#### Filter Results:

- Full text PDF available (13)

#### Publication Year

2012

2017

- This year (6)
- Last 5 years (17)
- Last 10 years (17)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Key Phrases

Learn More

- Lingxiao Huang, Jian Li
- ICALP
- 2015

We consider the stochastic graph model where the location of each vertex is a random point in a given metric space. We study the problems of computing the expected lengths of the minimum spanning tree, the minimum perfect matching and the minimum cycle cover on such a stochastic graph and obtain an FPRAS (Fully Polynomial Randomized Approximation Scheme)… (More)

- Jian Li, Yicheng Liu, Lingxiao Huang, Pingzhong Tang
- AAMAS
- 2014

We revisit the pairwise kidney exchange problem established by Roth Sonmez and Unver [23]. Our goal, explained in terms of graph theory, is to find a maximum fractional matching on an undirected graph, that Lorenz-dominates any other fractional matching. The Lorenz-dominant fractional matching, which can be implemented as a lottery of integral matchings, is… (More)

- Lingxiao Huang, Jian Li, Jeff M. Phillips, Haitao Wang
- ESA
- 2016

<lb>With the dramatic growth in the number of application domains that generate<lb>probabilistic, noisy and uncertain data, there has been an increasing interest in designing<lb>algorithms for geometric or combinatorial optimization problems over such data. In<lb>this paper, we initiate the study of constructing ε-kernel coresets for uncertain points.<lb>We… (More)

- Pengqian Fang, Xiangli Liu, Lingxiao Huang, Xiaoyan Zhang, Zi Fang
- International journal for equity in health
- 2014

INTRODUCTION
This study analyzes the factors that influence the turnover intention of village doctors by investigating village clinic workers in rural areas, particularly in Xiangyang City, Hubei Province.
METHODS
A total of 1184 village clinics were sampled randomly in Xiangyang City. The research assistants distributed 1930 questionnaires to village… (More)

- Lingxiao Huang, Jian Li
- SODA
- 2017

Solving geometric optimization problems over uncertain data have become increasingly important in many applications and have attracted a lot of attentions in recent years. In this paper, we study two important geometric optimization problems, the k-center problem and the j-flat-center problem, over stochastic/uncertain data points in Euclidean spaces. For… (More)

- Lingxiao Huang, Jian Li, Qicai Shi
- COCOON
- 2015

We study the minimum connected sensor cover problem (MIN-CSC) and the budgeted connected sensor cover (Budgeted-CSC) problem, both motivated by important applications in wireless sensor networks. In both problems, we are given a set of sensors and a set of target points in the Euclidean plane. In MIN-CSC, our goal is to find a set of sensors of minimum… (More)

- Lingxiao Huang, Jian Li
- ArXiv
- 2012

- Hu Ding, Lunjia Hu, Lingxiao Huang, Jian Li
- WADS
- 2017

In recent years, the capacitated center problems have attracted a lot of research interest. Given a set of vertices V , we want to find a subset of vertices S, called centers, such that the maximum cluster radius is minimized. Moreover, each center in S should satisfy some capacity constraint, which could be an upper or lower bound on the number of vertices… (More)

- Lingxiao Huang, Pinyan Lu, Chihao Zhang
- SODA
- 2016

Markov Chain Monte Carlo (MCMC) method is a widely used algorithm design scheme with many applications. To make efficient use of this method, the key step is to prove that the Markov chain is rapid mixing. Canonical paths is one of the two main tools to prove rapid mixing. However, there are much fewer success examples comparing to coupling, the other main… (More)

- Lingqing Ai, Xian Wu, Lingxiao Huang, Longbo Huang, Pingzhong Tang, Jian Li
- SIGMETRICS
- 2014

We consider the <i>multi-shop ski rental</i> problem. This problem generalizes the classic ski rental problem to a multi-shop setting, in which each shop has different prices for renting and purchasing a pair of skis, and a <i>consumer</i> has to make decisions on when and where to buy. We are interested in the <i>optimal online (competitive-ratio… (More)