Learning Unmanned Aerial Vehicle Control for Autonomous Target Following

While deep reinforcement learning (RL) methods have achieved unprecedented successes in a range of challenging problems, their applicability has been mainly limited to simulation or game domains due to the high sample complexity of the trial-and-error learning process. However, real-world robotic applications often need a data-efficient learning process with safety-critical constraints. In this paper, we consider the challenging problem of learning unmanned aerial vehicle (UAV) control for tracking a moving target. To acquire a strategy that combines perception and control, we represent the policy by a convolutional neural network. We develop a hierarchical approach that combines a model-free policy gradient method with a conventional feedback proportional-integral-derivative (PID) controller to enable stable learning without catastrophic failure. The neural network is trained by a combination of supervised learning from raw images and reinforcement learning from games of self-play. We show that the proposed approach can learn a target following policy in a simulator efficiently and the learned behavior can be successfully transferred to the DJI quadrotor platform for real-world UAV control

Accounting Estimates: Pervasive, Yet of Questionable Usefulness

Estimates and projections are embedded in most financial statement items. These estimates potentially improve the relevance of financial information by providing managers the means to convey to investors forward-looking, inside information (e.g., on future collections from customers via the bad debt provision, or on expected assets' cash flows reflected in impairment charges). On the other hand, the quality of financial information is compromised by: (i) the increasing difficulty of making reliable forecasts in a fast-changing, often turbulent economy, and (ii) the frequent managerial misuse of estimates to manipulate financial data. Given the prevalence of estimates in accounting data, whether these opposing forces result in an improvement in the quality of financial information or not is arguably the most fundamental issue in accounting. We examine in this study the contribution of accounting estimates embedded in accruals to the quality of financial information by focusing on the major use of this information by investors - the prediction of enterprise cash flows and earnings. Our extensive tests, reflecting both the statistical and economic significance of estimates, indicate that, by and large, accounting accruals and the estimates they embed do not improve the quality of financial information in terms of enhancing the prediction of enterprise performance. Accruals do not improve the prediction of cash flows, beyond that achieved by current cash flows, and improve only marginally the prediction of earnings. This latter improvement, however, appears to be economically insignificant. Thus, the objective difficulties of generating reliable estimates and projections in a volatile economy, and their frequent misuse by managers appear to offset the positive role of estimates in conveying forward looking information to investors

Modeling the Impacts of Compound Dry and Hot Extremes on Australia's Wheat

University of Technology Sydney. Faculty of Science.Compound dry and hot events, which refer to the simultaneous occurrence of drought and heat events in both time and space, have garnered increasing attention over recent decades due to their amplified detrimental effects on agricultural production compared to the impacts of either drought or heat alone. In Australia, rain-fed wheat production is particularly vulnerable, experiencing significant yield fluctuations or losses due to recurrent extreme weather conditions. While the effects of individual events like drought or heat on wheat yields have been extensively quantified, the compound dry and hot remains less understood. In this study, four interconnected studies were conducted: assessing the climate vulnerability of wheat in Australia, projecting future changes in frequency and intensity compound dry-hot events, quantifying their contribution to yield variability, and analyzing their relationships with climate drivers such as El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). Findings indicate that compound events amplify yield variability markedly, with projections showing increased occurrence under future climate change. Strategies such as adjusting sowing times and cultivar selection emerge as effective mitigation measures. Integrating these insights into climate risk management frameworks is essential for enhancing agricultural resilience in the face of changing climate conditions

Cold-start Sequential Recommendation via Meta Learner

This paper explores meta-learning in sequential recommendation to alleviate the item cold-start problem. Sequential recommendation aims to capture user's dynamic preferences based on historical behavior sequences and acts as a key component of most online recommendation scenarios. However, most previous methods have trouble recommending cold-start items, which are prevalent in those scenarios. As there is generally no side information in the setting of sequential recommendation task, previous cold-start methods could not be applied when only user-item interactions are available. Thus, we propose a Meta-learning-based Cold-Start Sequential Recommendation Framework, namely Mecos, to mitigate the item cold-start problem in sequential recommendation. This task is non-trivial as it targets at an important problem in a novel and challenging context. Mecos effectively extracts user preference from limited interactions and learns to match the target cold-start item with the potential user. Besides, our framework can be painlessly integrated with neural network-based models. Extensive experiments conducted on three real-world datasets verify the superiority of Mecos, with the average improvement up to 99%, 91%, and 70% in HR@10 over state-of-the-art baseline methods.Comment: Accepted at AAAI 202

Disclosure of Information in Chinese Juridical Field: Information & Information Technology?

As Chinese government has invested a lot in information technology within judicial field, authorities in the justice system made great efforts to improve their platforms to provide information to public. Information disclosed online serves as an important way for the public to know the government’s movements. This study aims to find out if there are any improvements to disclosure of information with such heavy investment on information technology, by analyzing data collected from 3 representative authorities within a quantitative and qualitative framework, which is specified by literature review and Core Index Evaluation System of Government Website Development issued by Ministry of Industry and Information Technology