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  1. Article ; Online: Pricing Mechanism of Charging Pile Power Supply Market——Based on RTP Theory and Price Discrimination Model

    Ding Shiyao / Zhang Siqi

    E3S Web of Conferences, Vol 275, p

    2021  Volume 01073

    Abstract: Based on the data of monopoly enterprises in China’s new energy charging pile power retail market, this paper explores the application of RTP differential pricing in new areas. First of all, from the perspective of business, this paper constructs the ... ...

    Abstract Based on the data of monopoly enterprises in China’s new energy charging pile power retail market, this paper explores the application of RTP differential pricing in new areas. First of all, from the perspective of business, this paper constructs the incentive cost model of low period which can minimize the supply pressure of power sales enterprises. Then, from the perspective of charging consumers, based on the assumption of user’s conversion cost, an improved demand response model is established according to the price elasticity. The paper is to consider the premise of maximizing social welfare, in the supply and demand of both sides to improve the pressure of electricity measurement, to minimize the operation and maintenance costs in peak and trough period.
    Keywords Environmental sciences ; GE1-350
    Language English
    Publishing date 2021-01-01T00:00:00Z
    Publisher EDP Sciences
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Book ; Online: Self-Agreement

    Ding, Shiyao / Ito, Takayuki

    A Framework for Fine-tuning Language Models to Find Agreement among Diverse Opinions

    2023  

    Abstract: Finding an agreement among diverse opinions is a challenging topic in multiagent systems. Recently, large language models (LLMs) have shown great potential in addressing this challenge due to their remarkable capabilities in comprehending human opinions ... ...

    Abstract Finding an agreement among diverse opinions is a challenging topic in multiagent systems. Recently, large language models (LLMs) have shown great potential in addressing this challenge due to their remarkable capabilities in comprehending human opinions and generating human-like text. However, they typically rely on extensive human-annotated data. In this paper, we propose Self-Agreement, a novel framework for fine-tuning LLMs to autonomously find agreement using data generated by LLM itself. Specifically, our approach employs the generative pre-trained transformer-3 (GPT-3) to generate multiple opinions for each question in a question dataset and create several agreement candidates among these opinions. Then, a bidirectional encoder representations from transformers (BERT)-based model evaluates the agreement score of each agreement candidate and selects the one with the highest agreement score. This process yields a dataset of question-opinion-agreements, which we use to fine-tune a pre-trained LLM for discovering agreements among diverse opinions. Remarkably, a pre-trained LLM fine-tuned by our Self-Agreement framework achieves comparable performance to GPT-3 with only 1/25 of its parameters, showcasing its ability to identify agreement among various opinions without the need for human-annotated data.
    Keywords Computer Science - Computation and Language ; Computer Science - Artificial Intelligence ; Computer Science - Multiagent Systems
    Subject code 006
    Publishing date 2023-05-19
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article: A study of the relationship between social anxiety and mask-wearing intention among college students in the post-COVID-19 era: mediating effects of self-identity, impression management, and avoidance.

    Xia, Tiansheng / Xu, Xuan / Ding, Shiyao

    Frontiers in psychology

    2023  Volume 14, Page(s) 1287115

    Abstract: Introduction: During the 2019 coronavirus (COVID-19) pandemic, wearing masks not only prevented transmission of the virus but also reduced social anxiety to some extent. With the end of the epidemic, the intention to wear masks to prevent transmission ... ...

