Core Viewpoint - The research published in Nature highlights the design of a new ductile FeNiCoAlTa alloy with unprecedented strength and plasticity, achieved through machine learning techniques, setting a new record for yield strength and tensile ductility in alloys [2][4]. Group 1: Research Findings - The alloy composition is Fe 35 Ni 29 Co 21 Al 12 Ta 3, which demonstrates a remarkable combination of 1.8 GPa yield strength and 25% true uniform elongation [4]. - The study emphasizes the use of machine learning models based on domain knowledge to design multi-principal element alloys, achieving high strength and high ductility [4]. Group 2: Microstructural Innovations - The research team enhanced the alloy's strength by maximizing microstructural heterogeneity, featuring large coherent L1 2 nano-precipitates and incoherent B2 micro-particles [6]. - The B2 micro-particles contribute to a multi-component structure that can accumulate dislocations, thereby maintaining a high strain hardening rate and extending uniform elongation [6].

齐鲁晚报·齐鲁壹点 陈乃彰 通讯员 刘军 王栩晨 为进一步深化科技社团建设内涵，培育兼具人文底蕴与科学精神的复合型人才，6月4日，威海市科普志 愿者协会李盛副秘书长牵头组织跨领域专家团队走进新都中学。环翠区美术馆馆长、中职院校骨干教 师，以及农业技术局退休资深专家等各界精英齐聚一堂，从艺术融合、技术实践、产业应用等多元视 角，为学校科技社团发展注入专业指导力量。 在科技发明社团活动现场，于老师化身"电路魔法师"，以电路板、太阳能发电设备为教具，为同学们开 启了一场充满趣味的电学探索之旅。他手持电路板，用通俗易懂的语言讲解电阻、电容的基础原 理："电阻就像电路里的'交通指挥员'，能控制电流的大小；电容则像'小水库'，可以储存和释放电 能。" 随后，于老师亲自示范如何使用万用表测量电阻，边操作边强调安全规范与测量技巧，让晦涩的电学知 识变得直观易懂。理论讲解结束后，于老师与同学们展开热烈互动。面对"电阻在生活中有哪些实际应 用"的疑问，他以手机充电器、台灯调光功能为例，剖析电阻调节电流的原理；针对"电容能否替代电池 供电"的好奇，于老师结合电容充放电特性与电池持续供电的差异，引导同学们思考两者的适用场景。 互动中 ...

在LSEG"与专家面对面"系列网络研讨会的最新一期中，深入剖析了实施全面合规计划的重要性，并为制定 更具主动性的风险管理策略提供了一系列最佳实践方面的见解。 以更少的资源做更多的事 如今，受监管实体正深陷一场"完美风暴"：法规不断演变，尽职调查工作量与成本急剧攀升，而资源却极为 有限。据LSEG的研究显示，90%的受访者表示，过去三年间，他们所处理的增强尽职调查（EDD）请求数 量呈上升态势。 这些不断攀升的工作量给预算和资源带来了巨大压力。与此同时，合规团队必须确保客户准入流程以及交易 决策过程快速、无缝且具备成本效益。 所有这些情况都凸显出"以更少资源达成更多成效"的迫切需求。借助恰当的数据与技术手段，您便能够驾驭 这一复杂多变的风险局面。 5项最佳实践见解 01 采用基于风险的方法 采用基于风险的方法至关重要，因为资源是有限的。即使是最大的组织也没有无限的资源，这意味着您应该 将可用的预算、时间和精力投入到潜在风险最高的领域。 筛查是识别潜在风险的重要初始环节。一旦怀疑或发现风险，便需要以增强尽职调查（EDD）的形式开展 更深入的尽职调查工作。 增强尽职调查（EDD）的力度应与怀疑的风险程度相匹配，并应 ...

