Volume 2 Issue 8, August 2022

A new Bayesian analysis of remote work data supports one of the oldest theories in social networks, with fresh implications for the future of work environments.

An algorithm is presented for the simulation of reaction–diffusion systems on complex geometries, providing insight on how the interplay of cell geometry and biochemistry can control polarity in living cells.

Determining how information flows throughout a network of interconnected components is a challenging task in many scientific domains. A framework is presented to deconstruct the flow of signals that are transmitted across any two areas (such as brain areas) and define how each area represents these signals.

A machine learning method is developed and used to predict the adsorption configurations and energies of complex molecules at the surfaces of transition metals and alloys. This method will be useful for investigating complex reaction networks at complex catalyst materials to understand and improve the performance of heterogeneous catalysts.

Multi-messenger astronomy offers promises for exploring Universe events in distance. Nevertheless, there are numerous computational challenges when analyzing the massive heterogeneous messenger data from various detectors, creating research opportunities to the community, such as developing multimodal machine learning.

A systematic framework is introduced to calculate the effective carrier lifetime in semiconductor crystals under realistic conditions that are comparable with experiments. It helps explain the discrepancy between the calculated and experimental lifetimes in hybrid perovskites.

This study suggests that a lack of co-location hinders the formation of ‘weak ties’—which are crucial for information spread—in communication networks on the basis of an analysis of an email network of more than 2,800 university researchers.

A spectrally accurate numerical method for solving partial differential equations (PDEs) on non-uniformly curved surfaces is developed. The method is applied to a PDE model of cell polarization to show that geometric effects allow the existence of unexpected multidomain solutions.

A dimensionality reduction framework, delayed latents across groups (DLAG), is proposed for disentangling the concurrent flow of signals between populations of neurons. DLAG reveals bidirectional communication between visual cortical areas.