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Yu-Yao Lin

dblp:157/8352 · DBLP profile ↗
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3ranked-venue papers
1as first author
0since 2021 · last 2018
0000-0001-9761-5156ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 1 · 1 first-authorComputer networks · 1Theory of computation · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
1 paper
Network measurement and analytics · 67% Internet architecture and protocols · 33%
Artificial intelligence
1 paper
Motion planning and robot control · 100%
Computer graphics and multimedia
1 paper
Geometric modeling and processing · 100%
Theoretical computer science
1 paper
Graph algorithms and graph theory · 100%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › path planning
coverage path planning
0.312017
Robot Coverage Path planning for general surfaces using quadratic differentials · ICRA 2017
Internet architecture and protocols › network topology
internet topology
0.212015
Ricci curvature of the Internet topology · INFOCOM 2015
Network measurement and analytics
topology analysis
0.212015
Ricci curvature of the Internet topology · INFOCOM 2015
Network measurement and analytics › topology measurement
topology characterization
0.212015
Ricci curvature of the Internet topology · INFOCOM 2015
Geometric modeling and processing
surface parameterization
0.112017
Robot Coverage Path planning for general surfaces using quadratic differentials · ICRA 2017
Graph algorithms and graph theory › network analysis
complex networks
0.112015
Ricci curvature of the Internet topology · INFOCOM 2015

Methods — techniques the papers use, named apart from their topics

holomorphic quadratic differentials · 0.6gromov thin triangle condition · 0.4discrete ricci curvature · 0.4
YearPublicationVenuePosition
2018 Network Alignment by Discrete Ollivier-Ricci Flow
Chien-Chun Ni, Yu-Yao Lin, Jie Gao 0001, Xianfeng Gu
GD2
2017 Robot Coverage Path planning for general surfaces using quadratic differentials
abstract
Robot Coverage Path planning (i.e., the process of providing full coverage of a given domain by one or multiple robots) is a classical problem in the field of robotics and motion planning. The goal of such planning is to provide nearly full coverage while also minimize duplicately visited area. In this paper, we focus on the scenario of path planning on general surface, including planar domains with complex topology, complex terrain, and general surface in 3D space. Our approach described in this paper adopts a natural, intrinsic and global parametrization of the surface for robot path planning, namely the holomorphic quadratic differentials. We give each point on the surface a uv-coordinates naturally represented by a complex number, except for a small number of zero points (singularities). We show that natural, efficient robot paths can be obtained by using such coordinate systems. The method is based on intrinsic geometry and thus can be adapted to general surface exploration in 3D.
Yu-Yao Lin, Chien-Chun Ni, Na Lei, Xianfeng Gu, Jie Gao 0001
ICRA1
2015 Ricci curvature of the Internet topology
abstract
Analysis of Internet topologies has shown that the Internet topology has negative curvature, measured by Gromov's “thin triangle condition”, which is tightly related to core congestion and route reliability. In this work we analyze the discrete Ricci curvature of the Internet, defined by Ollivier [1], Lin et al. [2], etc. Ricci curvature measures whether local distances diverge or converge. It is a more local measure which allows us to understand the distribution of curvatures in the network. We show by various Internet data sets that the distribution of Ricci cuvature is spread out, suggesting the network topology to be non-homogenous. We also show that the Ricci curvature has interesting connections to both local measures such as node degree and clustering coefficient, global measures such as betweenness centrality and network connectivity, as well as auxilary attributes such as geographical distances. These observations add to the richness of geometric structures in complex network theory.
Chien-Chun Ni, Yu-Yao Lin, Jie Gao 0001, Xianfeng Gu, Emil Saucan
INFOCOM2