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  1. ; ; ; ( , International Conference on Machine Learning)
    Free, publicly-accessible full text available July 1, 2023
  2. ; ; ; ; ; ; ( , Physical Review A)
    Free, publicly-accessible full text available July 1, 2023
  3. ; ; ; ( , Journal of Geophysical Research: Oceans)
    Full Text Available 
  4. ; ; ; ( , The International Conference on Learning Representations (ICLR))
    Accepted Manuscript Full Text Available
  5. ; ; ; ; ; ; ; ; ; ; et al ( , International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI))
    Segmenting 3D cell nuclei from microscopy image volumes is critical for biological and clinical analysis, enabling the study of cellular expression patterns and cell lineages. However, current datasets for neuronal nuclei usually contain volumes smaller than 0.01 cubic mm with fewer than 500 instances per volume, unable to reveal the complexity in large brain regions and restrict the investigation of neuronal structures. In this paper, we have pushed the task forward to the sub-cubic millimeter scale and curated the NucMM dataset with two fully annotated volumes: one 0.1 cubic mm electron microscopy (EM) volume containing nearly the entire zebra sh brainmore »with around 170,000 nuclei; and one 0.25 cubic mm micro-CT (uCT) volume containing part of a mouse visual cortex with about 7,000 nuclei. With two imaging modalities and significantly increased volume size and instance numbers, we discover a great diversity of neuronal nuclei in appearance and density, introducing new challenges to the  eld. We also perform a statistical analysis to illustrate those challenges quantitatively. To tackle the challenges, we propose a novel hybrid-representation learning model that combines the merits of foreground mask, contour map, and signed distance transform to produce high-quality 3D masks. The benchmark comparisons on the NucMM dataset show that our proposed method significantly outperforms state-of- the-art nuclei segmentation approaches. Code and data are available at https://connectomics-bazaar.github.io/proj/nucMM/index.html.« less
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  6. ; ; ; ; ; ; ; ( , Journal of geophysical research. Oceans)
    The Lagrangian method—where current location and intensity are determined by tracking the movement of flow along its path—is the oldest technique for measuring the ocean circulation. For centuries, mariners used compilations of ship drift data to map out the location and intensity of surface currents along major shipping routes of the global ocean. In the mid‐20th century, technological advances in electronic navigation allowed oceanographers to continuously track freely drifting surface buoys throughout the ice‐free oceans and begin to construct basin‐scale, and eventually global‐scale, maps of the surface circulation. At about the same time, development of acoustic methods to track neutrallymore »buoyant floats below the surface led to important new discoveries regarding the deep circulation. Since then, Lagrangian observing and modeling techniques have been used to explore the structure of the general circulation and its variability throughout the global ocean, but especially in the Atlantic Ocean. In this review, Lagrangian studies that focus on pathways of the upper and lower limbs of the Atlantic Meridional Overturning Circulation (AMOC), both observational and numerical, have been gathered together to illustrate aspects of the AMOC that are uniquely captured by this technique. These include the importance of horizontal recirculation gyres and interior (as opposed to boundary) pathways, the connectivity (or lack thereof) of the AMOC across latitudes, and the role of mesoscale eddies in some regions as the primary AMOC transport mechanism. There remain vast areas of the deep ocean where there are no direct observations of the pathways of the AMOC.« less
    Accepted Manuscript Full Text Available
  7. ; ; ; ; ; ; ; ; ( , Journal of Geophysical Research: Oceans)
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  8. ; ; ( , Geophysical Research Letters)
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  9. ; ; ; ( , Journal of Geophysical Research: Space Physics)
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  10. ; ; ; ; ; ( , Journal of Geophysical Research: Space Physics)
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