Array programming provides a powerful, compact and expressive syntax for accessing,
manipulating and operating on data in vectors, matrices and higher-dimensional arrays …

Unsupervised learning is becoming an essential tool to analyze the increasingly large
amounts of data produced by atomistic and molecular simulations, in material science, solid …

Pretrained neural network models for biological segmentation can provide good out-of-the-
box results for many image types. However, such models do not allow users to adapt the …

The NANOGrav 15 yr data set shows evidence for the presence of a low-frequency
gravitational-wave background (GWB). While many physical processes can source such low …

Few-shot in-context learning (ICL) enables pre-trained language models to perform a
previously-unseen task without any gradient-based training by feeding a small number of …

We introduce a new paradigm for generative modeling built on Continuous Normalizing
Flows (CNFs), allowing us to train CNFs at unprecedented scale. Specifically, we present …

This paper documents the seventeenth data release (DR17) from the Sloan Digital Sky
Surveys; the fifth and final release from the fourth phase (SDSS-IV). DR17 contains the …

In this paper we provide a first physical interpretation for the Event Horizon Telescopeʼs
(EHT) 2017 observations of Sgr A*. Our main approach is to compare resolved EHT data at …

We update the capabilities of the open-knowledge software instrument Modules for
Experiments in Stellar Astrophysics (MESA). The new auto _ diff module implements …

Mapping behavioural actions to neural activity is a fundamental goal of neuroscience. As our
ability to record large neural and behavioural data increases, there is growing interest in …