Self-Supervised Curriculum Generation for Autonomous Reinforcement Learning without Task-Specific Knowledge

A significant bottleneck in applying current reinforcement learning algorithms to real-world scenarios is the need to reset the environment between every episode. This reset process demands substantial human intervention, making it difficult for the agent to learn continuously and autonomously. Several recent works have introduced autonomous reinforcement learning (ARL) algorithms that generate curricula for jointly training reset and forward policies. While their curricula can reduce the number of required manual resets by taking into account the agent's learning progress, they rely on task-specific knowledge, such as predefined initial states or reset reward functions. In this paper, we propose a novel ARL algorithm that can generate a curriculum adaptive to the agent's learning progress without task-specific knowledge. Our curriculum empowers the agent to autonomously reset to diverse and informative initial states. To achieve this, we introduce a success discriminator that estimates the success probability from each initial state when the agent follows the forward policy. The success discriminator is trained with relabeled transitions in a self-supervised manner. Our experimental results demonstrate that our ARL algorithm can generate an adaptive curriculum and enable the agent to efficiently bootstrap to solve sparse-reward maze navigation tasks, outperforming baselines with significantly fewer manual resets.Comment: 8 pages, 5 figure

DPRS transformer - Dynamic pressure resistant system - Part I

In general, a transformer is designed and manufactured to operate under normal conditions. However, unexpected fault events occur due to various reasons in real-life substations. When such events do occur, an electric arc inside a transformer vaporizes the insulating oil, leading to a generation of very high expansion pressure. Once this pressure exceeds the designed threshold, the tank is then compromised, and oil starts to leak, becoming a potential cause of fire or explosion. DPRS (Dynamic Pressure Resistant System) transformer has been developed to cope with such unexpected events. In general, a PRD (Pressure Relief Device) is installed on a transformer to stabilize the pressure inside the tank. However, it requires a certain amount of time for this device to operate. DPRS transformer is designed to withstand the immediate pressure increase without severely damaging the tank (severe enough to cause an oil leak) until the PRD starts operating. Although not as much as to cause a leak, the tank will still be deformed as a result of the pressure increase. Then, insulating oil expanded by the arc is emitted safely through a designated path as the PRD starts to operate. DPRS transformer does not require additional equipment to prevent damage to the tank and is also capable of preventing fire while maintaining a similar configuration to common transformers. Due to these merits, the global demand for DPRS transformers is steadily increasing. In this article, the DPRS transformer tank design procedure and tank deformation prediction technology are presented. Additionally, a brief introduction to the explosion-proof performance verification test is addressed

A Magneto-Mechanical Piezoelectric Energy Harvester Designed to Scavenge AC Magnetic Field from Thermal Power Plant with Power-Line Cables

Piezoelectric energy harvesters have attracted much attention because they are crucial in portable industrial applications. Here, we report on a high-power device based on a magneto-mechanical piezoelectric energy harvester to scavenge the AC magnetic field from a power-line cable for industrial applications. The electrical output performance of the harvester (×4 layers) reached an output voltage of 60.8 Vmax, an output power of 215 mWmax (98 mWrms), and a power density of 94.5 mWmax/cm3 (43.5 mWrms/cm3) at an impedance matching of 5 kΩ under a magnetic field of 80 μT. The multilayer energy harvester enables high-output performance, presenting an obvious advantage given this improved level of output power. Finite element simulations were also performed to support the experimental observations. The generator was successfully used to power a wireless sensor network (WSN) for use on an IoT device composed of a temperature sensor in a thermal power station. The result shows that the magneto-mechanical piezoelectric energy harvester (MPEH) demonstrated is capable of meeting the requirements of self-powered monitoring systems under a small magnetic field, and is quite promising for use in actual industrial applications

Increased Risk of Dementia in Patients with Atopic Dermatitis: A Nationwide Population-Based Cohort Study

Atopic dermatitis (AD) is a chronic inflammatory skin disorder with bimodal incidence peaks in early childhood and middle-aged and older adults. Few studies have focused on the risk of dementia in AD. The aims of this study were to analyse the incidence, and risk factors for dementia in patients with AD. This nationwide population-based retrospective cohort study enrolled 38,391 adults ≥ 40 years of age with AD and 2,643,602 controls without AD from the Korean National Health Insurance System (NHIS) database from 2009 to 2016. The cumulative incidence probability of all-cause dementia, Alzheimer\u27s disease, or vascular dementia at 8 years was 50, 39, and 7 per 1,000 person-years in patients with AD, respectively. The adjusted risks of all-cause dementia (hazard ratio (HR), 1.072; 95% confidence interval (95% CI) 1.026-1.120), and Alzheimer\u27s disease (HR 1.051; 95% CI 1.000-1.104) were increased in patients with AD. The effect of AD on the development of all-cause dementia and Alzheimer\u27s dementia varied according to age and diabetes mellitus (all p for interaction, \u3c 0.05). The risks of all-cause dementia and Alzheimer\u27s disease were increased in patients with AD. Management of modifiable risk factors is important for preventing dementia in patients with AD

The effect of CEO turnover on audit report lag and management discretionary report lag: evidence from Korea

This study empirically investigates the effect of a CEO turnover on audit report lag (ARL), discretionary report lag (DRL) and total report lag (TRL). The object of this study is to provide empirical evidence for the responses of both the CEO and the external auditor on audit risk increases and information asymmetry that occur as a result of a CEO turnover. According to the previous study on CEO turnovers, the CEO turnover would increase audit risk and information asymmetry (Sohn et al., 2014). In this situation, the CEO has an incentive to provide timely information to decrease the monitoring costs and cost of debt (Lee et al., 2008). It is expected that an external auditor spends a large amount of time on audit procedures to lower the audit risk when the CEO changes. Therefore, the CEO turnover would have a conflicting effect on the ARL and DRL. The results of the analysis are as follows. First, the ARL increases and DRL decreases when the CEO changes, which suggests that an external auditor spends a great amount of time on audit procedures to lower the audit risk because the audit risk increases when the CEO changes. A new CEO provides information faster to reduce monitoring costs and cost of debt that occur due to information asymmetry. Second, the ARL increases and DRL decreases as the frequency of CEO turnover increases. An external auditor would estimate the audit risk as being high if the CEO changes more frequently. To lower the audit risk to an acceptable level, many audit hours are spent on audit procedures by an external auditor, which increases the ARL. A new CEO has an incentive to provide timely information when the CEO changes more frequently. Thus, the DRL decreases as the frequency of CEO turnover increase

Tau functions as Widom constants

We define a tau function for a generic Riemann-Hilbert problem posed on a union of non-intersecting smooth closed curves with jump matrices analytic in their neighborhood. The tau function depends on parameters of the jumps and is expressed as the Fredholm determinant of an integral operator with block integrable kernel constructed in terms of elementary parametrices. Its logarithmic derivatives with respect to parameters are given by contour integrals involving these parametrices and the solution of the Riemann-Hilbert problem. In the case of one circle, the tau function coincides with Widom's determinant arising in the asymptotics of block Toeplitz matrices. Our construction gives the Jimbo-Miwa-Ueno tau function for Riemann-Hilbert problems of isomonodromic origin (Painlev\'e VI, V, III, Garnier system, etc) and the Sato-Segal-Wilson tau function for integrable hierarchies such as Gelfand-Dickey and Drinfeld-Sokolov.Comment: 26 pages, 6 figure