Sampling-based path planning is a popular methodology for robot planning. With uniform sampling strategy to explore the state space, feasible can be found without complex geometric modeling of configuration space. However, quality initial solution not guaranteed, and convergence speed optimal slow. In this paper, we present novel image-based algorithm overcome these limitations. Specifically, g...

Recently, there has been growing interest in social network analysis. Graph models for social network analysis are usually assumed to be a deterministic graph with fixed weights for its edges or nodes. As activities of users in online social networks are changed with time, however, this assumption is too restrictive because of uncertainty, unpredictability and the time-varying nature of such re...

The paper studies empirically the time-space trade-off between sampling and inference in the cutset sampling algorithm. The algorithm samples over a subset of nodes in a Bayesian network and applies exact inference over the rest. As the size of the sampling space decreases, requiring less samples for convergence, the time for generating each single sample increases. Algorithm wcutset sampling s...

Sampling the full diversity of interactions in an ecological community is a highly intensive effort. Recent studies have demonstrated that many network metrics are sensitive to both sampling effort and network size. Here, we develop a statistical framework, based on bootstrap resampling, that aims to assess sampling sufficiency for some of the most widely used metrics in network ecology, namely...

Denial of Service (DoS) is a prevalent threat in today's networks because DoS attacks are easy to launch, while defending a network resource against them is disproportionately difficult. Despite the extensive research in recent years, DoS attacks continue to harm, as the attackers adapt to the newer protection mechanisms. There is an emerging need for the traffic processing capability of networ...

Finding a subset of users to statistically represent the original social network is a fundamental issue in Social Network Analysis (SNA). The problem has not been extensively studied in existing literature. In this paper, we present a formal definition of the problem of sampling representative users from social network. We propose two sampling models and theoretically prove their NP-hardness. T...

In this article we discuss a network sampling design that can be applied in drug abuse research at the community level. At this level often some partial sampling frame such as the register of a drug aid agency is available. This partial sampling frame can be used as the start of a network sample. Each selected registered drug abuser mentions his relationships with other drug abusers, and from t...

UNLABELLED Constraint-based models of metabolic networks are typically underdetermined, because they contain more reactions than metabolites. Therefore the solutions to this system do not consist of unique flux rates for each reaction, but rather a space of possible flux rates. By uniformly sampling this space, an estimated probability distribution for each reaction's flux in the network can be...

To understand the physical and biological relationships often requires high resolution sampling far exceeding present capabilities. If spatially adaptive sampling can be developed, data resolution can be greatly improved over that presently possible with conventional sensor platforms. One technique to achieve both spatially and temporally adaptive sampling is an Autonomous Oceanographic Samplin...