E(n) Equivariant Graph Neural Networks

This paper introduces a new model to learn graph neural networks equivariant to rotations, translations, reflections and permutations called E(n)-Equivariant Graph Neural Networks (EGNNs). In contrast with existing methods, our work does not require computationally expensive higher-order representations in intermediate layers while it still achieves competitive or better performance. In addition, whereas existing methods are limited to equivariance on 3 dimensional spaces, our model is easily scaled to higher-dimensional spaces. We demonstrate the effectiveness of our method on dynamical systems modelling, representation learning in graph autoencoders and predicting molecular properties

Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions

Generative flows and diffusion models have been predominantly trained on ordinal data, for example natural images. This paper introduces two extensions of flows and diffusion for categorical data such as language or image segmentation: Argmax Flows and Multinomial Diffusion. Argmax Flows are defined by a composition of a continuous distribution (such as a normalizing flow), and an argmax function. To optimize this model, we learn a probabilistic inverse for the argmax that lifts the categorical data to a continuous space. Multinomial Diffusion gradually adds categorical noise in a diffusion process, for which the generative denoising process is learned. We demonstrate that our method outperforms existing dequantization approaches on text modelling and modelling on image segmentation maps in log-likelihood.Comment: Accepted at Neural Information Processing Systems (NeurIPS 2021

Myosin II synergizes with F-actin to promote DNGR-1-dependent cross-presentation of dead cell-associated antigens

Conventional type 1 DCs (cDC1s) excel at cross-presentation of dead cell-associated antigens partly because they express DNGR-1, a receptor that recognizes exposed actin filaments on dead cells. In vitro polymerized F-actin can be used as a synthetic ligand for DNGR-1. However, cellular F-actin is decorated with actin-binding proteins, which could affect DNGR-1 recognition. Here, we demonstrate that myosin II, an F-actin-associated motor protein, greatly potentiates the binding of DNGR-1 to F-actin. Latex beads coated with F-actin and myosin II are taken up by DNGR-1+ cDC1s, and antigen associated with those beads is efficiently cross-presented to CD8+ T cells. Myosin II-deficient necrotic cells are impaired in their ability to stimulate DNGR-1 or to serve as substrates for cDC1 cross-presentation to CD8+ T cells. These results provide insights into the nature of the DNGR-1 ligand and have implications for understanding immune responses to cell-associated antigens and for vaccine design