Joint Computation and Communication Cooperation for Mobile Edge Computing

This paper proposes a novel joint computation and communication cooperation approach in mobile edge computing (MEC) systems, which enables user cooperation in both computation and communication for improving the MEC performance. In particular, we consider a basic three-node MEC system that consists of a user node, a helper node, and an access point (AP) node attached with an MEC server. We focus on the user's latency-constrained computation over a finite block, and develop a four-slot protocol for implementing the joint computation and communication cooperation. Under this setup, we jointly optimize the computation and communication resource allocation at both the user and the helper, so as to minimize their total energy consumption subject to the user's computation latency constraint. We provide the optimal solution to this problem. Numerical results show that the proposed joint cooperation approach significantly improves the computation capacity and the energy efficiency at the user and helper nodes, as compared to other benchmark schemes without such a joint design.Comment: 8 pages, 4 figure

The Effect of Corporate Governance on Firm Value of Listed Firms in China

The study’s aim was to investigate the impact that corporate governance poses upon the firm value of the Chinese listed firms in the stock exchange markets. The structures of the owners and the board were used as proxies of corporate governance. Under ownership structure, the study focused on institutional ownership, ownership concentration, and managerial ownership. Under the board structure, this study adopted board size, independence, as well as gender diversity as indicators. The study used data that was collected for the period 2015-2020 and analysed this data using descriptive statistics, correlation, and regression analysis. This considered a random sample of 2,570 firms with 15,420 observations over six years. The findings of the study showed that institutional ownership significantly and positively influences the value of listed companies in China. Managerial ownership was found to pose no impact on value, while ownership concentration was found to negatively influence Chinese listed firms’ value. This study further established that board independence and size generate a significant positive effect on firm value. However, it was found that board gender diversity in Chinese generates no significant impact. This study recommends that the listed firms in China enhance board size and independence to enhance firm value. However, a larger board size should reflect diverse skills set and experience pertinent to driving firm value. Institutional ownership should be encouraged, especially that institutional investors come with knowledge and experience critical in guiding investment and risk management decisions

Learning to learn graph topologies

Learning a graph topology to reveal the underlying relationship between data entities plays an important role in various machine learning and data analysis tasks. Under the assumption that structured data vary smoothly over a graph, the problem can be formulated as a regularised convex optimisation over a positive semidefinite cone and solved by iterative algorithms. Classic methods require an explicit convex function to reflect generic topological priors, e.g. the ℓ1 penalty for enforcing sparsity, which limits the flexibility and expressiveness in learning rich topological structures. We propose to learn a mapping from node data to the graph structure based on the idea of learning to optimise (L2O). Specifically, our model first unrolls an iterative primal-dual splitting algorithm into a neural network. The key structural proximal projection is replaced with a variational autoencoder that refines the estimated graph with enhanced topological properties. The model is trained in an end-to-end fashion with pairs of node data and graph samples. Experiments on both synthetic and real-world data demonstrate that our model is more efficient than classic iterative algorithms in learning a graph with specific topological properties