4,618 research outputs found
Search for Evergreens in Science: A Functional Data Analysis
Evergreens in science are papers that display a continual rise in annual
citations without decline, at least within a sufficiently long time period.
Aiming to better understand evergreens in particular and patterns of citation
trajectory in general, this paper develops a functional data analysis method to
cluster citation trajectories of a sample of 1699 research papers published in
1980 in the American Physical Society (APS) journals. We propose a functional
Poisson regression model for individual papers' citation trajectories, and fit
the model to the observed 30-year citations of individual papers by functional
principal component analysis and maximum likelihood estimation. Based on the
estimated paper-specific coefficients, we apply the K-means clustering
algorithm to cluster papers into different groups, for uncovering general types
of citation trajectories. The result demonstrates the existence of an evergreen
cluster of papers that do not exhibit any decline in annual citations over 30
years.Comment: 40 pages, 9 figure
LINE: Large-scale Information Network Embedding
This paper studies the problem of embedding very large information networks
into low-dimensional vector spaces, which is useful in many tasks such as
visualization, node classification, and link prediction. Most existing graph
embedding methods do not scale for real world information networks which
usually contain millions of nodes. In this paper, we propose a novel network
embedding method called the "LINE," which is suitable for arbitrary types of
information networks: undirected, directed, and/or weighted. The method
optimizes a carefully designed objective function that preserves both the local
and global network structures. An edge-sampling algorithm is proposed that
addresses the limitation of the classical stochastic gradient descent and
improves both the effectiveness and the efficiency of the inference. Empirical
experiments prove the effectiveness of the LINE on a variety of real-world
information networks, including language networks, social networks, and
citation networks. The algorithm is very efficient, which is able to learn the
embedding of a network with millions of vertices and billions of edges in a few
hours on a typical single machine. The source code of the LINE is available
online.Comment: WWW 201
Cross-source Point Cloud Registration: Challenges, Progress and Prospects
The emerging topic of cross-source point cloud (CSPC) registration has
attracted increasing attention with the fast development background of 3D
sensor technologies. Different from the conventional same-source point clouds
that focus on data from same kind of 3D sensor (e.g., Kinect), CSPCs come from
different kinds of 3D sensors (e.g., Kinect and { LiDAR}). CSPC registration
generalizes the requirement of data acquisition from same-source to different
sources, which leads to generalized applications and combines the advantages of
multiple sensors. In this paper, we provide a systematic review on CSPC
registration. We first present the characteristics of CSPC, and then summarize
the key challenges in this research area, followed by the corresponding
research progress consisting of the most recent and representative developments
on this topic. Finally, we discuss the important research directions in this
vibrant area and explain the role in several application fields.Comment: Accepted by Neurocomputing 202
Hidden Conformal Symmetry for Vector Field on Various Black Hole Backgrounds
Hidden conformal symmetries of scalar field on various black hole backgrounds
have been investigated for years, but whether those features holds for other
fields are still open questions. Recently, with proper assumptions, Lunin
achieves to the separation of variables for Maxwell equations on Kerr
background. In this paper, with that equation, we find that hidden conformal
symmetry appears at near region under low frequency limit. We also extended
those results to vector field on Kerr-(A)dS and Kerr-NUT-(A)dS backgrounds,
then hidden conformal symmetry also appears if we focusing on the near-horizon
region at low frequency limit.Comment: 18 pages, no figure, matches the published versio
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