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We consider the optimal multi-agent persistent monitoring problem defined by a team of cooperating agents visiting a set of nodes (targets) on a graph with the objective of minimizing a measure of overall node state uncertainty. The solution to this problem involves agent trajectories defined both by the sequence of nodes to be visited by each agent and the amount of time spent at each node. We propose a class of distributed threshold-based parametric controllers through which agent transitions from one node to the next are controlled by thresholds on the node uncertainty. The resulting behavior of the agent-target system is described by a hybrid dynamic system. This enables the use of Infinitesimal Perturbation Analysis (IPA) to determine on-line optimal threshold parameters through gradient descent and thus obtain optimal controllers within this family of threshold-based policies. Simulations are included to illustrate our results and compare them to optimal solutions derived through dynamic programming. 

We consider the problem of controlling the dynamic state of each of a finite collection of targets distributed in physical space using a much smaller collection of mobile agents. Each agent can attend to no more than one target at a given time, thus agents must move between targets to control the collective state, implying that the states of each of the individual targets are only controlled intermittently. We assume that the state dynamics of each of the targets are given by a linear, timeinvariant, controllable system and develop conditions on the visiting schedules of the agents to ensure that the property of controllability is maintained in the face of the intermittent control. We then introduce constraints on the magnitude of the control input and a bounded disturbance into the target dynamics and develop a method to evaluate system performance under this scenario. Finally, we use this method to determine how the amount of time the agents spend at a given target before switching to the next in its sequence influences 

It is critical in social network analysis to understand the underlying mechanisms of online information diffusion. Although there has been much progress on the influential factors that lead to online viral diffusion, little is known about the impact by public opinion. In this paper, we examine the relations between the public opinion among information propagators and the virality of online diffusion based on a large-scale real-world dataset. We propose a set of new metrics for public opinion in online diffusion to reveal their correlation with diffusion structural virality, and further apply our understanding to predict diffusion virality based on public opinion. The experimental results show the effectiveness of the proposed analysis in the prediction of viral diffusion events. 

Abstract JUNO is a multi-purpose neutrino observatory under construction in the south of China. This publication presents new sensitivity estimates for the measurement of the , , , and oscillation parameters using reactor antineutrinos, which is one of the primary physics goals of the experiment. The sensitivities are obtained using the best knowledge available to date on the location and overburden of the experimental site, the nuclear reactors in the surrounding area and beyond, the detector response uncertainties, and the reactor antineutrino spectral shape constraints expected from the TAO satellite detector. It is found that the and oscillation parameters will be determined to 0.5% precision or better in six years of data collection. In the same period, the parameter will be determined to about % precision for each mass ordering hypothesis. The new precision represents approximately an order of magnitude improvement over existing constraints for these three parameters.