Publications related to Physiological Characterization of Need for Assistance in Rescue Missions with Drones

RMSSD Is More Sensitive to Artifacts Than Frequency-Domain Parameters: Implication in Athletes? Monitoring

Jean-Marc Vesin, Grégoire Millet, Sasan Yazdani

Easy-to-use and accurate heart rate variability (HRV) assessments are essential in athletes??? follow-up, but artifacts may lead to erroneous analysis. Artifact detection and correction are the purpose of extensive literature and implemented in dedicated a ...

JOURNAL SPORTS SCIENCE & MEDICINE2022

Examples of normal, not geometrically normal, projective Gorenstein del Pezzo surfaces with at most Du Val singularities

Ursina Schweizer

While over fields of characteristic at least 5, a normal, projective and Gorenstein del Pezzo surface is geometrically normal, this does not hold for characteristic 2 and 3. There is no characterization of all such non-geometrically normal surfaces, but th ...

EPFL2021

Visualizing the multifractal wave functions of a disordered two-dimensional electron gas

Klaus Kern

The wave functions of a disordered two-dimensional electron gas at the quantum-critical Anderson transition are predicted to exhibit multifractal scaling in their real space amplitude. We experimentally investigate the appearance of these characteristics i ...

AMER PHYSICAL SOC2021

Effects of spanwise confinement on stratified shear instabilities

François Gallaire, Yves-Marie François Ducimetière

We consider the influence of transverse confinement on the instability properties of velocity and density distributions reminiscent of those pertaining to exchange flows in stratified inclined ducts, such as the recent experiment of Lefauve et al. [J. Flui ...

2021

Distributionally Robust Inverse Covariance Estimation: The Wasserstein Shrinkage Estimator

Peyman Mohajerin Esfahani, Viet Anh Nguyen, Daniel Kuhn

We introduce a distributionally robust maximum likelihood estimation model with a Wasserstein ambiguity set to infer the inverse covariance matrix of a p-dimensional Gaussian random vector from n independent samples. The proposed model minimizes the worst ...

2022

Wasserstein Distributionally Robust Learning

Soroosh Shafieezadeh Abadeh

Many decision problems in science, engineering, and economics are affected by uncertainty, which is typically modeled by a random variable governed by an unknown probability distribution. For many practical applications, the probability distribution is onl ...

EPFL2020

Integrating the Finite Element Method with a Data-Driven Approach for Dam Displacement Prediction

Zhenzhu Meng, Chenfei Shao, Yating Hu

Both numerical simulations and data-driven methods have been applied in dam's displacement modeling. For monitored displacement data-driven methods, the physical mechanism and structural correlations were rarely discussed. In order to take the spatial and ...

2020

Hydraulic Transport Through Calcite Bearing Faults With Customized Roughness: Effects of Normal and Shear Loading

Marie Estelle Solange Violay, Mateo Alejandro Acosta

Understanding fluid flow in rough fractures is of high importance to large scale geologic processes and to most anthropogenic geo‐energy activities. Here we conducted fluid transport experiments on Carrara marble fractures with a novel customized surface t ...

2020

Global turbulence features across marginality and non-local pedestal-core interactions

Stephan Brunner, Olivier Sauter, Laurent Villard, Alberto Bottino, Ben McMillan, Emmanuel Lanti, Noé Thomas Elie Ohana

Spatially non-local aspects of turbulent transport in tokamak plasmas are examined with global gyrokinetic simulations using the ORB5 code. Inspired by very accurate measurements in the TCV tokamak in L-mode, we initialise plasma profiles with constant log ...

2019

Concrete Dam Displacement Prediction Based on an ISODATA-GMM Clustering and Random Coefficient Model

Zhenzhu Meng, Chenfei Shao, Yating Hu

Displacement data modelling is of great importance for the safety control of concrete dams. The commonly used artificial intelligence method modelled the displacement data at each monitoring point individually, i.e., the data correlations between the monit ...

2019

Asian option pricing with orthogonal polynomials

Sander Félix M Willems

In this paper we derive a series expansion for the price of a continuously sampled arithmetic Asian option in the Black-Scholes setting. The expansion is based on polynomials that are orthogonal with respect to the log-normal distribution. All terms in the ...

ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD2019

Particle number control for direct simulation Monte-Carlo methodology using kernel estimates

Mohammadhossein Gorji

The efficiency of stochastic particle schemes for large scale simulations relies on the ability to preserve a uniform distribution of particles in the whole physical domain. While simple particle split and merge algorithms have been considered previously, ...

2019

Initial port integration concept for EC and NB systems in EU DEMO tokamak

Minh Quang Tran

The integration of the heating and current drive (HCD) systems in the EU DEMO tokamak must address a number of issues, namely space constraints in the tokamak building, remote handling requirements, breeding blanket penetration, neutron and photon radiatio ...

2019

Pricing interest rate, dividend, and equity risk

Sander Félix M Willems

This thesis studies the valuation and hedging of financial derivatives, which is fundamental for trading and risk-management operations in financial institutions. The three chapters in this thesis deal with derivatives whose payoffs are linked to interest ...

EPFL2019

Wasserstein Distributionally Robust Kalman Filtering

Peyman Mohajerin Esfahani, Viet Anh Nguyen, Daniel Kuhn, Soroosh Shafieezadeh Abadeh

We study a distributionally robust mean square error estimation problem over a nonconvex Wasserstein ambiguity set containing only normal distributions. We show that the optimal estimator and the least favorable distribution form a Nash equilibrium. Despit ...

2018

High-dimensional peaks-over-threshold inference

Anthony Christopher Davison, Raphaël Gérard Théodore Michel Marie de Deloÿe et Fourcade de Fondeville

Max-stable processes are increasingly widely used for modelling complex extreme events, but existing fitting methods are computationally demanding, limiting applications to a few dozen variables. r-Pareto processes are mathematically simpler and have the p ...

2018

An Application of Extreme Value Theory to Learning Analytics: Predicting Collaboration Outcome from Eye-tracking Data

Valérie Chavez, Kshitij Sharma

The statistics used in education research are based on central trends such as the mean or standard deviation, discarding outliers. This paper adopts another viewpoint that has emerged in Statistics, called the Extreme Value Theory (EVT). EVT claims that th ...

2017

Quasi-random numbers for copula models

Mathieu Jacques David Cambou

The present work addresses the question how sampling algorithms for commonly applied copula models can be adapted to account for quasi-random numbers. Besides sampling methods such as the conditional distribution method (based on a one-to-one transformatio ...

Springer2017

A characterization of the normal distribution using stationary max-stable processes

Sebastian Engelke

Consider a max-stable process of the form , , where are points of the Poisson process with intensity u (-2)du on (0,a), X (i) , , are independent copies of a random d-variate vector X (that are independent of the Poisson process), and is a function. We sho ...

Springer Verlag2016

Microtubule-Binding R3 Fragment from Tau Self-Assembles into Giant Multistranded Amyloid Ribbons

Raffaele Mezzenga, Hilal Lashuel, Nadine Aït Bouziad

Tau protein and its fragments self-assemble into amyloid fibrils in the presence of polyanions, such as heparin. By combining microscopy, scattering, and spectroscopy techniques, we studied the aggregation of the 26-mer Tau-derived peptide alone, Tau306-32 ...

Wiley-VCH Verlag GmbH2016