清华大学研究生支教团成员（中）和学生们一起交流。 清华大学供图 扬州大学"田管家"党员博硕士科技助农团成员查看水稻生 长情况。 扬州大学供图 行走于村落街巷，开展实地调研；在田间地头挥洒汗水，用知识孕育丰收的希望；奔赴偏远地区，以支 教的方式为孩子们传递知识和温暖……大学期间，不少学生选择参与社会实践，背起行囊、带着热情与 思考出发，在广袤大地上留下了一串串认识国情、锤炼自我、服务社会的成长足迹。 读万卷书，还要行万里路。这堂走出象牙塔的实践之课，如何拓宽视野？何以淬炼本领？让我们一起走 进一些大学生的实践故事。 在实践沃土中增长才干 正午时分，海南陵水黎族自治县坡留村的最高气温接近30摄氏度。玉米地里热浪翻涌，田间蒸腾着潮湿 的泥土气息。扬州大学农学院博士生曹秀标仍然留在试验田里，这已是他连续工作的第三天。他长时间 弯腰，一株一株地检视、间苗，后颈早已被晒得发红，膝盖以下的裤腿沾满了泥点。"这几天都是从天 亮工作到天黑，一刻也耽误不得。"曹秀标说。 这里是扬州大学"田管家"党员博硕士科技助农团（以下简称"助农团"）开展实践的重要基地。每年冬 天，他们就像候鸟一样南飞至此，利用热带气候资源开展良种繁育与助农实 ...

财经红利退潮，财经类大学将如何适应新时代的发展？高等教育"慢半拍"的问题如何解决？……12月5日，中国教 育科学论坛（2025）在北京开幕。本次论坛为期三天，由中国教育科学研究院主办。汇集教育及各个领域专家， 聚焦"智能时代中国教育学知识重建"的主题展开多样讨论。 "中国教育学建设不是一个纯粹的理论问题，应建立在教育实践基础之上。"教育部党组成员、副部长、总督学王 嘉毅出席会议并讲话。王嘉毅总结了"十四五"时期我国基础教育取得的成就，展望了"十五五"时期基础教育面临 的重大战略问题和迫切需要研究的重点问题。他强调，教育科研工作者要积极行动起来，在研究中国教育现实问 题中推动中国教育学知识的重建，为教育"十五五"规划编制工作、加快推进教育强国建设提供坚实的智力支撑。 各领域专家齐聚，探讨智能时代中国教育学知识重建 在专家主题报告环节，上海财经大学校长刘元春阐述了上海财经大学传统财经教育向"数智新财经"教育体系转向 的实践探索和理论思考。"过去十年，财经大学享受到了时代红利。但是随着智能时代、理工时代的到来，财经红 利退潮，财经类大学将如何适应新时代的发展？"刘元春介绍，上海财经大学以数智技术和现代人文为底座，发 ...

Core Viewpoint - Tsinghua University has released the "Guidelines for the Application of Artificial Intelligence in Education," which systematically provides global and hierarchical guidance for the use of AI on campus, emphasizing a "positive yet cautious" attitude towards AI as an auxiliary tool rather than a replacement for academic training [1][2][3]. Group 1: Guidelines and Principles - The guidelines cover core scenarios in teaching and academic research, prohibiting the use of AI for academic dishonesty such as ghostwriting, plagiarism, and forgery [1][2]. - The five core principles outlined in the guidelines are "Subject Responsibility," "Compliance and Integrity," "Data Security," "Prudent Reflection," and "Fairness and Inclusion" [2][3]. Group 2: Challenges and Concerns - There are doubts about whether a document can effectively curb students' reliance on AI, as the acceptance of these guidelines depends on the moral and ethical understanding of the students [3][4]. - The guidelines serve as a bridge between laws and norms, requiring respect from the ethical community to be effective [4][5]. Group 3: Ethical Considerations - The effective use of AI in education requires students to develop a moral understanding of how to appropriately and legally use AI [6][7]. - The guidelines must transform students' natural interest in technology into a moral interest to ensure compliance and prevent academic misconduct [7].

从清华大学获悉，近日，清华大学具身智能与机器人研究院正式成立。清华大学自动化系主任张涛任具 身智能与机器人研究院院长。清华具身智能与机器人研究院挂靠科研院，自动化系、机械系、电子系、 计算机系共同建设。研究院将集中校内外优势团队开展协同攻关，重点突破"强健本体+智慧大脑"全栈 技术的"0到1"原始创新。同时依托北京市产业生态资源，构建"技术研发-中试验证-场景应用"全链条转 化枢纽，加速技术成果落地。 ...

