Publications associées à Accelerated Microstructure Imaging via Convex Optimization (AMICO) in crossing fibers

Publications associées (40)

Voxel-Wise Brain Graphs From Diffusion MRI: Intrinsic Eigenspace Dimensionality and Application to Functional MRI

Goal: Structural brain graphs are conventionally limited to defining nodes as gray matter regions from an atlas, with edges reflecting the density of axonal projections between pairs of nodes. Here we explicitly model the entire set of voxels within a brai ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2025

Fine-grained mapping of neural progenitors subtypes in the embryonic brain unveils context-dependent roles of signalling on cell fate decisions

Irina Khven

The mammalian brain develops through precisely orchestrated events that transform a simple neural tube into a complex structure. While single-cell RNA sequencing has revealed remarkable cellular heterogeneity, the lack of spatial context limits our underst ...

EPFL2025

Membrane mechanics dictate axonal pearls-on-a-string morphology and function

Graham Knott

Axons are ultrathin membrane cables that are specialized for the conduction of action potentials. Although their diameter is variable along their length, how their morphology is determined is unclear. Here, we demonstrate that unmyelinated axons of the mou ...

2025

Randomized Block Gram–Schmidt Process for the Solution of Linear Systems and Eigenvalue Problems

Laura Grigori

Society for Industrial & Applied Mathematics (SIAM)2025

Individually optimized dynamic parallel transmit pulses for 3D high‐resolution SPACE imaging at 7T

Emilie Claire Sleight, Natalia Pato Montemayor, Bénédicte Marie Maréchal, Tom Hilbert, Thomas Yu, Tommaso Di Noto, Jocelyn Philippe, Lina Bacha

Purpose: Although clinical 7T MRI offers various advantages compared to lower field strengths, achieving spatially uniform flip angle distributions remains a challenge. Sampling Perfection with Application optimized Contrast using different flip angle Evol ...

Wiley2025

On decentralized computation of the leader's strategy in bi-level games

Nikolaos Geroliminis, Marko Maljkovic

Motivated by the omnipresence of hierarchical structures in many real-world applications, this study delves into the intricate realm of bi-level games, with a specific focus on exploring local Stackelberg equilibria as a solution concept. While existing li ...

2025

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

Safety, tolerability and blinding efficiency of non-invasive deep transcranial temporal interference stimulation: first experience from more than 250 sessions

Friedhelm Christoph Hummel, Maximilian Jonas Wessel, Elena Beanato, Pierre Theopistos Vassiliadis, Fabienne Windel

Objective. Selective neuromodulation of deep brain regions has for a long time only been possible through invasive approaches, because of the steep depth-focality trade-off of conventional non-invasive brain stimulation (NIBS) techniques. Approach. An appr ...

Iop Publishing Ltd2024

Mesh-based topology, shape and sizing optimization of ribbed plates

Ahmad Abdulrahman Ahmad Majdouba

In this paper, we present a new parameterization and optimization procedure for minimizing the weight of ribbed plates. The primary goal is to reduce embodied CO2 in concrete floors as part of the effort to diminish the carbon footprint of the construction ...

Springer2024

A ride time-oriented scheduling algorithm for dial-a-ride problems

Nikolaos Geroliminis, Claudia Bongiovanni, Mor Kaspi

This paper offers a new algorithm to efficiently optimize scheduling decisions for dial-a-ride problems (DARPs), including problem variants considering electric and autonomous vehicles (e-ADARPs). The scheduling heuristic, based on linear programming theor ...

Pergamon-Elsevier Science Ltd2024

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Anastasiia Koloskova

Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...

EPFL2024

COMMUNICATION LOWER BOUNDS AND OPTIMAL ALGORITHMS FOR MULTIPLE TENSOR-TIMES-MATRIX COMPUTATION

Laura Grigori

Multiple tensor-times-matrix (Multi-TTM) is a key computation in algorithms for computing and operating with the Tucker tensor decomposition, which is frequently used in multidimensional data analysis. We establish communication lower bounds that determine ...

Siam Publications2024

Frequency domain data-driven robust and optimal control

Philippe Louis Schuchert

The goal of this thesis is to propose pragmatic solutions to real challenges faced in the industry. The scope of this thesis encompasses two subjects: frequency-based structured controller synthesis for linear time-invariant (LTI) systems on one side, and ...

EPFL2024

Code for project entitled "study on conformal antennas, proof of concept prototype for a UAV"

Anja Skrivervik, Yuanyan Su

This code is used for developing the project entitled “Study on conformal antennas, proof of concept prototype for a UAV”, from the aspects of theory, design, and implementation. This code aims to speed up the investigation of an arbitrary phased array ant ...

Zenodo2024

Safe Zeroth-Order Convex Optimization Using Quadratic Local Approximations

Giancarlo Ferrari Trecate, Yuning Jiang, Maryam Kamgarpour, Baiwei Guo

We address black-box convex optimization problems, where the objective and constraint functions are not explicitly known but can be sampled within the feasible set. The challenge is thus to generate a sequence of feasible points converging towards an optim ...

IEEE2023

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Mehmet Fatih Sahin

Non-convex constrained optimization problems have become a powerful framework for modeling a wide range of machine learning problems, with applications in k-means clustering, large- scale semidefinite programs (SDPs), and various other tasks. As the perfor ...

EPFL2023

Universal and adaptive methods for robust stochastic optimization

Ali Kavis

Within the context of contemporary machine learning problems, efficiency of optimization process depends on the properties of the model and the nature of the data available, which poses a significant problem as the complexity of either increases ad infinit ...

EPFL2023

A Spatial Branch and Bound Algorithm for Continuous Pricing with Advanced Discrete Choice Demand Modeling

Michel Bierlaire

In this paper, we present a spatial branch and bound algorithm to tackle the continuous pricing problem, where demand is captured by an advanced discrete choice model (DCM). Advanced DCMs, like mixed logit or latent class models, are capable of modeling de ...

2023

Data-Driven Control and Optimization under Noisy and Uncertain Conditions

Baiwei Guo

Control systems operating in real-world environments often face disturbances arising from measurement noise and model mismatch. These factors can significantly impact the perfor- mance and safety of the system. In this thesis, we aim to leverage data to de ...

EPFL2023

Digital reconstruction of the mammalian spinal cord: from anatomical reference volume to cell type atlas of the mouse spinal cord

Ihor Kuras

The spinal cord is an elongated nervous structure that together with the brain forms the central nervous system. It relays sensory and motor information between the brain and the body, thus controlling most somatic and autonomic body functions. In recent y ...

EPFL2023