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Publications associées (31)

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An attention-based deep learning approach for the classification of subjective cognitive decline and mild cognitive impairment using resting-state EEG

Silvestro Micera

Objective. This study aims to design and implement the first deep learning (DL) model to classify subjects in the prodromic states of Alzheimer's disease (AD) based on resting-state electroencephalographic (EEG) signals. Approach. EEG recordings of 17 heal ...

IOP Publishing Ltd2023

Validation of semi-analytical, semi-empirical covariance matrices for two-point correlation function for early DESI data

Cheng Zhao

We present an extended validation of semi-analytical, semi-empirical covariance matrices for the two-point correlation function (2PCF) on simulated catalogs representative of luminous red galaxies (LRGs) data collected during the initial 2 months of operat ...

OXFORD UNIV PRESS2023

Biomass to energy: a machine learning model for optimum gasification pathways

Berend Smit, Kevin Maik Jablonka

Biomass is a highly versatile renewable resource for decarbonizing energy systems. Gasification is a promising conversion technology that can transform biomass into multiple energy carriers to produce heat, electricity, biofuels, or chemicals. At present, ...

Royal Soc Chemistry2023

Comparing the Buckling Strength of Spherical Shells With Dimpled Versus Bumpy Defects

Pedro Miguel Nunes Pereira de Almeida Reis, Arefeh Abbasi, Fani Derveni

We investigate the effect of defect geometry in dictating the sensitivity of the critical buckling conditions of spherical shells under external pressure loading. Specifically, we perform a comparative study between shells containing dimpled (inward) versu ...

ASME2023

Spatiotemporal wildfire modeling through point processes with moderate and extreme marks

Jonathan Koh Boon Han

Accurate spatiotemporal modeling of conditions leading to moderate and large wildfires provides better understanding of mechanisms driving fire-prone ecosystems and improves risk management. Here, we develop a joint model for the occurrence intensity and t ...

2023

Deep learning-based solid component measuring enabled interpretable prediction of tumor invasiveness for lung adenocarcinoma

Li Zhang, Jiancheng Yang, Xinchao Wang

Background: The nature of the solid component of subsolid nodules (SSNs) can indicate tumor pathological invasiveness. However, preoperative solid component assessment still lacks a reference standard.Methods: In this retrospective study, an AI algorithm w ...

Elsevier Ireland Ltd2023

Universal machine learning for the response of atomistic systems to external fields

Yaolong Zhang

Machine learned interatomic interaction potentials have enabled efficient and accurate molecular simulations of closed systems. However, external fields, which can greatly change the chemical structure and/or reactivity, have been seldom included in curren ...

Nature Portfolio2023

Autologous and Allogeneic Cytotherapies for Large Knee (Osteo)Chondral Defects: Manufacturing Process Benchmarking and Parallel Functional Qualification

Corinne Scaletta, Sandra Jaccoud, Philippe Abdel Sayed, Nathalie Hirt-Burri, Salim Elias Darwiche, Robin Martin

Cytotherapies are often necessary for the management of symptomatic large knee (osteo)-chondral defects. While autologous chondrocyte implantation (ACI) has been clinically used for 30 years, allogeneic cells (clinical-grade FE002 primary chondroprogenitor ...

MDPI2023

A Study on Gradient-based Meta-learning for Robust Deep Digital Twins

Olga Fink, Raffael Pascal Theiler, Michele Viscione

Deep-learning-based digital twins (DDT) are a promising tool for data-driven system health management because they can be trained directly on operational data. A major challenge for efficient training however is that industrial datasets remain unlabeled. T ...

Research Publishing2023

Confidence Matters: Applications to Semantic Segmentation

Prabhu Teja Sivaprasad

The successes of deep learning for semantic segmentation can in be, in part, attributed to its scale: a notion that encapsulates the largeness of these computational architectures and the labeled datasets they are trained on. These resource requirements hi ...

EPFL2023

Learning with tensors: a framework based on convex optimization and spectral regularization

Quoc Tran Dinh

We present a framework based on convex optimization and spectral regularization to perform learning when feature observations are multidimensional arrays (tensors). We give a mathematical characterization of spectral penalties for tensors and analyze a uni ...

Springer2014