    Abstract Introduction: During the 2019 coronavirus (COVID-19) pandemic, wearing masks not only prevented transmission of the virus but also reduced social anxiety to some extent. With the end of the epidemic, the intention to wear masks to prevent transmission declined, but the effect of social anxiety on the intention to wear masks is unclear. The current study investigated the effects of social anxiety and fear of COVID-19 on mask-wearing intentions in the post-epidemic era, using self-identity, impression management and avoidance as mediating variables.
    Methods: In total, 223 college students participated in the current study, and the related variables were measured using the social anxiety scale, the social behavior questionnaire, the self-identity questionnaire, and the mask-wearing intention questionnaire.
    Results: The results showed that social anxiety was significantly positively correlated with avoidance, impression management, and intention to wear masks, and significantly negatively correlated with self-identity. The fear of COVID-19, avoidance, and impression management were significantly positively correlated with mask-wearing intentions, while self-identity was significantly negatively correlated with mask-wearing intentions. Social anxiety affected college students' intention to wear masks through three main pathways: the mediating role of avoidance, impression management, and the chain mediating role of self-identity and avoidance. The fear of COVID-19 directly and positively affected mask-wearing intentions.
    Discussion: The current study reveals the differential pathways of the effects of COVID-19 fear and social anxiety on mask-wearing intentions in the post-COVID-19 era, and the findings have some practical implications for social anxiety interventions.
    Language English
    Publishing date 2023-11-21
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2563826-9
    ISSN 1664-1078
    ISSN 1664-1078
    DOI 10.3389/fpsyg.2023.1287115
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: The Aerial Guide Dog: A Low-Cognitive-Load Indoor Electronic Travel Aid for Visually Impaired Individuals.

    Zhang, Xiaochen / Pan, Ziyi / Song, Ziyang / Zhang, Yang / Li, Wujing / Ding, Shiyao

    Sensors (Basel, Switzerland)

    2024  Volume 24, Issue 1

    Abstract: Most navigation aids for visually impaired individuals require users to pay close attention and actively understand the instructions or feedback of guidance, which impose considerable cognitive loads in long-term usage. To tackle the issue, this study ... ...

    Abstract Most navigation aids for visually impaired individuals require users to pay close attention and actively understand the instructions or feedback of guidance, which impose considerable cognitive loads in long-term usage. To tackle the issue, this study proposes a cognitive burden-free electronic travel aid for individuals with visual impairments. Utilizing human instinctive compliance in response to external force, we introduce the "Aerial Guide Dog", a helium balloon aerostat drone designed for indoor guidance, which leverages gentle tugs in real time for directional guidance, ensuring a seamless and intuitive guiding experience. The introduced Aerial Guide Dog has been evaluated in terms of directional guidance and path following in the pilot study, focusing on assessing its accuracy in orientation and the overall performance in navigation. Preliminary results show that the Aerial Guide Dog, utilizing Ultra-Wideband (UWB) spatial positioning and Measurement Unit (IMU) angle sensors, consistently maintained minimal deviation from the targeting direction and designated path, while imposing negligible cognitive burdens on users while completing the guidance tasks.
    MeSH term(s) Animals ; Dogs ; Humans ; Pilot Projects ; Service Animals ; Aircraft ; Electronics ; Cognition
    Language English
    Publishing date 2024-01-04
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s24010297
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Book ; Online: Heterogeneous-Agent Mirror Learning

    Kuba, Jakub Grudzien / Feng, Xidong / Ding, Shiyao / Dong, Hao / Wang, Jun / Yang, Yaodong

    A Continuum of Solutions to Cooperative MARL

    2022  

    Abstract: The necessity for cooperation among intelligent machines has popularised cooperative multi-agent reinforcement learning (MARL) in the artificial intelligence (AI) research community. However, many research endeavors have been focused on developing ... ...

    Abstract The necessity for cooperation among intelligent machines has popularised cooperative multi-agent reinforcement learning (MARL) in the artificial intelligence (AI) research community. However, many research endeavors have been focused on developing practical MARL algorithms whose effectiveness has been studied only empirically, thereby lacking theoretical guarantees. As recent studies have revealed, MARL methods often achieve performance that is unstable in terms of reward monotonicity or suboptimal at convergence. To resolve these issues, in this paper, we introduce a novel framework named Heterogeneous-Agent Mirror Learning (HAML) that provides a general template for MARL algorithmic designs. We prove that algorithms derived from the HAML template satisfy the desired properties of the monotonic improvement of the joint reward and the convergence to Nash equilibrium. We verify the practicality of HAML by proving that the current state-of-the-art cooperative MARL algorithms, HATRPO and HAPPO, are in fact HAML instances. Next, as a natural outcome of our theory, we propose HAML extensions of two well-known RL algorithms, HAA2C (for A2C) and HADDPG (for DDPG), and demonstrate their effectiveness against strong baselines on StarCraftII and Multi-Agent MuJoCo tasks.
    Keywords Computer Science - Multiagent Systems ; Computer Science - Artificial Intelligence
    Subject code 006
    Publishing date 2022-08-02
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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