Much recent research has shown that network structure and human mobility have great influences on epidemic spreading. In this paper, we propose a discrete-time Markov chain method to model susceptible-infected-susceptible epidemic dynamics in heterogeneous networks. There are two types of locations, residences and common places, for which different infection mechanisms are adopted. We also give theoretical results about the impacts of important factors, such as mobility probability and isolation, on epidemic threshold. Numerical simulations are conducted, and experimental results support our analysis. In addition, we find that the dominations of different types of residences might reverse when mobility probability varies for some networks. In summary, the findings are helpful for policy making to prevent the spreading of epidemics.

The electric vehicle (EV) utilizing vehicle-to-grid (V2G) technology can serve as an important mobile storage in a microgrid of building. Since more and more buildings are equipped with charging piles and distributed renewable energy, it can potentially improve the operation efficiency of building microgrid and reduce the impact of EVs to the grid by V2G scheduling considering the uncertain supply and EV charging demand in the building. We consider this important problem in this paper and make the following contributions. First, we formulate this V2G scheduling problem as a constrained Markov decision process (CMDP). The objective function is to improve the overall building energy operation cost while constraining the expected cycling time to ensure the participation enthusiasm of the EV users. Second, a simulation-based primal-dual approach is developed to decompose the original problem into a continuous optimization subproblem on the supply side and a discrete optimization subproblem on the demand side. The demand side optimization can be further decoupled as a distributed single EV scheduling problem where simulation-based regularized rollout method can be applied to improve from existing base policies. Third, the structural property of the problem and the cost improvement of the proposed method are analyzed to speed up the optimization and ensure the solution quality. Numerical experiments based on real building load data and EV data are conducted to demonstrate the performance of this method. Note to Practitioners—With the rapid adoption of EVs in the microgrid of building, there goes the challenge of how to reduce their charging impact to the microgrid. The V2G scheduling between EVs and building microgrid can provide a promising way to reduce the impact and improve the building operation efficiency. However, it suffers from the uncertain EV charging demand and the conflict between synthesized coordination and ensuring the participation enthusiasm of drivers. In this paper, we propose a constrained V2G scheduling model to address this issue. After formulating the problem as a constrained Markov decision process, a simulation-based primal-dual approach is developed to decompose the problem where the derived control policy in the supply side can be deployed in the building energy controller and the derived control policy in the demand side can be deployed in the controller of each charging pile. Case studies show the performance of the proposed method and its win-win property for the building and EV users.

This paper studies the edge computing system (ECS) in which caching the frequently reusable service (FRS) at the edge server (ES) is an effective way to reduce delays. The larger cache space available in the ES (we call it ES cache space) might buffer the larger scale of FRS, and subsequently decrease the delays while bringing higher cache storage costs. Meanwhile, the distribution of FRS is not always known in advance. Therefore, how much ES cache space should be supplied to make the optimal tradeoff between the delays and cache storage costs arises as an interesting issue in practice. To address this issue, this paper first formulates the problem of determining the amount of ES cache space supply as a constrained Markov decision process (CMDP), then adopts the Zipf’s distribution to estimate the probability distribution of FRS, and finally proposes an effective cache space control algorithm (CSCA) guiding the ES to determine the amount of ES cache space supply to minimize the cache storage costs while maintaining the delays at the acceptable level. Theoretical analysis, simulations and field experiments document and illustrate its performance. Note to Practitioners —This paper addresses the interesting trade-off between the delays and cache storage costs for the edge computing system that operates with limited cache storage budgets while must satisfy the required real-time performances. It helps to improve the operation efficiency of the systems with edge computing setting in the area of Internet of Things (IoT) or Cyber-Physical Systems (CPS) that employ the edge server to cache the frequently reusable services, arriving at the minimization of the accumulative cache storage costs while maintaining the accumulative delays at the acceptable level. Theoretical analysis, simulation and field experimental investigations jointly show that the solution proposed here outperforms existing solutions.

Topic models are widely explored for summarizing a corpus of documents. Recent advances in Variational AutoEncoder (VAE) have enabled the development of black-box inference methods for topic modeling in order to alleviate the drawbacks of classical statistical inference. Most existing VAE based approaches assume a unimodal Gaussian distribution for the approximate posterior of latent variables, which limits the flexibility in encoding the latent space. In addition, the unsupervised architecture hinders the incorporation of extra label information, which is ubiquitous in many applications. In this paper, we propose a semi-supervised topic model under the VAE framework. We assume that a document is modeled as a mixture of classes, and a class is modeled as a mixture of latent topics. A multimodal Gaussian mixture model is adopted for latent space. The parameters of the components and the mixing weights are encoded separately. These weights, together with partially labeled data, also contribute to the training of a classifier. The objective is derived under the Gaussian mixture assumption and the semi-supervised VAE framework. Modules of the proposed framework are appropriately designated. Experiments performed on three benchmark datasets demonstrate the effectiveness of our method, comparing to several competitive baselines.

This paper considers the cloud-based multi-agent system (MAS) in which for any agent to obtain the most trustworthy data (MTD) that best matches the agents’ personalized demands on the trusted attributes of data, the agents’ data usage often comes with cost, and the better the trustworthiness of data (TOD), the higher the cost of data usage (CDU). Therefore, under the environments where the budgets for data usage are constrained, there may exist a trade-off between the TOD and CDU for the cloud-based MAS, and how to optimally make such trade-off arises as an interesting issue in practice. To address this issue, this paper first formulates the trade-off as a mathematical constrained optimization problem, then explores out the theoretical foundations, both using the fuzzy similarity computing, and finally proposes an algorithm to guarantee the agents to obtain the MTD within the acceptable level of CDU. The experiment results illustrate its effectiveness. Note to Practitioners — This paper addresses the interesting trade-off between TOD and CDU for the cloud-based MAS that operates in the cloud environments where the TOD and CDU are positively correlated with each other. By establishing the mathematical constrained optimization model and designing the algorithm for the MTD recommendation within the acceptable CDU based on the theoretical foundations using fuzzy similarity computing, it helps the MAS with cloud settings to achieve the MTD that best matches their personalized demands on the trusted attributes within the acceptable CDU, which could push the development of cloud-based MAS by benefiting from the optimal trade-off between TOD and CDU. The experiments show that the solution proposed here outperforms existing solutions.