Publications associées à Deploying Decentralized, Privacy-Preserving Proximity Tracing

Publications associées (6)

Finding Consensus on Trust in AI in Health Care: Recommendations From a Panel of International Experts

Marcello Ienca, Georg Michael Joachim Starke, Alberto Termine

Background: The integration of artificial intelligence (AI) into health care has become a crucial element in the digital transformation of health systems worldwide. Despite the potential benefits across diverse medical domains, a significant barrier to the ...

JMIR PUBLICATIONS, INC2025

ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classification

Jean-Philippe Thiran, Seyedbehzad Bozorgtabar, Thomas Grégoire Stegmüller, Antoine Jérôme Spahr

Progress in digital pathology is hindered by high-resolution images and the prohibitive cost of exhaustive localized annotations. The commonly used paradigm to categorize pathology images is patch-based processing, which often incorporates multiple instanc ...

IEEE COMPUTER SOC2023

A Simulation-Guided Paradigm for Logic Synthesis and Verification

Giovanni De Micheli, Heinz Riener, Siang-Yun Lee

This article proposes a new logic synthesis and verification paradigm based on circuit simulation. In this paradigm, high quality, expressive simulation patterns are pregenerated to be reused in multiple runs of optimization and verification algorithms, re ...

2022

A Lagrangian decomposition scheme for choice-based optimization

Michel Bierlaire, Meritxell Pacheco Paneque

Choice-based optimization problems are the family of optimization problems that incorporate the stochasticity of individual preferences according to discrete choice models to make planning decisions. This integration brings non-convexity and nonlinearity t ...

PERGAMON-ELSEVIER SCIENCE LTD2022

TwoResNet: Two-level resolution neural network for traffic forecasting on freeway networks

Nikolaos Geroliminis, Semin Kwak

Traffic forecasting problems of freeway networks are heavily tackled by deep learning methods because it requires learning highly complex correlations between variables both in time and space. Adopting a graph convolutional network (GCN) becomes a standard ...

IEEE2022

Editorial

Daniel Thalmann

WILEY2020