2,803 research outputs found
Humans perceive flicker artifacts at 500 Hz.
Humans perceive a stable average intensity image without flicker artifacts when a television or monitor updates at a sufficiently fast rate. This rate, known as the critical flicker fusion rate, has been studied for both spatially uniform lights, and spatio-temporal displays. These studies have included both stabilized and unstablized retinal images, and report the maximum observable rate as 50-90 Hz. A separate line of research has reported that fast eye movements known as saccades allow simple modulated LEDs to be observed at very high rates. Here we show that humans perceive visual flicker artifacts at rates over 500 Hz when a display includes high frequency spatial edges. This rate is many times higher than previously reported. As a result, modern display designs which use complex spatio-temporal coding need to update much faster than conventional TVs, which traditionally presented a simple sequence of natural images
Intrinsic flat stability of the positive mass theorem for graphical hypersurfaces of Euclidean space
The rigidity of the Positive Mass Theorem states that the only complete
asymptotically flat manifold of nonnegative scalar curvature and zero mass is
Euclidean space. We study the stability of this statement for spaces that can
be realized as graphical hypersurfaces in Euclidean space. We prove (under
certain technical hypotheses) that if a sequence of complete asymptotically
flat graphs of nonnegative scalar curvature has mass approaching zero, then the
sequence must converge to Euclidean space in the pointed intrinsic flat sense.
The appendix includes a new Gromov-Hausdorff and intrinsic flat compactness
theorem for sequences of metric spaces with uniform Lipschitz bounds on their
metrics.Comment: 31 pages, 2 figures, v2: to appear in Crelle's Journal, many minor
changes, one new exampl
One-step Estimation of Networked Population Size: Respondent-Driven Capture-Recapture with Anonymity
Population size estimates for hidden and hard-to-reach populations are
particularly important when members are known to suffer from disproportion
health issues or to pose health risks to the larger ambient population in which
they are embedded. Efforts to derive size estimates are often frustrated by a
range of factors that preclude conventional survey strategies, including social
stigma associated with group membership or members' involvement in illegal
activities.
This paper extends prior research on the problem of network population size
estimation, building on established survey/sampling methodologies commonly used
with hard-to-reach groups. Three novel one-step, network-based population size
estimators are presented, to be used in the context of uniform random sampling,
respondent-driven sampling, and when networks exhibit significant clustering
effects. Provably sufficient conditions for the consistency of these estimators
(in large configuration networks) are given. Simulation experiments across a
wide range of synthetic network topologies validate the performance of the
estimators, which are seen to perform well on a real-world location-based
social networking data set with significant clustering. Finally, the proposed
schemes are extended to allow them to be used in settings where participant
anonymity is required. Systematic experiments show favorable tradeoffs between
anonymity guarantees and estimator performance.
Taken together, we demonstrate that reasonable population estimates can be
derived from anonymous respondent driven samples of 250-750 individuals, within
ambient populations of 5,000-40,000. The method thus represents a novel and
cost-effective means for health planners and those agencies concerned with
health and disease surveillance to estimate the size of hidden populations.
Limitations and future work are discussed in the concluding section
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