Modality-agnostic decoders leverage modality-invariant representations in human subjects' brain activity to predict stimuli irrespective of their modality (image, text, mental imagery).

This study presents valuable findings by reanalyzing previously published MEG and ECoG datasets to challenge the predictive nature of pre-onset neural encoding effects. The evidence supporting the ...

Companies and researchers can use aggregated, anonymized LinkedIn data to spot trends in the job market. This means looking ...

This study highlights the potential for using deep learning methods on longitudinal health data from both primary and ...

Tungsten's superior performance in extreme environments makes it a leading candidate for plasma-facing components (PFCs) in ...

In a recent paper, SFI Complexity Postdoctoral Fellow Yuanzhao Zhang and co-author William Gilpin show that a deceptively simple forecasting strategy can outperform several leading machine learning ...

Afforestation—establishing forests on previously non-forested land, or where forests have not existed for a long time—is one ...

Building neural networks from scratch in Python with NumPy is one of the most effective ways to internalize deep learning fundamentals. By coding forward and backward propagation yourself, you see how ...

The rapid advancement of spatial and single-cell omics technologies has revolutionized molecular biosciences by enabling high-resolution profiling of gene ...

Understanding The Robotics Landscape The Current State of Robotics Robots aren’t just science fiction anymore; they’re ...