Humanity in the Light of Revolution—A Study of Evan King’s English Translation of National Patriotic Writer Xiao Jun’s Village in August

As one of the pivot figures of “Northeastern Writers”, Xiao Jun’s Village in August is of great importance in the history of modern Chinese literature, highlighting the local characteristics of Northeastern China, the revolutionary “banditry”, and the romantic personal heroism, attracting the attention of Chinese and Western critics. The translation by American sinology researcher Evan King shows attention to Chinese stories and makes a unique contribution to the cultural exchange between China and the West. His translation is noteworthy, but given the differences in cultural contexts between the two countries and the subjectivity of the translator, it is inevitably full of adaptation and mistranslation. Therefore, it is necessary to make a comprehensive examination of the literary style of the original work, and then propose handy translation strategies and principles for the new era: under the translation principle of truthfulness and accuracy, when translating Chinese culture, it is better to integrate both the foreignisation and domestication; when it comes to local culture, it is appropriate to apply various means to compensate for the cultural default given the specific situation and keep fidelity to the original text in the translation of the novel’s literary characteristics

A deep learning method for solving high-order nonlinear soliton equation

We propose effective scheme of deep learning method for high-order nonlinear soliton equation and compare the activation function for high-order soliton equation. The neural network approximates the solution of the equation under the conditions of differential operator, initial condition and boundary condition. We apply this method to high-order nonlinear soliton equation, and verify its efficiency by solving the fourth-order Boussinesq equation and the fifth-order Korteweg de Vries equation. The results show that deep learning method can solve the high-order nonlinear soliton equation and reveal the interaction between solitons

The real motivation behind Chinese people’s coffee consumption

Chinese market’s prosperity have received a huge amount of attention from marketing researchers. Entered into China in 1980s, coffee—this totally Western beverage has achieved a huge success in China with the expansion of Starbuck in 168 Chinese cities and is still continuing its business glory in China today (Biederman 2005). Previous studies about Chinese coffee consumption have discussed its development. However, few of them studied the motivation and drivers for Chinese people to consume coffee, especially consuming coffee in the coffee shop. Thus, our study uses the coffee shop as a useful example to illustrate that the function of luxuriousness together within Western products would be an important determinant influencing Chinese consumers’ purchase intention and willingness to pay more for a Western product with luxuriousness value. Likewise, our study fills the theoretical gap to understand the real motivation for Chinese consumers’ coffee consumption. Our findings suggest that the Perceived luxuriousness of the physical environment of the coffee shop in China induce both higher perceived quality of coffee and consistent self- congruency, which lead to positive store attitudes, thus increasing willingness to pay a premium price for the coffee. In addition, we find that perceived luxuriousness of the physical environment of the coffee shop would induce a high level of self-congruence for high cosmopolitan consumers, compared with those who are low cosmopolitan. The managerial implication of this study suggests that the establishment of luxuriousness in the physical setting of a coffee shop would be crucial to attract consumers’ attention and that managers should use premium pricing strategy to ensure the superior value of coffee that fit with coffee consumers’ identity in China

Analysis of the research progress of acupuncture with massage techniques in the treatment of migraine

Migraine is a painful disorder of the lateral side of the head, with a high clinical incidence and recurrent attacks, and is a long-term headache symptom that can damage the physical and mental health of patients and can easily cause anxiety and depression and other mental illnesses. For this reason, if migraine is suspected, patients should be admitted to hospital immediately so that the condition can be effectively controlled. At the present stage, based on the comprehensive promotion of Chinese medicine treatment technology, the advantages of Chinese medicine treatment (massage, acupuncture, etc.) are gradually highlighted, with fewer adverse effects and ideal results, and thus can be widely used in the diagnosis and treatment of various diseases. Based on this, the article focuses on migraine as the main research content, and focuses on the progress of acupuncture and massage treatment together, hoping to be helpful

Effective Multi-Agent Deep Reinforcement Learning Control with Relative Entropy Regularization