在肯尼亚期间，清华大学党委书记邱勇还访问了内罗毕大学，在内罗毕大学礼堂发表题为"人工智能时 代大学的思考、责任与行动"的演讲，中肯两国师生等各界代表300余人聆听演讲。访问内罗毕大学期 间，邱勇代表清华大学与内罗毕大学签署两校教育科研和人文交流合作协议。 新华网内罗毕12月2日电（严钰景、葛容辰）11月29日至30日，清华大学师生代表团在肯尼亚首都内罗 毕参加联合国环境规划署主办的2025年青年环境大会及相关高级别议程，此为中国青年代表首次系统性 参与联合国环境领域青年机制。 会前，清华大学师生代表团与联合国人口基金共同举办中非青年对话活动；在大会期间，代表团和"儿 童与青年主要团体"组织合办主题分论坛，并全程参与《世界青年环境宣言》的审议与完善，中国高校 学生在会上展现了中国青年在全球环境治理中日益提升的参与度和行动力。 ...

Group 1 - China's research and innovation capabilities have significantly improved, with the country ranking first in various global research metrics, including research output and innovation clusters [1][3] - The city of Nanjing, along with five other Chinese cities, has entered the top ten global research cities, marking a notable achievement for China in the international research landscape [1][2] - The World Intellectual Property Organization reports that China has 26 of the world's top 100 technology innovation clusters, leading globally, with the "Shenzhen-Hong Kong-Guangzhou" cluster ranking first [2][3] Group 2 - China's sustained investment in research and development has led to a rapid increase in research output, with funding growing nearly sixfold from 2007 to 2023, surpassing the European Union and nearing the United States [3][4] - The number of STEM graduates in China reached 3.6 million in 2020, contributing to a large pool of research talent, which is crucial for the country's scientific advancement [5][6] - Chinese researchers are increasingly taking leadership roles in international scientific collaborations, with a significant number of awards for high-impact research being granted to Chinese scientists [6]

清华大学近日成立具身智能与机器人研究院，将重点突破"强健本体+智慧大脑"全栈技术的"0到1"原始创新， 同时构建"技术研发-中试验证-场景应用"全链条转化枢纽，加速技术成果落地。 研究院挂靠科研院，由自动化系、机械系、电子系、计算机系共同建设，院长由清华大学自动化系主任张涛担 任。研究院将打造具有全球影响力的人才高地和创新策源地，强化清华大学在国家"机器人+"战略中的核心支 点地位，为我国抢占具身智能与机器人领域赛道，培育新质生产力提供核心驱动力，为国家在新一轮科技革命 和产业变革中赢得战略主动。 清华大学校长李路明表示，具身智能与机器人研究院的成立是该校主动服务国家战略需求，充分发挥多学科与 人才优势，进一步完善人工智能布局，有组织开展前瞻性、战略性、系统性的科技攻关的重要举措。希望研究 院充分发挥清华多学科优势，积极探索交叉创新新范式，打造科研创新和拔尖创新人才培养重要基地。研究院 还将与企业在智能算力供给、智能体研究、具身智能机器人开发、前沿场景验证、技术成果转化等方面展开深 度合作。 针对研究生群体，该校特别强调禁止用人工智能代替本应由本人进行的学术训练，严禁使用人工智能实施代 写、剽窃、伪造等行为。 ...

Core Points - Tsinghua University is enhancing educational and research collaboration with African countries, specifically Ethiopia and Kenya [1] - A cooperation agreement was signed with the University of Nairobi, which is a key partner for Tsinghua University [1] - The collaboration aims to expand exchanges in areas such as artificial intelligence, climate change, public health, and hospital management [1]

