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Publications associées (32)

Quantitative T2 Mapping of Acute Pancreatitis

Tom Hilbert

Background: Quantification of the T2 signal by means of T2 mapping in acute pancreatitis (AP) has the potential to quantify the parenchymal edema. Quantitative T2 mapping may overcome the limitations of previously reported scoring systems for reliable asse ...

Wiley2024

Robust Estimation of the Microstructure of the Early Developing Brain Using Deep Learning

Meritxell Bach Cuadra, Rizhong Lin

Diffusion Magnetic Resonance Imaging (dMRI) is a powerful non-invasive method for studying white matter tracts of the brain. However, accurate microstructure estimation with fiber orientation distribution (FOD) using existing computational methods requires ...

Springer2023

Partial Information Sharing Over Social Learning Networks

Ali H. Sayed, Virginia Bordignon

This work addresses the problem of sharing partial information within social learning strategies. In social learning, agents solve a distributed multiple hypothesis testing problem by performing two operations at each instant: first, agents incorporate inf ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Density estimation in RKHS with application to Korobov spaces in high dimensions

Fabio Nobile, Yoshihito Kazashi

A kernel method for estimating a probability density function from an independent and identically distributed sample drawn from such density is presented. Our estimator is a linear combination of kernel functions, the coefficients of which are determined b ...

2023

Experimental study of stability, quench propagation and detection methods on 15 kA sub-scale HTS fusion conductors in SULTAN

Pierluigi Bruzzone, Kamil Sedlák, Nikolay Bykovskiy, Ortensia Dicuonzo

High-temperature superconductors (HTSs) enable exclusive operating conditions for fusion magnets, boosting their performance up to 20 T generated magnetic fields in the temperature range from 4 K to 20 K. One of the main technological issues of HTS conduct ...

IOP Publishing Ltd2023

Combined use of intraoperative MRI and awake tailored microsurgical resection to respect functional neural networks: preliminary experience

Constantin Tuleasca

INTRODUCTION: The combined use of intraoperative MRI and awake surgery is a tailored microsurgical resection to respect functional neural networks (mainly the language and motor ones). Intraoperative MRI has been classically considered to increase the exte ...

2023

SmartSwim, a Novel IMU-Based Coaching Assistance

Kamiar Aminian, Farzin Dadashi, Fabien Massé, Mahdi Hamidi Rad

Swimming coaches provide regular timed and technical feedback to swimmers and guide them efficiently in training sessions. Due to the complexity of swimmers' performance, which is not visible in qualitative observation, quantitative and objective performan ...

MDPI2022

Attention-based domain adaptation for single-stage detectors

Mathieu Salzmann, Vidit Vidit

While domain adaptation has been used to improve the performance of object detectors when the training and test data follow different distributions, previous work has mostly focused on two-stage detectors. This is because their use of region proposals make ...

SPRINGER2022

Improved Inference On Risk Measures For Univariate Extremes

Anthony Christopher Davison, Léo Raymond-Belzile

We discuss the use of likelihood asymptotics for inference on risk measures in univariate extreme value problems, focusing on estimation of high quantiles and similar summaries of risk for uncertainty quantification. We study whether higher-order approxima ...

INST MATHEMATICAL STATISTICS-IMS2022

Value of T-2 Mapping MRI for Prostate Cancer Detection and Classification

Tom Hilbert

Background Currently, multi-parametric prostate MRI (mpMRI) consists of a qualitative T-2, diffusion weighted, and dynamic contrast enhanced imaging. Quantification of T-2 imaging might further standardize PCa detection and support artificial intelligence ...

WILEY2022

Fault Detection and Diagnosis with Imbalanced and Noisy Data: A Hybrid Framework for Rotating Machinery

Amin Kaboli

Fault diagnosis plays an essential role in reducing the maintenance costs of rotating machinery manufacturing systems. In many real applications of fault detection and diagnosis, data tend to be imbalanced, meaning that the number of samples for some fault ...

MDPI2022

PoGaIN: Poisson-Gaussian Image Noise Modeling From Paired Samples

Sabine Süsstrunk, Majed El Helou

Image noise can often be accurately fitted to a Poisson-Gaussian distribution. However, estimating the distribution parameters from a noisy image only is a challenging task. Here, we study the case when paired noisy and noise-free samples are accessible. N ...

IEEE Institute of Electrical and Electronics Engineers2022

A Statistical Test for Probabilistic Fairness

Daniel Kuhn, Viet Anh Nguyen, Bahar Taskesen

Algorithms are now routinely used to make consequential decisions that affect human lives. Examples include college admissions, medical interventions or law enforcement. While algorithms empower us to harness all information hidden in vast amounts of data, ...

2021

Locally Differentially-Private Randomized Response for Discrete Distribution Learning

Michael Christoph Gastpar, Adriano Pastore

We consider a setup in which confidential i.i.d. samples X1, . . . , Xn from an unknown finite-support distribution p are passed through n copies of a discrete privatization chan- nel (a.k.a. mechanism) producing outputs Y1, . . . , Yn. The channel law gua ...

2021

Robust Generalization despite Distribution Shift via Minimum Discriminating Information

Daniel Kuhn, Andreas Krause, Tobias Sutter

Training models that perform well under distribution shifts is a central challenge in machine learning. In this paper, we introduce a modeling framework where, in addition to training data, we have partial structural knowledge of the shifted test distribut ...

2021

Planning for a Changing Climate without Accurate Predictions

Parag Rastogi, Mohammad Emtiyaz Khan

Simulation is an informative tool to plan the design and renovation of engineering systems, but its use requires accurate inputs from the users. Inputs such as the unknown future operating conditions might be expected in a system’s lifetime, e.g., the futu ...

Taylor & Francis Group2021

Distributionally Robust Optimization with Markovian Data

Daniel Kuhn, Tobias Sutter, Mengmeng Li

We study a stochastic program where the probability distribution of the uncertain problem parameters is unknown and only indirectly observed via finitely many correlated samples generated by an unknown Markov chain with d states. We propose a data-driven d ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2021

Students' learning strategies, motivation and project-management skills during interdisciplinary projects in Covid times

Jessica Elke Dehler Zufferey, Siara Ruth Isaac, Helena Kovacs, Marc Lafuente Martinez, Julien Delisle

In this study, we investigated an interdisciplinary project-based learning program. Students were confronted with interdisciplinary challenges in the form of complex and authentic problems, like building a racing car or making a mobile laboratory for genet ...

Technische Universität Berlin2021

Built-in Self-Evaluation of First-Order Power Side-Channel Leakage for FPGAs

Francesco Regazzoni, Mirjana Stojilovic, Ognjen Glamocanin, Louis Coulon

Embedded and cyber-physical systems are pervading all aspects of our lives, including sensitive and critical ones. As a result, they are an alluring target for cyber attacks. These systems, whose implementation is often based on reconfigurable hardware, ar ...

2020

Flexible Support for Gas-Lubricated Bearing Bushings

Jürg Alexander Schiffmann

Herringbone-grooved journal bearings (HGJBs) for high-speed turbo-machinery are often supported on O-rings due to their favorable stiffness and damping characteristics for stability and alignment reasons. They yield limited lifetime at high temperatures, i ...

2020