109 research outputs found
Analysis of Crowdsourced Sampling Strategies for HodgeRank with Sparse Random Graphs
Crowdsourcing platforms are now extensively used for conducting subjective
pairwise comparison studies. In this setting, a pairwise comparison dataset is
typically gathered via random sampling, either \emph{with} or \emph{without}
replacement. In this paper, we use tools from random graph theory to analyze
these two random sampling methods for the HodgeRank estimator. Using the
Fiedler value of the graph as a measurement for estimator stability
(informativeness), we provide a new estimate of the Fiedler value for these two
random graph models. In the asymptotic limit as the number of vertices tends to
infinity, we prove the validity of the estimate. Based on our findings, for a
small number of items to be compared, we recommend a two-stage sampling
strategy where a greedy sampling method is used initially and random sampling
\emph{without} replacement is used in the second stage. When a large number of
items is to be compared, we recommend random sampling with replacement as this
is computationally inexpensive and trivially parallelizable. Experiments on
synthetic and real-world datasets support our analysis
Human Mobility Trends during the COVID-19 Pandemic in the United States
In March of this year, COVID-19 was declared a pandemic and it continues to
threaten public health. This global health crisis imposes limitations on daily
movements, which have deteriorated every sector in our society. Understanding
public reactions to the virus and the non-pharmaceutical interventions should
be of great help to fight COVID-19 in a strategic way. We aim to provide
tangible evidence of the human mobility trends by comparing the day-by-day
variations across the U.S. Large-scale public mobility at an aggregated level
is observed by leveraging mobile device location data and the measures related
to social distancing. Our study captures spatial and temporal heterogeneity as
well as the sociodemographic variations regarding the pandemic propagation and
the non-pharmaceutical interventions. All mobility metrics adapted capture
decreased public movements after the national emergency declaration. The
population staying home has increased in all states and becomes more stable
after the stay-at-home order with a smaller range of fluctuation. There exists
overall mobility heterogeneity between the income or population density groups.
The public had been taking active responses, voluntarily staying home more, to
the in-state confirmed cases while the stay-at-home orders stabilize the
variations. The study suggests that the public mobility trends conform with the
government message urging to stay home. We anticipate our data-driven analysis
offers integrated perspectives and serves as evidence to raise public awareness
and, consequently, reinforce the importance of social distancing while
assisting policymakers.Comment: 11 pages, 9 figure
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