23:T44f,We present Dictionary Fields, a novel neural representation which decomposes a signal into a product of factors, each represented by a classical or neural field representation, operating on transformed input coordinates. More specifically, we factorize a signal into a coefficient field and a basis field, and exploit periodic coordinate transformations to apply the same basis functions across multiple locations and scales. Our experiments show that Dictionary Fields lead to improvements in approximation quality, compactness, and training time when compared to previous fast reconstruction methods. Experimentally, our representation achieves better image approximation quality on 2D image regression tasks, higher geometric quality when reconstructing 3D signed distance fields, and higher compactness for radiance field reconstruction tasks. Furthermore, Dictionary Fields enable generalization to unseen images/3D scenes by sharing bases across signals during training which greatly benefits use cases such as image regression from partial observations and few-shot radiance field reconstruction.24:T45a,Abstract The number of publications in biomedicine and life sciences has rapidly grown over the last decades, with over 1.5 million papers now being published every year. This makes it difficult to keep track of new scientific works and to have an overview of the evolution of the field as a whole. Here we present a 2D map of the entire corpus of biomedical literature, and argue that it provides a unique and useful overview of the life sciences research. We based our atlas on the abstract texts of 21 million English articles from the PubMed database. To embed the abstracts into 2D, we used the large language model PubMedBERT, combined with t -SNE tailored to handle samples of our size. We used our atlas to study the emergence of the Covid-19 literature, the evolution of the neuroscience discipline, the uptake of machine learning, the distribution of gender imbalance in academic authorship, and the distribution of retracted paper mill articles. Furthermore, we present an interactive web version of our atlas that allows easy exploration and will enable further insights and facilitate future research.25:T4e3,The body of an animal influences how its nervous system generates behaviour¹. Accurately modelling the neural control of sensorimotor behaviour requires an anatomically detailed biomechanical representation of the body. Here we introduce a whole-body model of the fruit fly Drosophila melanogaster in a physics simulator². Designed as a general-purpose framework, our model enables the simulation of diverse fly behaviours, including both terrestrial and aerial locomotion. We validate its versatility by replicating realistic walking and flight behaviours. To support these behaviours, we develop phenomenological models for fluid and adhesion forces. Using data-driven, end-to-end reinforcement learning^3,4, we train neural network controllers capable of generating naturalistic locomotion^5-7 along complex trajectories in response to high-level steering commands. Furthermore, we show the use of visual sensors and hierarchical motor control⁸, training a high-level controller to reuse a pretrained low-level flight controller to perform visually guided flight tasks. Our model serves as an open-source platform for studying the neural control of sensorimotor behaviour in an embodied context.

#	Concept	H-Index	Publications	Citations
1	Engineering	5	6	429
2	Medicine	2	3	73
3	Language Studies	1	1	17
4	Natural Sciences	1	2	204
5	Public Health	1	1	17

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