Core Viewpoint - The article highlights the efforts of research teams from Xiamen University and Guangxi University in coral reef restoration and conservation in China, emphasizing the importance of coral reefs as vital ecosystems and the innovative methods being employed to protect and restore them in response to climate change and human activities [4][5][6][10]. Group 1: Coral Reef Research and Restoration Efforts - Xiamen University has established a marine observation and experimental station on Dongshan Island to monitor and protect coral reefs, which are crucial for biodiversity and ecological health [5][6]. - The research team has implemented a real-time underwater monitoring system to analyze coral growth and fish activity, significantly enhancing data collection and analysis efficiency [7]. - Guangxi University has developed a comprehensive coral restoration system, including species selection, breeding, and transplantation, to address coral degradation effectively [8][9]. Group 2: Impact of Climate Change and Human Activities - Climate change, particularly rising sea temperatures, is identified as the primary cause of coral bleaching, prompting research teams to explore heat acclimatization techniques for corals [9][10]. - Human activities, such as fishing and tourism, have also negatively impacted coral reefs, leading to the implementation of protective measures like buoy deployment to prevent anchor damage [6][10]. Group 3: Community Involvement and Volunteer Efforts - The involvement of volunteers has been crucial in coral conservation efforts, with over 10,000 volunteers participating in various activities across China, particularly in Guangdong [15]. - The article emphasizes the collaborative nature of coral restoration, highlighting the contributions of local communities and volunteers in supporting scientific research and conservation initiatives [15].

Core Viewpoint - The article discusses the advancements in AI, particularly focusing on Huawei's innovations in the MoE (Mixture of Experts) architecture and the introduction of RL (Reinforcement Learning) post-training techniques, which aim to enhance the efficiency and performance of large language models (LLMs) in the competitive AI landscape [1][3]. Group 1: MoE Architecture and Huawei's Innovations - The MoE model, originally proposed by Canadian scholars, has evolved significantly, with Huawei introducing the MoGE architecture that addresses inefficiencies in the traditional MoE model, leading to cost reduction and improved training and deployment [1]. - Huawei's approach emphasizes the importance of creating a collaborative ecosystem to foster the growth of the Ascend ecosystem in China [1]. Group 2: RL Post-Training Techniques - RL post-training has emerged as a critical pathway to enhance LLM performance, with models like OpenAI's o1 and DeepSeek-R1 leveraging this technique to improve reasoning capabilities in complex tasks [3][5]. - The RL post-training phase currently consumes 20% of the total computational resources, projected to rise to 50%, significantly impacting model performance and costs [3]. Group 3: Challenges in RL Post-Training - The traditional On-Policy algorithms create a "computational black hole" due to the alternating execution of training and inference tasks, leading to underutilization of resources [6][7]. - The complexity of task scheduling in large-scale clusters, exacerbated by the adoption of various parallel strategies, poses significant challenges for efficient resource utilization [8]. Group 4: Innovations in Resource Utilization - Huawei's RL Fusion technology allows a single card to handle both training and inference tasks simultaneously, effectively doubling resource utilization and throughput [9][10]. - The StaleSync mechanism enables near-asynchronous execution of tasks, achieving over 90% efficiency in horizontal scaling across CloudMatrix 384 super nodes [16][20]. Group 5: Performance Metrics and Results - The combination of RL Fusion and StaleSync has led to a significant increase in efficiency, with single-node throughput improving by 78.5% and overall performance enhancement of 1.5 times [30][31]. - StaleSync's implementation in cluster scaling shows a linear throughput increase from 35k tokens/s to 127k tokens/s as the number of super nodes increases, demonstrating its effectiveness in enhancing scalability [32]. Group 6: Conclusion - The advancements in RL post-training techniques by Huawei represent a significant leap in AI efficiency, positioning the company as a key player in the next generation of AI technology [33].

