Cultural, Social and Family Shadows: Finding a Place in the Rainbow

Due to Chinese traditions, certain living environments are not friendly towards the LGBT community in China, who experience immense pressure to keep silent in society. They often are discriminated against, and in the case of most, their families do not support them. It is difficult to have healthy self-identification for sexual minorities. Besides cultural and family pressure, and representation in media, the current legal framework and society are unfriendly to this community. There is no legislation on homosexuality in China at present, and China does not make any clear provisions on homosexual marriage. In this environment, most LGBT people are not willing to disclose their identity or situation to other people. Some LGBTs enter fake marriages. Some in fake marriages cheat on their wives about their sexual orientation, and their wives cannot live in a regular marriage. However, in recent years, the environment has become more flexible. Due to education and the Internet, the new generation is changing its attitude to the LGBT group, and more and more sexual minorities are trying to come out. Based on these changing conditions, there is a potential developing market, named the pink economy, which is the economy produced by the LGBT population. As the LGBT community grows, the pink economy has emerged, offering Internet social platforms, homosexual bars, and specific travel programs for the group. The pink economy greatly enhances the visibility of the LGBT community and attempts to use the power of commerce to increase public acceptance to the LGBT people

Efficient Multi-Task and Transfer Reinforcement Learning with Parameter-Compositional Framework

In this work, we investigate the potential of improving multi-task training and also leveraging it for transferring in the reinforcement learning setting. We identify several challenges towards this goal and propose a transferring approach with a parameter-compositional formulation. We investigate ways to improve the training of multi-task reinforcement learning which serves as the foundation for transferring. Then we conduct a number of transferring experiments on various manipulation tasks. Experimental results demonstrate that the proposed approach can have improved performance in the multi-task training stage, and further show effective transferring in terms of both sample efficiency and performance.Comment: 8 pages, accepted by IEEE Robotics and Automation Letters (RA-L

Learn and Transfer Knowledge of Preferred Assistance Strategies in Semi-autonomous Telemanipulation

Enabling robots to provide effective assistance yet still accommodating the operator's commands for telemanipulation of an object is very challenging because robot's assistive action is not always intuitive for human operators and human behaviors and preferences are sometimes ambiguous for the robot to interpret. Although various assistance approaches are being developed to improve the control quality from different optimization perspectives, the problem still remains in determining the appropriate approach that satisfies the fine motion constraints for the telemanipulation task and preference of the operator. To address these problems, we developed a novel preference-aware assistance knowledge learning approach. An assistance preference model learns what assistance is preferred by a human, and a stagewise model updating method ensures the learning stability while dealing with the ambiguity of human preference data. Such a preference-aware assistance knowledge enables a teleoperated robot hand to provide more active yet preferred assistance toward manipulation success. We also developed knowledge transfer methods to transfer the preference knowledge across different robot hand structures to avoid extensive robot-specific training. Experiments to telemanipulate a 3-finger hand and 2-finger hand, respectively, to use, move, and hand over a cup have been conducted. Results demonstrated that the methods enabled the robots to effectively learn the preference knowledge and allowed knowledge transfer between robots with less training effort

Improvements on Recommender System based on Mathematical Principles

In this article, we will research the Recommender System's implementation about how it works and the algorithms used. We will explain the Recommender System's algorithms based on mathematical principles, and find feasible methods for improvements. The algorithms based on probability have its significance in Recommender System, we will describe how they help to increase the accuracy and speed of the algorithms. Both the weakness and the strength of two different mathematical distance used to describe the similarity will be detailed illustrated in this article

Impact of water sediment diversion and afflux on erosion deposition in the Luoshan Hankou reach, 1 middle Yangtze River, China

