In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

[Q]: Consider a weighted or unweighted k-nearest neighbor graph that has been built on n data points drawn randomly according to some density p on R. We study the convergence of the shortest path distance in such graphs as the sample size tends to infinity. We prove that for unweighted kNN graphs, this distance converges to an unpleasant distance function on the underlying space whose properties are detrimental to machine learning. We also study the behavior of the shortest path distance in weighted kNN graphs.
[A]: Shortest path distance in random k-nearest neighbor graphs


[Q]: In animal monitoring applications, both animal detection and their movement prediction are major tasks. While a variety of animal monitoring strategies exist, most of them rely on mounting devices. However, in real world, it is difficult to find these animals and install mounting devices. In this paper, we propose an animal monitoring application by utilizing wireless sensor networks (WSNs) and unmanned aerial vehicle (UAV). The objective of the application is to detect locations of endangered species in large-scale wildlife areas and monitor movement of animals without any attached devices. In this application, sensors deployed throughout the observation area are responsible for gathering animal information. The UAV flies above the observation area and collects the information from sensors. To achieve the information efficiently, we propose a path planning approach for the UAV based on a Markov decision process (MDP) model. The UAV receives a certain amount of reward from an area if some animals are detected at that location. We solve the MDP using Q-learning such that the UAV prefers going to those areas that animals are detected before. Meanwhile, the UAV explores other areas as well to cover the entire network and detects changes in the animal positions. We first define the mathematical model underlying the animal monitoring problem in terms of the value of information (VoI) and rewards. We propose a network model including clusters of sensor nodes and a single UAV that acts as a mobile sink and visits the clusters. Then, one MDP-based path planning approach is designed to maximize the VoI while reducing message delays. The effectiveness of the proposed approach is evaluated using two real-world movement datasets of zebras and leopard. Simulation results show that our approach outperforms greedy and random heuristics as well as the path planning based on the solution of the traveling salesman problem.
[A]: Internet of Things Applications: Animal Monitoring with Unmanned Aerial Vehicle


[Q]: This paper examines the "K2" network scoring metric of Cooper and Herskovits. It shows counterintuitive results from applying this metric to simple networks. One family of noninformative priors is suggested for assigning equal scores to equivalent networks.
[A]:
A Bayesian Method Reexamined