VN-Transformer: Rotation-Equivariant Attention for Vector Neurons

Rotation equivariance is a desirable property in many practical applications such as motion forecasting and 3D perception, where it can offer benefits like sample efficiency, better generalization, and robustness to input perturbations. Vector Neurons (VN) is a recently developed framework offering a simple yet effective approach for deriving rotation-equivariant analogs of standard machine learning operations by extending one-dimensional scalar neurons to three-dimensional "vector neurons." We introduce a novel "VN-Transformer" architecture to address several shortcomings of the current VN models. Our contributions are: $(i)$ we derive a rotation-equivariant attention mechanism which eliminates the need for the heavy feature preprocessing required by the original Vector Neurons models; $(ii)$ we extend the VN framework to support non-spatial attributes, expanding the applicability of these models to real-world datasets; $(iii)$ we derive a rotation-equivariant mechanism for multi-scale reduction of point-cloud resolution, greatly speeding up inference and training; $(iv)$ we show that small tradeoffs in equivariance ($\epsilon$-approximate equivariance) can be used to obtain large improvements in numerical stability and training robustness on accelerated hardware, and we bound the propagation of equivariance violations in our models. Finally, we apply our VN-Transformer to 3D shape classification and motion forecasting with compelling results.Comment: Published in Transactions on Machine Learning Research (TMLR), 2023; Previous version appeared in Workshop on Machine Learning for Autonomous Driving, Conference on Neural Information Processing Systems (NeurIPS), 202

Saturated fatty acids synergize with elevated glucose to cause pancreatic beta-cell death

We have proposed the "glucolipotoxicity" hypothesis in which elevated free fatty acids (FFAs) together with hyperglycemia are synergistic in causing islet β-cell damage because high glucose inhibits fat oxidation and consequently lipid detoxification. T

F-18-FDG PET/CT as a central tool in the shift from chronic Q fever to Coxiella burnetii persistent focalized infection: A consecutive case series

International audienceBecause Q fever is mostly diagnosed serologically, localizing a persistent focus of Coxiella burnetii infection can be challenging. F-18-fluorodeoxyglucose positron emission tomography/computed tomography (F-18-FDG PET/CT) could be an interesting tool in this context.We performed a retrospective study on patients diagnosed with C burnetii infection, who had undergone F-18-FDG PET/CT between 2009 and 2015. When positive F-18-FDG PET/CT results were obtained, we tried to determine if it changed the previous diagnosis by discovering or confirming a suspected focus of C burnetii infection.One hundred sixty-seven patients benefited from F-18-FDG PET/CT. The most frequent clinical subgroup before F-18-FDG PET/CT was patients with no identified focus of infection, despite high IgG1 serological titers (34%). For 59% (n=99) of patients, a hypermetabolic focus was identified. For 62 patients (62.6%), the positive F-18-FDG PET/CT allowed the diagnosis to be changed. For 24 of them, (38.7%), a previously unsuspected focus of infection was discovered. Forty-two (42%) positive patients had more than 1 hypermetabolic focus. We observed 21 valvular foci, 34 vascular foci, and a high proportion of osteoarticular localizations (n=21). We also observed lymphadenitis (n=27), bone marrow hypermetabolism (n=11), and 9 pulmonary localizations.We confirmed that(18)F-FDG PET/CT is a central tool in the diagnosis of C burnetii focalized persistent infection. We proposed new diagnostic scores for 2 main clinical entities identified using F-18-FDG PET/CT: osteoarticular persistent infections and lymphadenitis