22,992 research outputs found
Detecting Outliers in Data with Correlated Measures
Advances in sensor technology have enabled the collection of large-scale
datasets. Such datasets can be extremely noisy and often contain a significant
amount of outliers that result from sensor malfunction or human operation
faults. In order to utilize such data for real-world applications, it is
critical to detect outliers so that models built from these datasets will not
be skewed by outliers.
In this paper, we propose a new outlier detection method that utilizes the
correlations in the data (e.g., taxi trip distance vs. trip time). Different
from existing outlier detection methods, we build a robust regression model
that explicitly models the outliers and detects outliers simultaneously with
the model fitting.
We validate our approach on real-world datasets against methods specifically
designed for each dataset as well as the state of the art outlier detectors.
Our outlier detection method achieves better performances, demonstrating the
robustness and generality of our method. Last, we report interesting case
studies on some outliers that result from atypical events.Comment: 10 page
Towards Spinning Mellin Amplitudes
We construct the Mellin representation of four point conformal correlation
function with external primary operators with arbitrary integer spacetime
spins, and obtain a natural proposal for spinning Mellin amplitudes. By
restricting to the exchange of symmetric traceless primaries, we generalize the
Mellin transform for scalar case to introduce discrete Mellin variables for
incorporating spin degrees of freedom. Based on the structures about spinning
three and four point Witten diagrams, we also obtain a generalization of the
Mack polynomial which can be regarded as a natural kinematical polynomial basis
for computing spinning Mellin amplitudes using different choices of interaction
vertices.Comment: 32 pages, 2 figures, v2: typos corrected, clarification added,
references updated, to appear in NP
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