Task Transfer by Preference-Based Cost Learning

The goal of task transfer in reinforcement learning is migrating the action policy of an agent to the target task from the source task. Given their successes on robotic action planning, current methods mostly rely on two requirements: exactly-relevant expert demonstrations or the explicitly-coded cost function on target task, both of which, however, are inconvenient to obtain in practice. In this paper, we relax these two strong conditions by developing a novel task transfer framework where the expert preference is applied as a guidance. In particular, we alternate the following two steps: Firstly, letting experts apply pre-defined preference rules to select related expert demonstrates for the target task. Secondly, based on the selection result, we learn the target cost function and trajectory distribution simultaneously via enhanced Adversarial MaxEnt IRL and generate more trajectories by the learned target distribution for the next preference selection. The theoretical analysis on the distribution learning and convergence of the proposed algorithm are provided. Extensive simulations on several benchmarks have been conducted for further verifying the effectiveness of the proposed method.Comment: Accepted to AAAI 2019. Mingxuan Jing and Xiaojian Ma contributed equally to this wor

Research and application of maximum surface subsidence model under the condition of repeated mining in weakly cemented strata

The characteristics of surface subsidence under the condition of repeated mining in weakly cemented strata are of great significance to the safe and efficient mining and ecological restoration of coal resources in weakly cemented mining areas in western China. Theoretical analysis, similar simulation, numerical simulation and field monitoring are used to study the migration law of overlying strata and surface subsidence model under repeated mining conditions in weakly cemented strata, and the model is applied in engineering. The bulking characteristics of weakly cemented rock and the influence mechanism of repeated mining overburden strata movement on surface subsidence are discussed through theoretical analysis. The ‘maximum surface subsidence model under the condition of repeated mining in weakly cemented strata’ is established. There is a linear relationship between the bulking coefficient of weakly cemented rock, the mining thickness of lower coal and the maximum surface subsidence of weakly cemented strata. Through similar simulation and numerical simulation, the characteristics of repeated mining overburden and surface subsidence in weakly cemented strata are analyzed. The research results show that the development law of the separation height of the initial mining and repeated mining of the weakly cemented strata is basically the same, and both show a step-like rise. The surface subsidence curve of repeated mining is asymmetrically distributed, and the maximum subsidence value is biased towards the side of open cut. The maximum development height of overlying strata, the maximum surface subsidence value and the surface subsidence coefficient after initial mining and repeated mining are given. The established maximum surface subsidence model is used to predict the maximum surface subsidence value on site. The predicted value of the maximum surface subsidence model is similar to the measured value on site during the mining process of the working face, which verifies the rationality of the ' maximum surface subsidence model under the condition of repeated mining of weakly cemented strata '. At the same time, the predicted value of the maximum surface subsidence after the mining of the working face can provide a reference for the actual work on site

LogPrompt: Prompt Engineering Towards Zero-Shot and Interpretable Log Analysis

Automated log analysis is crucial in modern software-intensive systems for ensuring reliability and resilience throughout software maintenance and engineering life cycles. Existing methods perform tasks such as log parsing and log anomaly detection by providing a single prediction value without interpretation. However, given the increasing volume of system events, the limited interpretability of analysis results hinders analysts' trust and their ability to take appropriate actions. Moreover, these methods require substantial in-domain training data, and their performance declines sharply (by up to 62.5%) in online scenarios involving unseen logs from new domains, a common occurrence due to rapid software updates. In this paper, we propose LogPrompt, a novel zero-shot and interpretable log analysis approach. LogPrompt employs large language models (LLMs) to perform zero-shot log analysis tasks via a suite of advanced prompt strategies tailored for log tasks, which enhances LLMs' performance by up to 107.5% compared with simple prompts. Experiments on nine publicly available evaluation datasets across two tasks demonstrate that LogPrompt, despite using no training data, outperforms existing approaches trained on thousands of logs by up to around 50%. We also conduct a human evaluation of LogPrompt's interpretability, with six practitioners possessing over 10 years of experience, who highly rated the generated content in terms of usefulness and readability (averagely 4.42/5). LogPrompt also exhibits remarkable compatibility with open-source and smaller-scale LLMs, making it flexible for practical deployment

Effects of vegetation on the structure and diversity of soil bacterial communities in the Arctic tundra

The relatively simple vegetation of the Arctic tundra provides an ideal site in which to study the relationships between plants, bacterial communities and soil chemistry. Here, results of 16S rRNA gene sequencing of secondary Arctic brown soils collected from underneath colonies of Dryasoctopetala, Luzulaconfusa and Bistortavivipara in the Arctic tundra near Ny-Ålesund, Svalbard, Norway, reveal significant differences in bacterial communities related to soil environmental properties. Redundancy analysis shows that all measured geochemical factors were significant in structuring microbiomes, with strong correlations related to soil pH and organic matter contents. Vegetation is likely to affect the physical and chemical properties of the soil, which in turn affects the bacterial community and composition of the soil

Yi Qi Qing Re Gao Attenuates Podocyte Injury and Inhibits Vascular Endothelial Growth Factor Overexpression in Puromycin Aminonucleoside Rat Model

Proteinuria is the hallmark of chronic kidney disease. Podocyte damage underlies the formation of proteinuria, and vascular endothelial growth factor (VEGF) functions as an autocrine/paracrine regulator. Yi Qi Qing Re Gao (YQQRG) has been used to treat proteinuria for more than two decades. The objective of this study was to investigate the protective effect and possible mechanisms of YQQRG on puromycin aminonucleoside (PAN) rat model. Eighty male Sprague-Dawley rats were randomized into sham group, PAN group, PAN + YQQRG group, and PAN + fosinopril group. Treatments were started 7 days before induction of nephrosis (a single intravenous injection of 40 mg/kg PAN) until day 15. 24 h urinary samples were collected on days 5, 9, and 14. The animals were sacrificed on days 3, 10, and 15, respectively. Blood samples and renal tissues were obtained for detection of biochemical and molecular biological parameters. YQQRG significantly reduced proteinuria, elevated serum albumin, and alleviated renal pathological lesions. YQQRG inhibited VEGF-A, nephrin, podocin, and CD2AP mRNA expression and elevated nephrin, podocin, and CD2AP protein levels starting on day 3. In conclusion, YQQRG attenuates podocyte injury in the rat PAN model through downregulation of VEGF-A and restoration of nephrin, podocin, and CD2AP protein expression