Publications associées à Privacy-Preserving Image Acquisition for Neural Vision Systems

Publications associées (28)

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

The Dawn of High-throughput and Genome-scale Kinetic Modeling: Recent Advances and Future Directions

Vassily Hatzimanikatis, Ljubisa Miskovic, Subham Choudhury, Ilias Toumpe

Researchers have invested much effort into developing kinetic models due to their ability to capture dynamic behaviors, transient states, and regulatory mechanisms of metabolism, providing a detailed and realistic representation of cellular processes. Hist ...

AMER CHEMICAL SOC2025

Transfer learning and augmented data-driven parameter prediction for robotic welding

Sichao Liu

Robotic welding envisioned for the future of factories will promote high-demanding and customised tasks with overall higher productivity and quality. Within the context, robotic welding parameter prediction is essential for maintaining high standards of qu ...

PERGAMON-ELSEVIER SCIENCE LTD2025

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

Benchmarking conventional and learning-based objective image quality metrics on compression artifacts

Touradj Ebrahimi, Michela Testolina

Evaluating the visual quality of distorted images is a crucial task in the field of image compression, as artifacts may significantly impair the appeal and fidelity of images, therefore reducing the user experience. As assessing the visual quality of image ...

2024

Adaptive Feedforward Super-Twisting Sliding Mode Control of Parallel Kinematic Manipulators With Real-Time Experiments

Mohamed Bouri

In this paper, we propose a novel adaptive feedforward super-twisting sliding mode control algorithm to resolve the tracking control problem of parallel manipulators. The proposed control scheme includes three main terms, (i) the standard super-twisting al ...

Institute of Electrical and Electronics Engineers Inc.2024

MapPool -Bubbling up an extremely large corpus of maps for AI

Raimund Thies Schnürer

MapPool is a dataset of 75 million potential maps and textual captions. It has been derived from CommonPool, a dataset consisting of 12 billion text-image pairs from the Internet. The images have been encoded by a vision transformer and classified into map ...

2024

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

Yves Perriard, Paolo Germano, Marjan Ghorbani, Xinxin Liao, Marc Michel Charles Favier

Including friction modeling in simulation can be challenging and requires time domain simulation, which is time consuming. We propose to separate the friction modelling from the FEA (Finite Element Analysis). The approach is defined for UCM (Ultrasonic Cer ...

Institute of Electrical and Electronics Engineers Inc.2024

A neurometabolic mechanism involving dmPFC/dACC lactate in physical effort-based decision-making

Maria del Carmen Sandi Perez, Lijing Xin, Arthur Barakat, Nicolas Clairis

Motivation levels vary across individuals, yet the underlying mechanisms driving these differences remain elusive. The dorsomedial prefrontal cortex/dorsal anterior cingulate cortex (dmPFC/dACC) and the anterior insula (aIns) play crucial roles in effort-b ...

Springer Science and Business Media LLC2024

Brain and grammar: revealing electrophysiological basic structures with competing statistical models

Silvestro Micera

Acoustic, lexical, and syntactic information are simultaneously processed in the brain requiring complex strategies to distinguish their electrophysiological activity. Capitalizing on previous works that factor out acoustic information, we could concentrat ...

OXFORD UNIV PRESS INC2024

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

Exploring Novel Modalities for Optical Diffraction Tomography

Amirhossein Saba Shirvan

In the past decade, optical diffraction tomography has gained a lot of attention for its ability to create label-free three-dimensional (3D) images of the refractive index distribution of biological samples using scattered fields measured through holograph ...

EPFL2023

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

Machine Learning Security in Industry: A Quantitative Survey

Kathrin Grosse

Despite the large body of academic work on machine learning security, little is known about the occurrence of attacks on machine learning systems in the wild. In this paper, we report on a quantitative study with 139 industrial practitioners. We analyze at ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

NeuroAI: If grid cells are the answer, is path integration the question?

Alexander Mathis, Mackenzie Mathis, Markus Frey

Spatially modulated neurons known as grid cells are thought to play an important role in spatial cognition. A new study has found that units with grid-cell-like properties can emerge within artificial neural networks trained to path integrate, and develope ...

CELL PRESS2023

Your Email Address Holds the Key: Understanding the Connection Between Email and Password Security with Deep Learning

Dario Pasquini

In this work, we investigate the effectiveness of deep-learning-based password guessing models for targeted attacks on human-chosen passwords. In recent years, service providers have increased the level of security of users' passwords. This is done by requ ...

IEEE COMPUTER SOC2023

An artificial neural network approach to bifurcating phenomena in computational fluid dynamics

Jan Sickmann Hesthaven, Federico Pichi

This work deals with the investigation of bifurcating fluid phenomena using a reduced order modelling setting aided by artificial neural networks. We discuss the POD-NN approach dealing with non-smooth solutions set of nonlinear parametrized PDEs. Thus, we ...

PERGAMON-ELSEVIER SCIENCE LTD2023

Partial Information Sharing Over Social Learning Networks

Ali H. Sayed, Virginia Bordignon

This work addresses the problem of sharing partial information within social learning strategies. In social learning, agents solve a distributed multiple hypothesis testing problem by performing two operations at each instant: first, agents incorporate inf ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Visual extensions and anomaly detection in the pNEUMA experiment with a swarm of drones

Nikolaos Geroliminis, Emmanouil Barmpounakis, Sohyeong Kim, Georgios Anagnostopoulos

The usage of Unmanned Aerial Systems (UAS) in traffic monitoring has advantages such as broader vision, flexibility, privacy, and cost-efficiency compared to other traffic monitoring sensors like loop detectors or fixed surveillance cameras. UAS has been i ...

PERGAMON-ELSEVIER SCIENCE LTD2023

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