Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks

This paper investigates the use of deep reinforcement learning (DRL) in a MAC protocol for heterogeneous wireless networking referred to as Deep-reinforcement Learning Multiple Access (DLMA). The thrust of this work is partially inspired by the vision of DARPA SC2, a 3-year competition whereby competitors are to come up with a clean-slate design that "best share spectrum with any network(s), in any environment, without prior knowledge, leveraging on machine-learning technique". Specifically, this paper considers the problem of sharing time slots among a multiple of time-slotted networks that adopt different MAC protocols. One of the MAC protocols is DLMA. The other two are TDMA and ALOHA. The nodes operating DLMA do not know that the other two MAC protocols are TDMA and ALOHA. Yet, by a series of observations of the environment, its own actions, and the resulting rewards, a DLMA node can learn an optimal MAC strategy to coexist harmoniously with the TDMA and ALOHA nodes according to a specified objective (e.g., the objective could be the sum throughput of all networks, or a general alpha-fairness objective)

Overcoming Catastrophic Forgetting in Graph Neural Networks

Catastrophic forgetting refers to the tendency that a neural network "forgets" the previous learned knowledge upon learning new tasks. Prior methods have been focused on overcoming this problem on convolutional neural networks (CNNs), where the input samples like images lie in a grid domain, but have largely overlooked graph neural networks (GNNs) that handle non-grid data. In this paper, we propose a novel scheme dedicated to overcoming catastrophic forgetting problem and hence strengthen continual learning in GNNs. At the heart of our approach is a generic module, termed as topology-aware weight preserving~(TWP), applicable to arbitrary form of GNNs in a plug-and-play fashion. Unlike the main stream of CNN-based continual learning methods that rely on solely slowing down the updates of parameters important to the downstream task, TWP explicitly explores the local structures of the input graph, and attempts to stabilize the parameters playing pivotal roles in the topological aggregation. We evaluate TWP on different GNN backbones over several datasets, and demonstrate that it yields performances superior to the state of the art. Code is publicly available at \url{https://github.com/hhliu79/TWP}.Comment: Accepted by AAAI 202

Labor leverage, financial statement comparability, and corporate employment

We examine how labor-induced operating leverage shapes managers' decision to adopt more comparable financial statement. We hypothesize that firms subject to higher labor-induced operating leverage are more likely to adopt more comparable financial statements in order to facilitate more timely employment adjustment which reduces firm risk related to labor leverage. Consistent with our hypothesis, we find that proxies for labor-induced operating leverage, such as labor unions, labor intensity, and labor share are positively related to financial statement comparability. We also find that financial statement comparability increases the sensitivity of hiring to performance change, particularly, for negative operating performance, supporting our notion that financial statement comparability helps managers' timelier labor adjustment. Last, we examine whether the improved comparability prevents massive layoffs thanks to continuously more timely employment adjustment. Consistent with our prediction, we find that comparability reduces the likelihood of large-scale layoffs

Employees’ voluntary disclosures about business outlook and labor investment efficiency

We examine how employee business outlook affects firm-level labor investment efficiency by using data from Glassdoor. We hypothesize that due to the popularity and informativeness of employee voluntary disclosure through social media as a form of crowd wisdom in labor markets, more positive business outlook disclosed by employees can significantly reduce firms’ labor adjustment costs by attracting more job applicants in a timely matter, resulting in higher labor investment efficiency. Consistent with the hypothesis, we document that positive employee business outlook enhances labor investment efficiency by reducing both over-investment and under-investment in labor. Extending our first hypothesis, we also hypothesize and find that when peer firms’ employee business outlook is more positive than that of focal firms, focal firms’ labor adjustment costs increase because of the relative disadvantage in obtaining talented labor in labor markets, resulting in less efficient labor investment. We mitigate the endogeneity concerns by employing sub-sample analysis and using Anti-SLAPP laws as an exogenous shock