Core Insights - The article discusses the successful hosting of the "Hydrogen Heart Together, Create a Win-Win Future" event in Beijing, focusing on the achievements and future plans of the Beijing Hydrogen Innovation Center [2][4][44] - The event highlighted the importance of hydrogen energy in China's energy transformation and the role of the Beijing Hydrogen Innovation Center in driving technological advancements and industry collaboration [44] Event Overview - The event was co-hosted by the Beijing Municipal Bureau of Economy and Information Technology and the Daxing District People's Government, with over a hundred representatives from government, research institutions, and enterprises attending [4][6] - Key speeches were delivered by officials, emphasizing the center's role in promoting hydrogen technology and market applications [8][10] Achievements and Future Goals - The Beijing Hydrogen Innovation Center announced three significant achievements in hydrogen technology, including advancements in integrated hydrogen-electric smart chassis and green hydrogen solutions [14][16][18] - The center aims to create a collaborative innovation system to support the high-quality development of the hydrogen industry in Beijing and nationwide [13][26] Collaborative Agreements - Several cooperation agreements were signed during the event, including partnerships with testing centers and various enterprises to enhance the hydrogen energy ecosystem [20][24][26] - The agreements focus on key areas such as hydrogen fuel cell vehicle platforms, green hydrogen supply solutions, and quality testing systems [20][24][26] New Initiatives - The event launched two significant initiatives: the "Joint Innovation Demonstration Application Alliance" and the "Hydrogen Industry Innovation Think Tank," aimed at promoting sustainable development in the hydrogen sector [27][31] - These initiatives will leverage resources from various sectors to validate hydrogen technologies in real-world applications and provide strategic guidance for industry development [31][42] Expert Engagement - The Beijing Hydrogen Innovation Center established an expert committee to provide ongoing intellectual support and guidance for technology planning and innovation capacity building [32][34] - The committee includes leading scholars and experts in hydrogen technology, which will enhance the center's capabilities in research and development [34] Industry Development - The Daxing International Hydrogen Energy Demonstration Zone has attracted numerous quality enterprises, marking a significant step in building a robust hydrogen industry cluster [35][37] - The zone has nearly 300 companies covering the entire hydrogen energy value chain, contributing to a complete industrial ecosystem [37][44] Political and Organizational Support - The establishment of the Hydrogen Industry Chain Party Committee aims to enhance collaboration between government and enterprises, fostering a supportive environment for industry growth [38][42] - This initiative is expected to strengthen the integration of policy, resources, and industry collaboration, driving the development of the hydrogen energy sector [42]

Core Insights - The article discusses the significant advancements in computer vision and the challenges faced in deploying high-precision models in resource-constrained environments, such as robotics and autonomous driving, due to increased computational demands and energy consumption [2][3]. - It highlights the limitations of existing global representation learning paradigms, which process all pixels of an image or video simultaneously, leading to inefficiencies in energy and computational resources [3]. - The article introduces the AdaptiveNN architecture, which emulates human-like adaptive vision by modeling visual perception as a sequential decision-making process, allowing for efficient and flexible machine visual perception [7][11]. Group 1: Challenges in Current Computer Vision Models - High-precision models require activation of millions of parameters, resulting in increased power consumption, storage needs, and response delays, making them difficult to deploy in real-world applications [2]. - The global parallel computation paradigm leads to a significant energy efficiency bottleneck, as the computational complexity grows with the input size, making it challenging to balance high-resolution input, performance, and efficient inference [3]. Group 2: Insights from Human Visual System - Human vision operates through selective sampling of key areas rather than processing all visual information at once, which significantly reduces computational overhead and allows for efficient functioning even in resource-limited scenarios [5]. - The concept of "active observation" proposed by researchers emphasizes the need for AI systems to adopt a human-like approach to visual perception, focusing on task-driven observation [5]. Group 3: Introduction of AdaptiveNN - AdaptiveNN architecture models visual perception as a multi-step sequential decision process, allowing the model to focus on specific areas of interest and accumulate information progressively [11]. - The architecture combines representation learning with self-rewarding reinforcement learning, enabling the model to optimize its attention and decision-making without additional supervision [15][16]. Group 4: Performance and Efficiency of AdaptiveNN - In extensive experiments, AdaptiveNN achieved up to 28 times reduction in inference costs while maintaining accuracy comparable to traditional static models, demonstrating its potential for efficient visual perception [7][22]. - The model's attention mechanism automatically focuses on discriminative regions, enhancing interpretability and aligning closely with human visual behavior [22][26]. Group 5: Broader Implications and Future Research - The findings from AdaptiveNN provide insights into cognitive science, particularly in understanding human visual behavior and the mechanisms behind visual decision-making [25]. - The architecture's application in embodied intelligence models shows significant improvements in reasoning and perception efficiency, suggesting a promising direction for future research in AI and cognitive science [29].