In the coming decades, the relatively constant age-standardized prevalence of global CVD suggests that the net effect of summative preventative efforts will likely continue to be unchanged.
Socioeconomic disparities and health care access should be addressed in communities with high chronic disease prevalence, and carefully directed resource allocation and interventions are necessary to reduce the effects of chronic disease on these communities.
It is argued that these new approaches would allow researchers to revisit the classic urban theories and themes and potentially help cities create environments that align with human behaviors and aspirations in today’s AI-driven and data-centric era.
This work proposes a Multi-Pretext Masked Autoencoder (MP-MAE) approach to learn general-purpose representations for optical satellite images and demonstrates that this approach outperforms both MAEs pretrained on ImageNet and MAEs pretrained on domain-specific satellite images.
It is shown that the micro-averaged producer’s accuracy, user’s accuracy, and F1-score, as well as weighted macro-averaged statistics where the class prevalences are used as weights, are all equivalent to each other and to the overall accuracy, and thus, are redundant and should be avoided.
PANGAEA is a standardized evaluation protocol that covers a diverse set of datasets, tasks, resolutions, sensor modalities, and temporalities, allowing for the seamless inclusion of new datasets, models, and tasks in future research and is designed to be highly extensible.
This Perspective argues for using cell-phone data as a standard because they are information rich and geographically expansive and because they illuminate both people’s concentrations in given areas and flows among them.
A future where GIS moves beyond traditional workflows to autonomously reason, derive, innovate, and advance geospatial solutions to pressing global challenges is envisions.
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