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Publications associées (30)

Navigation Improves the Survival Rate of Mobile-Bearing Total Knee Arthroplasty by Severe Preoperative Coronal Deformity: A Propensity Matched Case-Control Comparative Study

Brigitte Jolles-Haeberli

The primary hypothesis of this study was that the survival rate over 10 years of total knee arthroplasties (TKAs) implanted with a navigation system was superior to that of TKAs implanted with a conventional technique. The secondary hypothesis was that the ...

GEORG THIEME VERLAG KG2021

Rapid Recalibration of Peri-Personal Space: Psychophysical, Electrophysiological, and Neural Network Modeling Evidence

Olaf Blanke, Bruno Herbelin, Tommaso Enrico Bertoni, Andrea Serino, Jean-Paul Noel, Elisa Pellencin

Interactions between individuals and the environment occur within the peri-personal space (PPS). The encoding of this space plastically adapts to bodily constraints and stimuli features. However, these remapping effects have not been demonstrated on an ada ...

2020

Disentangling feature and lazy training in deep neural networks

Matthieu Wyart, Mario Geiger, Stefano Spigler, Arthur Ulysse Jacot-Guillarmod, Arthur Jacot

Two distinct limits for deep learning have been derived as the network width h -> infinity, depending on how the weights of the last layer scale with h. In the neural tangent Kernel (NTK) limit, the dynamics becomes linear in the weights and is described b ...

IOP PUBLISHING LTD2020

Cognitive workload monitoring in virtual reality based rescue missions with drones

David Atienza Alonso, Adriana Arza Valdes, Fabio Isidoro Tiberio Dell'Agnola, Niloofar Momeni

The use of drones in search and rescue (SAR) missions can be very cognitively demanding. Since high levels of cognitive workload can negatively affect human performance, there is a risk of compromising the mission and leading to failure with catastrophic o ...

2020

Drynx: Decentralized, Secure, Verifiable System for Statistical Queries and Machine Learning on Distributed Datasets

Jean-Pierre Hubaux, Juan Ramón Troncoso-Pastoriza, David Jules Froelicher, Joao André Gomes de Sá e Sousa

Data sharing has become of primary importance in many domains such as big-data analytics, economics and medical research, but remains difficult to achieve when the data are sensitive. In fact, sharing personal information requires individuals' unconditiona ...

2020

Learning Activation Functions in Deep (Spline) Neural Networks

Michaël Unser, Pakshal Narendra Bohra, Shayan Aziznejad, Harshit Gupta, Joaquim Gonçalves Garcia Barreto Campos

We develop an efficient computational solution to train deep neural networks (DNN) with free-form activation functions. To make the problem well-posed, we augment the cost functional of the DNN by adding an appropriate shape regularization: the sum of the ...

2020

Assessing Urban Sustainability through Participatory Multi-Criteria Approaches (PMCAs): An Updated Comparative Analysis

Albert Merino

This chapter presents a comparative analysis of four Participatory Multi-Criteria Approaches (PMCAs) usually applied in sustainability assessments (SMCE, MCM, 3-SPM and INTEGRAAL). Such approaches are presented in detail and subsequently compared to each o ...

Cambridge University Press2020

No difference in 13-year survival after medial pivot or central pivot mobile bearing total knee arthroplasty. A propensity matched comparative analysis

Brigitte Jolles-Haeberli

Purpose The present study was designed to evaluate the long-term results (more than 10 years) of mobile bearing total knee arthroplasty (TKA) and to compare the survival of medial pivot axis (MPA) and central pivot axis (CPA) TKAs. The primary hypothesis w ...

2021

Adaptive Gradient Descent without Descent

Konstantin Mishchenko

We present a strikingly simple proof that two rules are sufficient to automate gradient descent: 1) don’t increase the stepsize too fast and 2) don’t overstep the local curvature. No need for functional values, no line search, no information about the func ...

2020

Artificial neural networks can be used for Ambrosia pollen emission parameterization in COSMO-ART

Charlotte Marie Bürki

The emission parameterization is a crucial part of numerical pollen dispersion models. This paper shows that Artificial Neural Networks (ANNs) can substantially improve the performance of the Ambrosia pollen emission in numerical pollen dispersion models s ...

Elsevier2019

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