Protecting Circuits From Computationally Bounded And Noisy Leakage

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Memory of Motion for Initializing Optimization in Robotics

Teguh Santoso Lembono

Many robotics problems are formulated as optimization problems. However, most optimization solvers in robotics are locally optimal and the performance depends a lot on the initial guess. For challenging problems, the solver will often get stuck at poor loc ...

EPFL2022

On Succinct Non-interactive Arguments in Relativized Worlds

Alessandro Chiesa

Succinct non-interactive arguments of knowledge (SNARKs) are cryptographic proofs with strong efficiency properties. Applications of SNARKs often involve proving computations that include the SNARK verifier, a technique called recursive composition. Unfort ...

SPRINGER INTERNATIONAL PUBLISHING AG2022

A Prony-based approach for accelerating the lightning electromagnetic fields computation above a perfectly conducting ground

Farhad Rachidi-Haeri, Marcos Rubinstein

Lightning strikes can seriously affect the reliability and availability of electrical infrastructures. The computation of lightning-electromagnetic fields represents one of the most crucial steps in the evaluation of the induced effects; thus, having at di ...

Elsevier2022

cRacklet: a spectral boundary integral method library for interfacial rupture simulation

Jean-François Molinari, Fabian Barras, Thibault Didier Roch

The study of dynamically propagating rupture along interfaces is of prime importance in various fields and system sizes, including tribology (nm to m), engineering (mm to m) and geophysics (m to km) (Armstrong-Hélouvry et al., 1994; Ben-Zion, 2008; Vanossi ...

2022

Distribution-on-distribution regression via optimal transport maps

Victor Panaretos, Laya Ghodrati

We present a framework for performing regression when both covariate and response are probability distributions on a compact interval. Our regression model is based on the theory of optimal transportation, and links the conditional Frechet mean of the resp ...

OXFORD UNIV PRESS2022

Unbiased Monte Carlo cluster updates with autoregressive neural networks

Giuseppe Carleo, Riccardo Rossi, Dian Wu

Efficient sampling of complex high-dimensional probability distributions is a central task in computational science. Machine learning methods like autoregressive neural networks, used with Markov chain Monte Carlo sampling, provide good approximations to s ...

AMER PHYSICAL SOC2021

Computation Of A 30750-Bit Binary Field Discrete Logarithm

Arjen Lenstra, Thorsten Kleinjung, Robert Granger, Benjamin Pierre Charles Wesolowski

This paper reports on the computation of a discrete logarithm in the finite field F-230750, breaking by a large margin the previous record, which was set in January 2014 by a computation in F-29234. The present computation made essential use of the elimina ...

AMER MATHEMATICAL SOC2021

Over-the-Air Computation via Reconfigurable Intelligent Surface

Wei Chen, Yuning Jiang

Over-the-air computation (AirComp) is a disruptive technique for fast wireless data aggregation in Internet of Things (IoT) networks via exploiting the waveform superposition property of multiple-access channels. However, the performance of AirComp is bott ...

2021

AKSEL: Fast Byzantine SGD

Rachid Guerraoui, El Mahdi El Mhamdi, Alexandre David Olivier Maurer, Sébastien Louis Alexandre Rouault

Modern machine learning architectures distinguish servers and workers. Typically, a d-dimensional model is hosted by a server and trained by n workers, using a distributed stochastic gradient descent (SGD) optimization scheme. At each SGD step, the goal is ...

Schloss Dagstuhl--Leibniz-Zentrum für Informatik2021