By combining metal nodes with organic linkers we can potentially synthesize millions of
possible metal–organic frameworks (MOFs). The fact that we have so many materials opens …

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 …

Accurate segmentation of topological tubular structures, such as blood vessels and roads, is
crucial in various fields, ensuring accuracy and efficiency in downstream tasks. However …

The medial entorhinal cortex is part of a neural system for mapping the position of an
individual within a physical environment. Grid cells, a key component of this system, fire in a …

Topological data analysis refers to approaches for systematically and reliably computing
abstract 'shapes' of complex data sets. There are various applications of topological data …

We introduce giotto-tda, a Python library that integrates high-performance topological data
analysis with machine learning via a scikit-learn-compatible API and state-of-the-art C++ …

Abstract Echo State Networks (ESNs) are a class of single-layer recurrent neural networks
that have enjoyed recent attention. In this paper we prove that a suitable ESN, trained on a …

We revisit the classic database of weighted-P 4 s which admit Calabi-Yau 3-fold
hypersurfaces equipped with a diverse set of tools from the machine-learning toolbox …

A number of machine learning methods have been recently proposed to circumvent the high
computational cost of the gradient-based topology optimization solvers. By and large, these …

Accurate theoretical predictions of desired properties of materials play an important role in
materials research and development. Machine learning (ML) can accelerate the materials …