Publications associées à Perception du risque

On the symmetries in the dynamics of wide two-layer neural networks

We consider the idealized setting of gradient flow on the population risk for infinitely wide two-layer ReLU neural networks (without bias), and study the effect of symmetries on the learned parameters and predictors. We first describe a general class of s ...

AMER INST MATHEMATICAL SCIENCES-AIMS2023

Technology Strategy in Dynamic Environments: A Computational Analysis of the Automation of Routines, the Organization of AI, and the Evaluation of Technology Risk

Maximilian Wieland Hofer

In this dissertation, I develop theory and evidence to argue that new technologies are central to how firms organize to create and capture value. I use computational methods such as reinforcement learning and probabilistic topic modeling to investigate thr ...

EPFL2022

Specchio-COVID19 cohort study: a longitudinal follow-up of SARS-CoV-2 serosurvey participants in the canton of Geneva, Switzerland

Stéphane Joost, Idris Guessous

Introduction The COVID-19 pandemic has affected billions of people around the world both directly through the infection itself and indirectly through its economic, social and sanitary impact. Collecting data over time is essential for the understanding of ...

BMJ PUBLISHING GROUP2022

Corrosion testing of solar cells: Wear-out degradation behavior

Christophe Ballif, Alessandro Francesco Aldo Virtuani, Luca Gnocchi, Andrew Wayne Fairbrother

Corrosion is one of the main end-of-life degradation and failure modes in photovoltaic (PV) modules. However, it is a gradual process and can take many years to become a major risk factor because of the slow accumulation of water and acetic acid (from enca ...

ELSEVIER2022

Identifying and Characterizing Trans Women in the Swiss HIV Cohort Study as an Epidemiologically Distinct Risk Group

Jacques Fellay

Background As trans women are disproportionately affected by the HIV epidemic, and are still understudied, we aimed to identify and characterize the trans women in the Swiss HIV Cohort Study (SHCS). Methods A combination of criteria from pre-existing cohor ...

OXFORD UNIV PRESS INC2022

Who Is Listening? Spokesperson Effect on Communicating Social and Physical Distancing Measures During the COVID-19 Pandemic

Robert West, Andreas Oliver Spitz, Ahmad Abu-Akel

Effective communication during a pandemic, such as the current COVID-19 crisis, can save lives. At the present time, social and physical distancing measures are the lead strategy in combating the spread of COVID-19. In this study, a survey was administered ...

Frontiers Media2021

Developing a UV climatology for public health purposes using satellite data

Athanasios Nenes, David Vernez

The effects of solar ultraviolet (UV) radiation on life on Earth differ greatly. While overexposure to UV rays is harmful, small amounts of exposure are necessary for the synthesis of Vitamin D and good health. To optimize individual exposure to solar UV, ...

2021

Estimating and predicting snakebite risk in the Terai region of Nepal through a high-resolution geospatial and One Health approach

Stéphane Joost

Most efforts to understand snakebite burden in Nepal have been localized to relatively small areas and focused on humans through epidemiological studies. We present the outcomes of a geospatial analysis of the factors influencing snakebite risk in humans a ...

Nature Portfolio2021

Robot-induced hallucinations in Parkinson’s disease depend on altered sensorimotor processing in fronto-temporal network

Dimitri Nestor Alice Van De Ville, Olaf Blanke, Fosco Bernasconi, Jevita Potheegadoo, Nathan Quentin Faivre, Giulio Rognini, Masayuki Hara, Michela Bassolino, Michel Akselrod, Eva Blondiaux

Hallucinations in Parkinson’s disease (PD) are disturbing and frequent non-motor symptoms and constitute a major risk factor for psychosis and dementia. We report a robotics-based approach applying conflicting sensorimotor stimulation, enabling the inducti ...

2021

A Pareto Dominance Principle for Data-Driven Optimization

Daniel Kuhn, Tobias Sutter

We propose a statistically optimal approach to construct data-driven decisions for stochastic optimization problems. Fundamentally, a data-driven decision is simply a function that maps the available training data to a feasible action. It can always be exp ...

2024