THE NUMBER OF WOMEN IN THE BOARD AND CORPORATE FINANCIAL PERFORMANCE IN CHINA

Incorporating more women into the board of directors is often regarded as a correct business decision, which breaks the traditional “glass ceiling” phenomenon. In general, senior managers believe that women directors in the board can provide unique insights on how an entity is perceived, thereby facilitating more open discussion among board members and improving the relationship between employees and board members. The purpose of this study was to explore whether gender differences in the board could improve the financial result of 1,589 A-share listed companies in China between 2013 and 2018. In this study, quantitative research method was adopted on the basis of secondary data collected from the official CSMAR database, which covered the board composition and the performance of the companies. Microsoft Excel and STATA 16.0 software were used to analyze the data through systematic review, so as to achieve the research objectives. The results demonstrated that gender differences can promote the financial performance of Chinese listed companies. The educational level of women directors is in direct proportion to the company’s financial performance. In addition, in China, if two women exist on the board, women as the minority have the greatest influence on the company. Therefore, public listed companies should put more attention on the right combination of men and women, instead of merely relying on the presence of no more than one woman to increase company performance. This study provides new insights into China’s board dynamics. Corporate performance should be measured both quantitatively and qualitatively in future research

Multiagent Reinforcement Learning with an Attention Mechanism for Improving Energy Efficiency in LoRa Networks

Long Range (LoRa) wireless technology, characterized by low power consumption and a long communication range, is regarded as one of the enabling technologies for the Industrial Internet of Things (IIoT). However, as the network scale increases, the energy efficiency (EE) of LoRa networks decreases sharply due to severe packet collisions. To address this issue, it is essential to appropriately assign transmission parameters such as the spreading factor and transmission power for each end device (ED). However, due to the sporadic traffic and low duty cycle of LoRa networks, evaluating the system EE performance under different parameter settings is time-consuming. Therefore, we first formulate an analytical model to calculate the system EE. On this basis, we propose a transmission parameter allocation algorithm based on multiagent reinforcement learning (MALoRa) with the aim of maximizing the system EE of LoRa networks. Notably, MALoRa employs an attention mechanism to guide each ED to better learn how much ''attention'' should be given to the parameter assignments for relevant EDs when seeking to improve the system EE. Simulation results demonstrate that MALoRa significantly improves the system EE compared with baseline algorithms with an acceptable degradation in packet delivery rate (PDR).Comment: 6 pages, 3 figures, This paper has been accepted for publication in IEEE Global Communications Conference (GLOBECOM) 202

Sampling-accelerated First-principles Prediction of Phonon Scattering Rates for Converged Thermal Conductivity and Radiative Properties

First-principles prediction of thermal conductivity and radiative properties is crucial. However, computing phonon scattering, especially for four-phonon scattering, could be prohibitively expensive, and the thermal conductivity even for silicon was still under-predicted and not converged in the literature. Here we propose a method to estimate scattering rates from a small sample of scattering processes using maximum likelihood estimation. The computational cost of estimating scattering rates and associated thermal conductivity and radiative properties is dramatically reduced by over 99%. This allows us to use an unprecedented q-mesh of 32*32*32 for silicon and achieve a converged thermal conductivity value that agrees much better with experiments. The accuracy and efficiency of our approach make it ideal for the high-throughput screening of materials for thermal and optical applications

Linking a predictive model to causal effect estimation

A predictive model makes outcome predictions based on some given features, i.e., it estimates the conditional probability of the outcome given a feature vector. In general, a predictive model cannot estimate the causal effect of a feature on the outcome, i.e., how the outcome will change if the feature is changed while keeping the values of other features unchanged. This is because causal effect estimation requires interventional probabilities. However, many real world problems such as personalised decision making, recommendation, and fairness computing, need to know the causal effect of any feature on the outcome for a given instance. This is different from the traditional causal effect estimation problem with a fixed treatment variable. This paper first tackles the challenge of estimating the causal effect of any feature (as the treatment) on the outcome w.r.t. a given instance. The theoretical results naturally link a predictive model to causal effect estimations and imply that a predictive model is causally interpretable when the conditions identified in the paper are satisfied. The paper also reveals the robust property of a causally interpretable model. We use experiments to demonstrate that various types of predictive models, when satisfying the conditions identified in this paper, can estimate the causal effects of features as accurately as state-of-the-art causal effect estimation methods. We also show the potential of such causally interpretable predictive models for robust predictions and personalised decision making.Comment: 1