Core Viewpoint - The acquisition of 100% equity in Xinnuo Communication by Gaoling Information has been terminated due to a lack of consensus among the parties involved in the transaction [1][5]. Group 1: Acquisition Details - Gaoling Information planned to acquire Xinnuo Communication for six months but announced the termination of the acquisition on June 2, 2024 [1]. - The termination was attributed to the failure of the parties to reach an agreement on the final transaction plan [1][5]. - Xinnuo Communication had submitted an IPO application in June 2023, which was accepted by the Shanghai Stock Exchange, but it was voluntarily withdrawn on June 25, 2024 [4]. Group 2: Financial Performance - Gaoling Information has experienced a decline in revenue for two consecutive years, with reported revenues of CNY 5.17 billion, CNY 3.56 billion, and CNY 2.66 billion from 2022 to 2024, reflecting year-on-year changes of 4.48%, -31.13%, and -25.23% respectively [7]. - The net profit has also decreased for three consecutive years, with figures of CNY 884.17 million, CNY 460.52 million, and a loss of CNY 522.76 million for the same period, showing year-on-year declines of 25.66%, 47.92%, and 213.52% respectively [7]. - In 2024, the company reported its first loss since going public, primarily due to reduced demand in the military telecommunications equipment sector and increased accounts receivable [7][8]. Group 3: Research and Development - Despite financial challenges, Gaoling Information maintained stable R&D investment, amounting to CNY 80.04 million in 2024, which constituted 30.03% of its revenue [1][9]. - The company has focused its R&D efforts on advanced technologies such as secure fixed-line communication, integrated communication technology, and artificial intelligence, targeting key business areas like military communication network construction [9].

南都记者近日获悉，清华大学深圳国际研究生院"可见光通信"课程落地深圳市格致中学。作为国内首个在中学阶 段引入的可见光通信课程，这不仅是格致中学校本课程体系的一次重大飞跃，更是中学阶段网络通信教学领域的 新篇章，这一创新举措充分彰显了格致中学在"中学+大学+科研机构"协同育人模式下的科学教育实践成果。 可见光通信课程首次面向中学生开设 探索"中学+大学+企业+专业机构"办学新路径 格致中学探索"中学+大学+企业+专业机构"办学新路径，精心打造"航天科创""零一创新""人文素养"三大类百余门 校本课程，与学生社团相得益彰，为学生个性发展和多赛道成长提供支持。 除了可见光通信课程，学校早在2021年就建设大湾区第一家量子计算中心，与深圳国际量子研究院达成战略合 作，联合国际量子研究院的科研人员和深圳量旋科技有限公司科研人员共同开设中学量子校本课程，成立学生量 子社团，探索量子计算在中学的科普方式。 深圳市格致中学联合广东省无线光通信工程技术研究中心、深圳市可见光通信系统重点实验室（依托单位清华大 学深圳国际研究生院），建设"可见光通信创新实验室"，面向本校初中、高中学生开设"可见光通信"科创课程。 清华大学袁克虹教 ...

训练效率不足 ，甚至 一半以上训练时间都浪费在"等待"上 。 现在，为了突破MoE的训练瓶颈，华为出手了： 构建了一套名为 Adaptive Pipe & EDPB 的优化方案，开启"上帝视角"，让MoE面临"交通拥堵"的训练集群， 实现无等待流畅运行。 MoE大规模训练难题：一半以上的训练时间在等待？ 实践已经表明，MoE模型训练集群的效率面临两方面挑战： 首先，是 专家并行引入了计算和通信等待 。 允中 发自 凹非寺 量子位 | 公众号 QbitAI Scaling Law之下，MoE（混合专家）如今已经成为各大模型厂商扩展模型能力的制胜法宝。 不过，在高效实现模型参数规模化的同时，MoE的 训练难题 也日益凸显： 当模型规模较大时，需要切分专家到不同设备形成并行（EP），这就引入额外All-to-All通信。 与此同时，MoE层绝大部分EP通信与计算存在时序依赖关系，一般的串行执行模式会导致大量计算单元空闲， 等待通信。 其次， 负载不均会引入计算和计算等待 。 MoE算法核心是"有能者居之"，在训练过程中会出现部分热专家被频繁调用，而冷专家使用率较低的情况。 同时，真实训练数据的长度不一，不同的模型层 ...

5月31日，在2025未来汽车先行者大会上，华为常务董事、终端BG董事长余承东表示，鸿蒙智行可能不 会再有第六"界"。他称："因为我们的能力做两三个界都已经很不容易了，做五个界非常非常难。一个 拳头还真的需要五个手指头，握上才有力量。所以也不可能有六个手指头，我们就到此暂时握紧了，一 起努力干，一起打天下。"(第一财经) ...

