Armed with an influx of cash and public enthusiasm, researchers are looking to improve how ageing is measured.

We study entanglement percolation in qubit-based planar quantum network models of arbitrary topology, where neighboring nodes are initially connected by pure states with quenched disorder in their ent...

We investigate the kinetics of material transport across interfaces in phase-separated mixtures. Using non-equilibrium thermodynamics we derive the interfacial kinetics, describing the movement of the...

Cognitive scientists often infer multidimensional representations from data. Whether the data involve text, neuroimaging, neural networks, or human judgments, researchers frequently infer and analyze latent representational spaces (i.e., embeddings). However, the properties of a latent representation (e.g., prediction performance, interpretability, compactness) depend on the inference procedure, which can vary widely across endeavors. For example, dimensions are not always globally interpretable and the dimensionality of different embeddings may not be readily comparable. Moreover, the dichotomy between multidimensional spaces and purportedly richer representational formats, such as graph representations, is misleading. We review what the different notions of dimension in cognitive science imply for how these latent representations should be used and interpreted.

arXiv abstract link

The influence anatomy exerts on communication between brain regions remains unclear. In this Review, Greaves et al. synthesize how methods of structural connectivity integration constrain inference-ba...

Crocodile head scales self-organize through purely mechanical compressive skin folding rather than a patterning process controlled by gene interactions.

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Or: when you don't even see what your model is

The authors introduce a single-molecule DNA-barcoding method, resolution enhancement by sequential imaging, that improves the resolution of fluorescence microscopy down to the Ångström scale using off...

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