As basic research, it has also received increasing attention from people that the “curse of
dimensionality” will lead to increase the cost of data storage and computing; it also …

In recent years, we have witnessed a significant shift toward ever-more complex and ever-
larger-scale systems in the majority of the grand societal challenges tackled in applied …

Modern machine learning has subverted and bypassed the theoretical framework of
Chomsky's generative approach to linguistics, including its core claims to particular insights …

Climate change interacts with local processes to threaten biodiversity by disrupting the
complex network of ecological interactions. While changes in network interactions drastically …

We develop an auto-reservoir computing framework, Auto-Reservoir Neural Network
(ARNN), to efficiently and accurately make multi-step-ahead predictions based on a short …

Natural systems are often complex and dynamic (ie nonlinear), making them difficult to
understand using linear statistical approaches. Linear approaches are fundamentally based …

The detection of causal interactions is of great importance when inferring complex
ecosystem functional and structural networks for basic and applied research. Convergent …

Animal behaviour is often quantified through subjective, incomplete variables that mask
essential dynamics. Here, we develop a maximally predictive behavioural-state space from …

Forecasting all components in complex systems is an open and challenging task, possibly
due to high dimensionality and undesirable predictors. We bridge this gap by proposing a …

Ecosystems are complex and sparsely observed making inference and prediction
challenging. Empirical dynamic modelling (EDM) circumvents the need for a parametric …