It is not yet fully understood how water-sediment diversion and afflux along a mainstream reach of a river affect erosion-deposition in downstream reaches. This study focuses on the Luoshan-Hankou mainstream reach of the middle Yangtze River, China. The Luoshan-Hankou reach is vitally important for flood control, being located downstream of three diversion mouths and an afflux outlet along the Jingjiang reach. We establish empirical formulae for sediment transport rates at boundary cross-sections, and hence estimate the amount and proportion of erosion-deposition and its relative increase (termed erosion-deposition promotion) in the Luoshan-Hankou reach. We then propose critical net water supplies from Dongting Lake to Luoshan-Hankou reach based on maxima and equilibria of erosion-deposition and its promotion. It is found that net water supply partly drives erosion-deposition in the Luoshan-Hankou reach where maximal proportions of deposition and deposition-promotion may be approximated by 0.01c-37.67 and 0.01c-37.67 + c-1, in which c is a dimensionless parameter representing the erosion-deposition condition in Luoshan-Hankou reach for no water-sediment exchange. At Zhicheng hydrological station, the critical ratio of net water supply to overall water discharge is 0.418c-33.33-1, and critical net water supply ratios for equilibria of erosion-deposition and its promotion are −1 (or c-33.33-1) and 0 (or (0.06 + 3.257c54.61)-1-1). A chart based on net water supply and c is devised representing four types of erosion–deposition and its promotion for the Luoshan-Hankou reach. Historical data over the past 65 years demonstrate that erosion-deposition and its promotion in the reach are respectively governed by c and net water supply; there is a remarkable shift from alternate erosion-deposition to monotonic erosion whilst the erosion-deposition effect remains consistent. The foregoing are in agreement with observed data, and comparable with data for the Jingjiang reach (affected by the three water-sediment diversion mouths). Satisfactory flood-control conditions in the convergence zone between the Yangtze mainstream and Dongting Lake accompanied by increasing erosion in the Luoshan-Hankou reach are predicted for the future

Ultrafine-Grained Materials Fabrication with High Pressure Torsion and Simulation of Plastic Deformation Inhomogeneous Characteristics

Utilization of severe plastic deformation (SPD) methods has provided a convenient approach for producing ultrafine-grained (UFG) materials exhibiting outstanding characteristics especially mechanical properties. HPT as one of the SPD methods can lead both to smaller grains and to a higher fraction of high-angle grain boundaries, which is an especially attractive procedure by researchers. In order to understand the nonlinearities relationship between the mechanical properties and the developed strain during plastic deformation, local deformation analysis using the finite element methodwas applied for the HPT process. In this chapter, results are reported of an investigation on the deformed microstructure and mechanical properties of different materials samples during the HPT process using experiments and FEM simulations. Simulation results indicate that the disks show inhomogeneity development and distribution of strain and stress during the plastic deformation. Microstructure and hardness investigation results can give a well support to verify the rules of inhomogenous plastic deformation in the early stage of the HPT disks. Furthermore, the friction and anvil geometry play important roles in the homogeneity of the deformation. After the hollow cone high pressure torsion (HC-HPT), the thermal stability of Zr64.13Cu15.75Ni10.12Al10 BMGs is enhanced, while the elastic modulus of BMG will be decreased

First record of the genus Conotalopia Iredale, 1929 (Vetigastropoda, Trochidae) in China

The genus Conotalopia Iredale, 1929 consisting of marine trochids, primarily inhabits the intertidal zone. Globally, eight recent species have been documented, all of which occur in the Pacific Region. The genus has not previously been recorded from Chinese seas.This study fills a knowledge gap by reporting, for the first time, the presence of the trochid genus Conotalopia Iredale, 1929 in China. Specifically, Conotalopia sematensis (Oyama, 1942) was detailed using morphological characteristics derived from the shell (Fig. 1A-F and H-I), operculum (Fig. 1G) and radula (Fig. 1J-L). Additionally, this study introduces comprehensive scanning electron microscope illustrations and molecular data, contributing valuable taxonomic information for the first time

The Language Barrier: Dissecting Safety Challenges of LLMs in Multilingual Contexts

As the influence of large language models (LLMs) spans across global communities, their safety challenges in multilingual settings become paramount for alignment research. This paper examines the variations in safety challenges faced by LLMs across different languages and discusses approaches to alleviating such concerns. By comparing how state-of-the-art LLMs respond to the same set of malicious prompts written in higher- vs. lower-resource languages, we observe that (1) LLMs tend to generate unsafe responses much more often when a malicious prompt is written in a lower-resource language, and (2) LLMs tend to generate more irrelevant responses to malicious prompts in lower-resource languages. To understand where the discrepancy can be attributed, we study the effect of instruction tuning with reinforcement learning from human feedback (RLHF) or supervised finetuning (SFT) on the HH-RLHF dataset. Surprisingly, while training with high-resource languages improves model alignment, training in lower-resource languages yields minimal improvement. This suggests that the bottleneck of cross-lingual alignment is rooted in the pretraining stage. Our findings highlight the challenges in cross-lingual LLM safety, and we hope they inform future research in this direction