3D Position Nanometrology of a Coronavirus-like Nanoparticle with Topologically Structured Light

Scattering of topologically structured light is highly sensitive to the position of a scattering object. We show that the position of a coronavirus-like 100 nm polystyrene sphere can be measured optically with deeply subwavelength accuracy. © Optica Publishing Group 2022, © 2022 The Author(s)

Diffusion Earth Mover's Distance and Distribution Embeddings

We propose a new fast method of measuring distances between large numbers of related high dimensional datasets called the Diffusion Earth Mover's Distance (EMD). We model the datasets as distributions supported on common data graph that is derived from the affinity matrix computed on the combined data. In such cases where the graph is a discretization of an underlying Riemannian closed manifold, we prove that Diffusion EMD is topologically equivalent to the standard EMD with a geodesic ground distance. Diffusion EMD can be computed in (O) over tilde (n) time and is more accurate than similarly fast algorithms such as tree-based EMDs. We also show Diffusion EMD is fully differentiable, making it amenable to future uses in gradient-descent frameworks such as deep neural networks. Finally, we demonstrate an application of Diffusion EMD to single cell data collected from 210 COVID-19 patient samples at Yale New Haven Hospital. Here, Diffusion EMD can derive distances between patients on the manifold of cells at least two orders of magnitude faster than equally accurate methods. This distance matrix between patients can be embedded into a higher level patient manifold which uncovers structure and heterogeneity in patients. More generally, Diffusion EMD is applicable to all datasets that are massively collected in parallel in many medical and biological systems.

Telework in Canada: Who Is Working from Home during the COVID-19 Pandemic?

This study focuses on the demographic and human capital characteristics of Canadians that are associated with working from home (WFH), before and during the COVID-19 pandemic, or being absent from work, versus those Canadians who continue to work outside the home (i.e., who do not WFH). The results show significant differences in the incidence of WFH during the pandemic: 1) there are no significant differences between females and mates;2) immigrants are less likely to WFH;3) younger workers are more likely to WFH;4) education is positively associated with WFH;and 5) self-reported health is unrelated to WFH. The results from this natural experiment suggest potential policy and organizational implications if the pandemic WFH environment continues for an extended period of time.