Publications related to Incentive Mechanism in the Sponsored Content Market With Network Effects

Polynomial-time universality and limitations of deep learning

The goal of this paper is to characterize function distributions that general neural networks trained by descent algorithms (GD/SGD), can or cannot learn in polytime. The results are: (1) The paradigm of general neural networks trained by SGD is poly-time ...

WILEY2023

Euclid preparation: XXVIII. Forecasts for ten different higher-order weak lensing statistics

Frédéric Courbin, Gianluca Castignani, Jean-Luc Starck, Austin Chandler Peel, Maurizio Martinelli, Yi Wang, Richard Massey, Fabio Finelli, Marcello Farina

Recent cosmic shear studies have shown that higher-order statistics (HOS) developed by independent teams now outperform standard two-point estimators in terms of statistical precision thanks to their sensitivity to the non-Gaussian features of large-scale ...

EDP SCIENCES S A2023

CONVERGENCE AND NONCONVERGENCE OF SCALED SELF-INTERACTING RANDOM WALKS TO BROWNIAN MOTION PERTURBED AT EXTREMA

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We use generalized Ray-Knight theorems, introduced by B. Toth in 1996, together with techniques developed for excited random walks as main tools for establishing positive and negative results concerning convergence of some classes of diffusively scaled sel ...

Cleveland2023

A birth-death-migration model for life in astrophysical environments

Claudio Grimaldi

To assess the number of life-bearing worlds in astrophysical environments, it is necessary to take the intertwined processes of abiogenesis (birth), extinction (death), and transfer of life (migration) into account. We construct a mathematical model that i ...

OXFORD UNIV PRESS2021

Moment approach of the multi-species non-linear Coulomb collision operator adapted to particle-in-cell codes

Laurent Villard, Stephan Brunner, Emmanuel Lanti, Noé Thomas Elie Ohana, Claudio Gheller, Moahan Murugappan

In this article an approximated version of the multi-species, non-linear Coulomb collision operator is derived via the use of a truncated moment expansion of the distribution function to compute the Rosenbluth potentials. The evolution of the distribution ...

IOP PUBLISHING LTD2021

Designing Gamified Activities with Haptic-Enabled Tangible Robots for Therapy and Assistance

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In this thesis, we contribute to the field of rehabilitation robotics by designing haptic-enabledtangible robot-based activities and exploring their added value for therapy and assistance.The research specifically focuses on the design and development of g ...

EPFL2021

Fractional extreme distributions

Thomas Simon, Lotfi Boudabsa

We consider three classes of linear differential equations on distribution functions, with a fractional order alpha is an element of [0; 1]. The integer case alpha = 1 corresponds to the three classical extreme families. In general, we show that there is a ...

2020

Non-perturbative probability distribution function for cosmological counts in cells

Sergey Sibiryakov, Mikhail Ivanov

We present a non-perturbative calculation of the 1-point probability distribution function (PDF) for the spherically-averaged matter density field. The PDF is represented as a path integral and is evaluated using the saddle-point method. It factorizes into ...

IOP PUBLISHING LTD2019

The stochastic heat equation: hitting probabilities and the probability density function of the supremum via Malliavin calculus

Fei Pu

In this thesis, we study systems of linear and/or non-linear stochastic heat equations and fractional heat equations in spatial dimension

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driven by space-time white noise. The main topic is the study of hitting probabilities for the solutions to these ...

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Privacy in Recommender Systems

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The upsurge in the number of web users over the last two decades has resulted in a significant growth of online information. Recommenders are machine learning approach and are becoming one of the main ways to navigate the Internet. They recommend appropria ...

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Incentive Mechanism in the Sponsored Content Market With Network Effects

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Polynomial-time universality and limitations of deep learning

Euclid preparation: XXVIII. Forecasts for ten different higher-order weak lensing statistics

CONVERGENCE AND NONCONVERGENCE OF SCALED SELF-INTERACTING RANDOM WALKS TO BROWNIAN MOTION PERTURBED AT EXTREMA

A birth-death-migration model for life in astrophysical environments

Moment approach of the multi-species non-linear Coulomb collision operator adapted to particle-in-cell codes

Designing Gamified Activities with Haptic-Enabled Tangible Robots for Therapy and Assistance

Fractional extreme distributions

Non-perturbative probability distribution function for cosmological counts in cells

The stochastic heat equation: hitting probabilities and the probability density function of the supremum via Malliavin calculus

Privacy in Recommender Systems

A birth-death-migration model for life in astrophysical environments

Designing Gamified Activities with Haptic-Enabled Tangible Robots for Therapy and Assistance

Euclid preparation: XXVIII. Forecasts for ten different higher-order weak lensing statistics

CONVERGENCE AND NONCONVERGENCE OF SCALED SELF-INTERACTING RANDOM WALKS TO BROWNIAN MOTION PERTURBED AT EXTREMA

Moment approach of the multi-species non-linear Coulomb collision operator adapted to particle-in-cell codes

Polynomial-time universality and limitations of deep learning

Fractional extreme distributions

Non-perturbative probability distribution function for cosmological counts in cells

The stochastic heat equation: hitting probabilities and the probability density function of the supremum via Malliavin calculus

Privacy in Recommender Systems