skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


  • NSF Public Access
  • Search Results
  • How Did We Get Here: What Are Droplets and Aerosols and How Far Do They Go? A Historical Perspective on the Transmission of Respiratory Infectious Diseases.
Title: How Did We Get Here: What Are Droplets and Aerosols and How Far Do They Go? A Historical Perspective on the Transmission of Respiratory Infectious Diseases.
Award ID(s):
2026225
PAR ID:
10315915
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
Interface focus
ISSN:
2042-8898
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. (, Forest Ecosystems)
  2. ; ; ; ; ; (, Journal of Chemical Information and Modeling)
  3. ; ; (, Eos)
    Earthquakes: Nucleation, Triggering, Rupture, and Relationships to Aseismic Processes; Cargèse, Corsica, France, 2–6 October 2017 
    more » « less
  4. ; (, Trends in Biochemical Sciences)
  5. ; (, Annual Review of Statistics and Its Application)
    Reid, Nancy (Ed.)
    Manifold learning (ML), also known as nonlinear dimension reduction, is a set of methods to find the low-dimensional structure of data. Dimension reduction for large, high-dimensional data is not merely a way to reduce the data; the new representations and descriptors obtained by ML reveal the geometric shape of high-dimensional point clouds and allow one to visualize, denoise, and interpret them. This review presents the underlying principles of ML, its representative methods, and their statistical foundations, all from a practicing statistician's perspective. It describes the trade-offs and what theory tells us about the parameter and algorithmic choices we make in order to obtain reliable conclusions. Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 11 is March 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email