5,939,076 research outputs found
Point Information Gain and Multidimensional Data Analysis
We generalize the Point information gain (PIG) and derived quantities, i.e.
Point information entropy (PIE) and Point information entropy density (PIED),
for the case of R\'enyi entropy and simulate the behavior of PIG for typical
distributions. We also use these methods for the analysis of multidimensional
datasets. We demonstrate the main properties of PIE/PIED spectra for the real
data on the example of several images, and discuss possible further utilization
in other fields of data processing.Comment: 16 pages, 6 figure
ecp: An R Package for Nonparametric Multiple Change Point Analysis of Multivariate Data
There are many different ways in which change point analysis can be
performed, from purely parametric methods to those that are distribution free.
The ecp package is designed to perform multiple change point analysis while
making as few assumptions as possible. While many other change point methods
are applicable only for univariate data, this R package is suitable for both
univariate and multivariate observations. Estimation can be based upon either a
hierarchical divisive or agglomerative algorithm. Divisive estimation
sequentially identifies change points via a bisection algorithm. The
agglomerative algorithm estimates change point locations by determining an
optimal segmentation. Both approaches are able to detect any type of
distributional change within the data. This provides an advantage over many
existing change point algorithms which are only able to detect changes within
the marginal distributions
Point Discriminative Learning for Data-efficient 3D Point Cloud Analysis
3D point cloud analysis has drawn a lot of research attention due to its wide
applications. However, collecting massive labelled 3D point cloud data is both
time-consuming and labor-intensive. This calls for data-efficient learning
methods. In this work we propose PointDisc, a point discriminative learning
method to leverage self-supervisions for data-efficient 3D point cloud
classification and segmentation. PointDisc imposes a novel point discrimination
loss on the middle and global level features produced by the backbone network.
This point discrimination loss enforces learned features to be consistent with
points belonging to the corresponding local shape region and inconsistent with
randomly sampled noisy points. We conduct extensive experiments on 3D object
classification, 3D semantic and part segmentation, showing the benefits of
PointDisc for data-efficient learning. Detailed analysis demonstrate that
PointDisc learns unsupervised features that well capture local and global
geometry.Comment: This work is published in 3DV 202
Innovations in the Analysis of Chandra-ACIS Observations
As members of the instrument team for the Advanced CCD Imaging Spectrometer
(ACIS) on NASA's Chandra X-ray Observatory and as Chandra General Observers, we
have developed a wide variety of data analysis methods that we believe are
useful to the Chandra community, and have constructed a significant body of
publicly-available software (the ACIS Extract package) addressing important
ACIS data and science analysis tasks. This paper seeks to describe these data
analysis methods for two purposes: to document the data analysis work performed
in our own science projects, and to help other ACIS observers judge whether
these methods may be useful in their own projects (regardless of what tools and
procedures they choose to implement those methods).
The ACIS data analysis recommendations we offer here address much of the
workflow in a typical ACIS project, including data preparation, point source
detection via both wavelet decomposition and image reconstruction, masking
point sources, identification of diffuse structures, event extraction for both
point and diffuse sources, merging extractions from multiple observations,
nonparametric broad-band photometry, analysis of low-count spectra, and
automation of these tasks. Many of the innovations presented here arise from
several, often interwoven, complications that are found in many Chandra
projects: large numbers of point sources (hundreds to several thousand), faint
point sources, misaligned multiple observations of an astronomical field, point
source crowding, and scientifically relevant diffuse emission.Comment: Accepted by the ApJ, 2010 Mar 10 (\#343576) 39 pages, 16 figure
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