Distributionally Robust Mechanism Design

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Feature distribution learning by passive exposure

Humans can rapidly estimate the statistical properties of groups of stimuli, including their average and variability. But recent studies of so-called Feature Distribution Learning (FDL) have shown that observers can quickly learn even more complex aspects ...

2022

Assessment of the Lightning Performance of overhead distribution lines based on Lightning Location Systems data

Farhad Rachidi-Haeri

This article presents the Lightning Performance (LP) assessment of a realistic portion of the Italian distribution network with the use of probability distributions for lightning parameters inferred from local data recorded by a Lightning Location System ( ...

2022

Regularized Diffusion Adaptation via Conjugate Smoothing

Ali H. Sayed, Stefan Vlaski

The purpose of this article is to develop and study a decentralized strategy for Pareto optimization of an aggregate cost consisting of regularized risks. Each risk is modeled as the expectation of some loss function with unknown probability distribution, ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Bias Cancellation of MixColumns

Francesco Regazzoni, Subhadeep Banik, Andrea Felice Caforio

At COSADE'2020, Carre et al. established a novel biascancelling property of the AES MixColumns matrix that effectively corrects any skewed output distribution of a state byte due to a faulty substitution box. Consequently, any effected byte is rendered uni ...

SPRINGER INTERNATIONAL PUBLISHING AG2022

Capturing trade-offs between daily scheduling choices

Michel Bierlaire, Timothy Michael Hillel, Janody Pougala

We propose a new modelling approach for daily activity scheduling which integrates the different daily scheduling choice dimensions (activity participation, location, schedule, duration and transportation mode) into a single optimisation problem. The funda ...

ELSEVIER SCI LTD2022

System Support for Robust Distributed Learning

Arsany Hany Abdelmessih Guirguis

Machine learning (ML) applications are ubiquitous. They run in different environments such as datacenters, the cloud, and even on edge devices. Despite where they run, distributing ML training seems the only way to attain scalable, high-quality learning. B ...

EPFL2022

Impact of the plasma operation on the technical requirements in EU-DEMO

Olivier Sauter, Minh Quang Tran, Emiliano Fable, Simon Van Mulders, Hartmut Zohm

The plasma design point during stationary phase operation of EU-DEMO has been described extensively in the past, and the assumptions employed for its definition have been supported by various investigations. However, a stationary snapshot is by far not suf ...

2022

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