In this paper, a novel Multi-agent Reinforcement Learning (MARL) approach, Multi-Agent Continuous Dynamic Policy Gradient (MACDPP) was proposed to tackle the issues of limited capability and sample efficiency in various scenarios controlled by multiple agents. It alleviates the inconsistency of multiple agents' policy updates by introducing the relative entropy regularization to the Centralized Training with Decentralized Execution (CTDE) framework with the Actor-Critic (AC) structure. Evaluated by multi-agent cooperation and competition tasks and traditional control tasks including OpenAI benchmarks and robot arm manipulation, MACDPP demonstrates significant superiority in learning capability and sample efficiency compared with both related multi-agent and widely implemented signal-agent baselines and therefore expands the potential of MARL in effectively learning challenging control scenarios

Practical Probabilistic Model-based Deep Reinforcement Learning by Integrating Dropout Uncertainty and Trajectory Sampling

This paper addresses the prediction stability, prediction accuracy and control capability of the current probabilistic model-based reinforcement learning (MBRL) built on neural networks. A novel approach dropout-based probabilistic ensembles with trajectory sampling (DPETS) is proposed where the system uncertainty is stably predicted by combining the Monte-Carlo dropout and trajectory sampling in one framework. Its loss function is designed to correct the fitting error of neural networks for more accurate prediction of probabilistic models. The state propagation in its policy is extended to filter the aleatoric uncertainty for superior control capability. Evaluated by several Mujoco benchmark control tasks under additional disturbances and one practical robot arm manipulation task, DPETS outperforms related MBRL approaches in both average return and convergence velocity while achieving superior performance than well-known model-free baselines with significant sample efficiency. The open source code of DPETS is available at https://github.com/mrjun123/DPETS

In vitro apatite formation and visible-light photocatalytic activity of Ti metal subjected to chemical and thermal treatments

In this study, we investigated the surface structure, apatite formation in simulated body fluid (SBF), and visible-light photocatalytic activity of Ti metal subjected to chemical and thermal treatments. Ti metal samples treated with NaOH, a nitrogen-containing solution (0.1 M HNO3, 0.1–1.0 M (H2N)2Cdouble bond; length as m-dashO, or 0.1–1.0 M NH4Cl), and heat showed apatite formation on their surfaces in SBF, whereas those treated with NaOH, 0.5 or 1.0 M HNO3, and heat did not. In the former case, apatite formation may be attributable to the fine network structure of anatase-type TiO2 doped with a small amount of nitrogen on the surface of the Ti metal. The Ti metal treated with the latter treatment showed higher methylene blue decomposition than the untreated sample and the one treated with the former treatment. This preliminary result suggests that Ti metal treated with NaOH, 0.1 M HNO3, and heat can potentially show visible-light-induced antibacterial property as well as bone-bonding ability

Ask more, know better: Reinforce-Learned Prompt Questions for Decision Making with Large Language Models

Large language models (LLMs) demonstrate their promise in tackling complicated practical challenges by combining action-based policies with chain of thought (CoT) reasoning. Having high-quality prompts on hand, however, is vital to the framework's effectiveness. Currently, these prompts are handcrafted utilizing extensive human labor, resulting in CoT policies that frequently fail to generalize. Human intervention is also required in order to develop grounding functions that ensure low-level controllers appropriately process CoT reasoning. In this paper, we take the first step towards a fully integrated end-to-end framework for task-solving in real settings employing complicated reasoning. To that purpose, we offer a new leader-follower bilevel framework capable of learning to ask relevant questions (prompts) and subsequently undertaking reasoning to guide the learning of actions to be performed in an environment. A good prompt should make introspective revisions based on historical findings, leading the CoT to consider the anticipated goals. A prompt-generator policy has its own aim in our system, allowing it to adapt to the action policy and automatically root the CoT process towards outputs that lead to decisive, high-performing actions. Meanwhile, the action policy is learning how to use the CoT outputs to take specific actions. Our empirical data reveal that our system outperforms leading methods in agent learning benchmarks such as Overcooked and FourRoom