Core Viewpoint - Huawei has achieved significant advancements in training large models through its "Ascend + Pangu Ultra MoE" system, demonstrating a fully domestic and GPU-free training process that enhances computational efficiency and model performance [3][4][38]. Group 1: Technical Innovations - Huawei's training system has achieved a model training efficiency with a utilization rate (MFU) of 41% during the pre-training phase using the Ascend Atlas 800T A2 cluster [4][38]. - The Pangu Ultra MoE model consists of 718 billion parameters, featuring a unique architecture with 61 layers, including 58 MoE layers, and is designed for high performance and scalability [38][39]. - The system supports a high throughput of 35K Tokens/s during the reinforcement learning (RL) post-training phase, showcasing its capability to process complex tasks rapidly [39]. Group 2: Challenges Addressed - The report identifies six key challenges in the current MoE pre-training and RL post-training processes, including difficulties in parallel strategy configuration, communication bottlenecks, and uneven system load distribution [7][10][12][13]. - Huawei has developed a comprehensive end-to-end solution to address these challenges, focusing on optimizing training cluster utilization and enhancing communication efficiency [14][16][25]. Group 3: Specific Solutions - The first strategy involves improving training cluster utilization through intelligent parallel strategy selection and global dynamic load balancing, significantly enhancing overall training efficiency [16][23]. - The second strategy focuses on releasing computational power at the single-node level by optimizing training operators and enhancing memory management, achieving a twofold increase in micro-batch size [26][30]. - The third strategy introduces high-performance scalable RL post-training technologies, allowing for flexible deployment modes and doubling the utilization rate of RL post-training clusters [33][34].

Group 1 - Dongfeng Motor Group Co., Ltd. and Huawei Technologies Co., Ltd. have signed a comprehensive strategic cooperation agreement in Wuhan [1] - The collaboration will focus on areas such as automotive intelligence, enterprise digitalization, and intelligent upgrades [1] - The partnership aims to promote ecological co-construction and deepen cooperation across various fields [1]

Core Viewpoint - Shenzhen Zhenyou Technology Co., Ltd. has been awarded the second prize of the Shenzhen Science and Technology Progress Award for its project on "Key Technology Innovation and Industry Application of Hierarchical Heterogeneous Communication Integration and All-Domain Command and Dispatch" [1][2] Group 1: Project Overview - The awarded project focuses on the integration of various communication technologies, including wireless communication, satellite communication, and IoT, to create a comprehensive emergency management information system [1] - The project aims to enhance real-time perception of natural disasters and safety production accidents through a multi-dimensional sensing system, improving the timeliness, accuracy, and effectiveness of information acquisition [1] Group 2: Innovation and Impact - The project addresses bottlenecks in command and dispatch systems related to information sharing, emergency response, and task collaboration by utilizing big data and artificial intelligence [2] - The award is a recognition of the company's technological innovation capabilities, which is expected to enhance its core technology advantages and market competitiveness [2]

Group 1 - The appointment of the new U.S. ambassador to China, Qin Gang, reflects the deep contradictions in the Trump administration's China policy, as evidenced by Trump's simultaneous desire to visit China while imposing domestic pressures on Walmart and the Federal Reserve [1][3] - The U.S. federal debt has surpassed $36 trillion, and the 10-year Treasury yield has risen to 4.5%, causing anxiety in the White House, especially as China has been reducing its holdings of U.S. Treasuries [1][3] - Trump's pressure on Walmart and the Federal Reserve reveals his true policy intentions, as the Consumer Price Index has risen by 6.2% year-on-year due to tariffs on Chinese goods, ultimately burdening American consumers [3][5] Group 2 - The new ambassador, Qin Gang, has a close relationship with Trump and previously advocated for "supply chain decoupling," indicating a hawkish stance despite claims of strategic engagement [5][6] - The ongoing "truce" period in U.S.-China relations allows for potential negotiations, but China insists on equality in discussions, highlighting the failure of Trump's previous tariff strategies [5][6] - The shifting attitudes of the Trump administration are accelerating the "de-dollarization" process, as countries like Saudi Arabia and the UAE seek alternatives to U.S. influence [6][8]