Santander, Natali Barros Zulaica, Alexander Dietz, Omar Awile, Samuel Lieven D. Lapere, Matthias Wolf, Sirio Bolaños Puchet, Thomas Brice Delemontex, Jorge Blanco Alonso, Mustafa Anil Tuncel, Tanguy Pierre Louis Damart, Maria Reva, James Bryden Isbister, Darshan Mandge, Weina Ji, Aleksandra Zuzanna Teska, Polina Litvak, Gianluca Ficarelli, Joni Henrikki Herttuainen, ,

Confidence Matters: Applications to Semantic Segmentation

Prabhu Teja Sivaprasad

The successes of deep learning for semantic segmentation can in be, in part, attributed to its scale: a notion that encapsulates the largeness of these computational architectures and the labeled datasets they are trained on. These resource requirements hi ...

EPFL2023

Adaptive Symplectic Model Order Reduction Of Parametric Particle-Based Vlasov-Poisson Equation

Cecilia Pagliantini, Jan Sickmann Hesthaven, Nicolò Ripamonti

. High-resolution simulations of particle-based kinetic plasma models typically require a high number of particles and thus often become computationally intractable. This is exacerbated in multi-query simulations, where the problem depends on a set of para ...

AMER MATHEMATICAL SOC2023

Stochastic adaptive resampling for the estimation of discrete choice models

Michel Bierlaire, Nicola Marco Ortelli, Matthieu Marie Cochon de Lapparent

In the field of choice modeling, the availability of ever-larger datasets has the potential to significantly expand our understanding of human behavior, but this prospect is limited by the poor scalability of discrete choice models (DCMs): as sample sizes ...

2023