Publications associées à Contact Tip Trajectory in Steady-State Regime Prediction Using Deep Learning for Piezoelectric Actuators

Publications associées (19)

An Exponentially Converging Particle Method for the Mixed Nash Equilibrium of Continuous Games

We consider the problem of computing mixed Nash equilibria of two-player zero-sum games with continuous sets of pure strategies and with first-order access to the payoff function. This problem arises for example in game-theory-inspired machine learning app ...

2025

Quantifying the biomimicry gap in biohybrid robot-fish pairs

Francesco Mondada, Vaios Papaspyros

Biohybrid systems in which robotic lures interact with animals have become compelling tools for probing and identifying the mechanisms underlying collective animal behavior. One key challenge lies in the transfer of social interaction models from simulatio ...

2024

Sparse Attacks for Manipulating Explanations in Deep Neural Network Models

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Michail Vlachos

We investigate methods for manipulating classifier explanations while keeping the predictions unchanged. Our focus is on using a sparse attack, which seeks to alter only a minimal number of input features. We present an efficient and novel algorithm for co ...

Ieee Computer Soc2023

Privacy-Preserving Image Acquisition for Neural Vision Systems

Pascal Frossard, Yamin Sepehri

Preserving privacy is a growing concern in our society where cameras are ubiquitous. In this work, we propose a trainable image acquisition method that removes the sensitive information in the optical domain before it reaches the image sensor. The method b ...

Ieee-Inst Electrical Electronics Engineers Inc2023

Adversarial Training Should Be Cast As a Non-Zero-Sum Game

Volkan Cevher, Seyed Hamed Hassani, Fabian Ricardo Latorre Gomez

One prominent approach toward resolving the adversarial vulnerability of deep neural networks is the two-player zero-sum paradigm of adversarial training, in which predictors are trained against adversarially-chosen perturbations of data. Despite the promi ...

2023

Bayesian Error-in-Variables Models for the Identification of Distribution Grids

Giancarlo Ferrari Trecate, Pulkit Nahata, Jean-Sébastien Hubert Brouillon

The increasing integration of renewable energy requires a good model of the existing power distribution infrastructure, represented by its admittance matrix. However, a reliable estimate may either be missing or quickly become obsolete, as distribution gri ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Natural-Looking Adversarial Examples From Freehand Sketches

Davide Nanni, Hakgu Kim

Deep neural networks (DNNs) have achieved great success in image classification and recognition compared to previous methods. However, recent works have reported that DNNs are very vulnerable to adversarial examples that are intentionally generated to misl ...

IEEE2022

Efficient Simulation for Visual Comfort Evaluations

Marilyne Andersen, Jan Wienold, Stephen William Wasilewski

This paper provides a validation of a novel sampling, storage, and evaluation method named raytraverse that can quickly and accurately compute glare and visual comfort metrics including vertical illuminance, Daylight Glare Probability (DGP), and Unified Gl ...

2022

NUQSGD: Provably Communication-efficient Data-parallel SGD via Nonuniform Quantization

Dan Alistarh

As the size and complexity of models and datasets grow, so does the need for communication-efficient variants of stochastic gradient descent that can be deployed to perform parallel model training. One popular communication-compression method for data-para ...

Microtome Publishing2021

Physico-chemical properties prediction of hydrochar in macroalgae Sargassum horneri hydrothermal carbonisation

Farzaneh Talebkeikhah, Mohsen Talebkeikhah

This study evaluates and compares several machine learning methods on the effects of different parameters in the hydrothermal carbonisation (HTC) process of macroalgae Sargassum horneri. Reaction temperature, residence time, biomass particle size, the amou ...

TAYLOR & FRANCIS LTD2021

Time-Dependent Deep Image Prior for Dynamic MRI

Michaël Unser, Harshit Gupta, Kyong Hwan Jin, Jaejun Yoo

We propose a novel unsupervised deep-learning-based algorithm for dynamic magnetic resonance imaging (MRI) reconstruction. Dynamic MRI requires rapid data acquisition for the study of moving organs such as the heart. We introduce a generalized version of t ...

Institute of Electrical and Electronics Engineers2021

AiiDAlab - an ecosystem for developing, executing, and sharing scientific workflows

Berend Smit, Nicola Marzari, Giovanni Pizzi, Christoph Leopold Talirz, Dou Du, Aliaksandr Yakutovich, Carl Simon Adorf, Casper Welzel Andersen

Cloud platforms allow users to execute tasks directly from their web browser and are a key enabling technology not only for commerce but also for computational science. Research software is often developed by scientists with limited experience in (and time ...

ELSEVIER2021

Time-Delay Encoded Image Recognition in a Network of Resistively Coupled VO2 on Si Oscillators

Kirsten Emilie Moselund

Oscillatory neural networks based on insulator to metal transition of VO2 switches are implemented for image recognition. The VO2 oscillators are fabricated on silicon in a CMOS compatible process.A fullyconnected network of coupled oscillators is investig ...

Institute of Electrical and Electronics Engineers2020

Modelling and Optimization of Demand Responsive Systems and Urban Congestion

Burak Boyaci

EPFL2014

Distributed real-time dynamic security assessment using intelligent techniques

Maher Kayal, Sidi-Rachid Cherkaoui, Theodoros Kyriakidis

In real time scenario, transmission (of power) in power systems may not be always ideal i.e. they may be interrupted. As a result, the synchronization among the power generating modules may be lost. Loss of synchronization has a direct effect on the stabil ...

IEEE2013

Comprehensive Annual Daylight Design through a Goal-Based Approach

Marilyne Andersen

Decisions affecting building form and orientation and choices regarding opening size, type and positioning have a great effect on the building’s access to daylight and are typically made during the earliest stages of architectural design. Computer simulati ...

2012

Oncilla robot: a light-weight bio-inspired quadruped robot for fast locomotion in rough terrain

Auke Ijspeert, Alexander Spröwitz, Rico Möckel, Mostafa Ajallooeian, Alexandre Tuleu

: On the hardware level, we are proposing and testing a bio-inspired quadruped robot design (Oncilla robot), based on light-weight, compliant, and three-segmented legs. Our choice of placing the compliance such that it is spanning two joints enforces a non ...

2011

Dry sliding contact between rough surfaces at the atomistic scale

Jean-François Molinari, Guillaume Anciaux, Peter Spijker

Although, a lot is known about the factors contributing to friction, a complete physical understanding of the origins of friction is still lacking. At the macroscale several laws have long since described the relation between load (Amontons, Coulomb), appa ...

Springer US2011

A generalised stochastic model for the simulation of occupant presence

Jean-Louis Scartezzini, Darren Robinson, Nicolas Morel, Jessen Page

This paper describes an algorithm for the simulation of occupant presence, to be later used as an input for future occupant behaviour models within building simulation tools. By considering occupant presence as an inhomogeneous Markov chain interrupted by ...

2008