Deploying Decentralized, Privacy-Preserving Proximity Tracing

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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 ...

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 ...

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 ...

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 ...

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 ...

Deploying Decentralized, Privacy-Preserving Proximity Tracing

Chattez avec Graph Search

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

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

A Simulation-Guided Paradigm for Logic Synthesis and Verification

A Lagrangian decomposition scheme for choice-based optimization

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

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