A Key Business Node Identification Model for Internet of Things Security

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CoNIC Challenge: Pushing the frontiers of nuclear detection, segmentation, classification and counting

Martin Weigert

Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a communitywide challenge using the largest availab ...

Elsevier2023

Self-supervised learning-based cervical cytology for the triage of HPV-positive women in resource-limited settings and low-data regime

Jean-Philippe Thiran, Seyedbehzad Bozorgtabar, Thomas Grégoire Stegmüller, Christian Robert Abbet

Screening Papanicolaou test samples has proven to be highly effective in reducing cervical cancer-related mortality. However, the lack of trained cytopathologists hinders its widespread implementation in low-resource settings. Deep learning-assisted telecy ...

Pergamon-Elsevier Science Ltd2023

Latent Representation of CFD Meshes and Application to Airfoil Aerodynamics

Pascal Fua, Benoît Alain René Guillard, Zhen Wei

Mesh manipulation is central to Computational Fluid Dynamics (CFD). However, creating appropriate computational meshes often involves substantial manual intervention that has to be repeated each time the target shape changes. To address this problem, we p ...

American Institute of Aeronautics and Astronautics2023

Enhancing argumentative writing with automated feedback and social comparison nudging

Thiemo Wambsganss

The advantages offered by natural language processing (NLP) and machine learning enable students to receive automated feedback on their argumentation skills, independent of educator, time, and location. Although there is a growing amount of literature on f ...

PERGAMON-ELSEVIER SCIENCE LTD2022

Fluctuations, Bias, Variance & Ensemble of Learners: Exact Asymptotics for Convex Losses in High-Dimension

Florent Gérard Krzakala, Bruno Loureiro

From the sampling of data to the initialisation of parameters, randomness is ubiquitous in modern Machine Learning practice. Understanding the statistical fluctuations engendered by the different sources of randomness in prediction is therefore key to unde ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2022

Hessian-based toolbox for reliable and interpretable machine learning in physics

Patrick Hümbeli

Machine learning (ML) techniques applied to quantum many-body physics have emerged as a new research field. While the numerical power of this approach is undeniable, the most expressive ML algorithms, such as neural networks, are black boxes: The user does ...

2022

Multiple sclerosis cortical lesion detection with deep learning at ultra-high-field MRI

Jean-Philippe Thiran, Francesco La Rosa

Manually segmenting multiple sclerosis (MS) cortical lesions (CLs) is extremely time consuming, and past studies have shown only moderate inter-rater reliability. To accelerate this task, we developed a deep-learning-based framework (CLAIMS: Cortical Lesio ...

WILEY2022

Experimental quantum key distribution certified by Bell's theorem

Rüdiger Urbanke, Kirill Ivanov

Cryptographic key exchange protocols traditionally rely on computational conjectures such as the hardness of prime factorization(1) to provide security against eavesdropping attacks. Remarkably, quantum key distribution protocols such as the Bennett-Brassa ...

NATURE PORTFOLIO2022