Group 1 - The report titled "2025 Financial Industry Intelligent Risk Control Practice White Paper" focuses on the intelligent risk control practices in the financial sector, aiming to provide references for the industry [1][12] - The report highlights the urgent need for financial institutions to enhance their intelligent risk control capabilities due to the increasing severity of fraud risks and the evolving landscape of financial crime [1][18] - The current state of intelligent risk control in the financial industry is characterized by a shift towards technology-driven solutions, with traditional models facing limitations in data and algorithmic capabilities [2][20] Group 2 - The report identifies several challenges in the construction of intelligent risk control systems, including difficulties in data sharing, data quality issues, and the evolving tactics of criminal enterprises [2][29] - Proposed solutions include broadening data dimensions, automating analysis, and developing more refined model strategies to enhance risk management [2][36] - Practical case studies from various financial institutions demonstrate the effectiveness of intelligent risk control applications, such as the use of blockchain technology and integrated intelligence systems [2][26] Group 3 - The future of intelligent risk control in the financial industry is expected to focus on collaborative defense, ecosystem co-construction, and technology empowerment to improve overall risk management levels [2][36] - The report emphasizes the importance of high-quality data and dynamic risk assessment models in achieving effective intelligent risk control [2][36] - The financial sector is urged to adapt to new fraud techniques and enhance monitoring capabilities to address the challenges posed by evolving criminal methods [2][31]

其产品组合包括贡献主要收入的组织分子分析解决方案MI Profile，以及2024年一季度推出的血液分子 检测方案Caris Assure。此外，公司还运营药物发现业务，利用检测数据和基因组数据集识别潜在药物 靶点并研发疗法。 财务数据显示，2025年一季度Caris营收1.209亿美元，净亏损1.27亿美元;去年同期则是8070万美元营收 和1.341亿美元亏损，亏损幅度收窄。 招股书显示，上市后哈尔伯特将持股41.7%，Sixth Street Partners和私募公司JH Whitney Capital Partners 的关联机构分别持股9.8%和6.8%。 医疗科技公司Caris Life Sciences(CAI.US)于美东时间周三登陆纳斯达克市场，在美股首次公开募股(IPO) 中筹资4.94亿美元，发行价高于原定区间上限。该股一经上市便高涨至近40%，周三收涨33%于28美 元。 由Sixth Street Partners支持的Caris以每股21美元定价发行2350万股，较最初19至20美元的定价区间再度 上调。按此前备案文件中的流通股计算，这家总部位于得州欧文的公司市值达79亿美元。 ...

Core Viewpoint - AMD's CDNA 4 architecture represents a moderate update over CDNA 3, focusing on enhancing matrix multiplication performance for low-precision data types, which are crucial for machine learning workloads [2][26]. Architecture Overview - CDNA 4 maintains a similar system-level architecture to CDNA 3, utilizing a large chiplet setup with eight compute dies (XCD) and a memory-side cache of 256 MB [4][20]. - The architecture employs AMD's Infinity Fabric technology for consistent memory access across multiple chips [4]. Performance Comparison - The MI355X GPU, based on CDNA 4, features a clock speed of 2.4 GHz and 256 cores, compared to MI300X's 304 cores at 2.1 GHz, indicating a slight reduction in core count but improved clock speed [5]. - MI355X offers 288 GB of HBM3E memory with a bandwidth of 8 TB/s, surpassing Nvidia's B200, which has a maximum capacity of 180 GB and bandwidth of 7.7 TB/s [25]. Matrix and Vector Throughput - CDNA 4 has rebalanced execution units to focus on low-precision matrix multiplication, doubling matrix throughput per compute unit (CU) in many cases [6][39]. - The architecture supports new low-precision data formats, significantly enhancing AI performance, with matrix core improvements leading to nearly four times the computational throughput for low-precision formats [46][47]. Local Data Sharing (LDS) Enhancements - CDNA 4 increases the Local Data Share (LDS) capacity to 160 KB and doubles the read bandwidth to 256 bytes per clock, improving data locality for matrix multiplication routines [14][48]. - The architecture introduces new instructions for reading transposed LDS, optimizing memory access patterns for matrix operations [18]. Memory Hierarchy and Cache - The memory hierarchy includes a shared 4 MB L2 cache and a 32 KB L1 vector cache per CU, with enhancements for caching non-coherent data from DRAM [49][50]. - The Infinity Cache remains at 256 MB, providing high bandwidth and supporting the increased memory demands of modern AI workloads [53]. Chiplet Architecture - The CDNA 4 architecture continues to leverage a chiplet-based design, allowing for independent evolution of each chiplet for improved performance and manufacturability [35][36]. - Each XCD contains 36 compute units, organized into arrays, with a focus on maximizing yield and operational frequency [39]. System Communication and Expansion - The architecture includes eight AMD Infinity Fabric links, with improved speeds of up to 38.4 Gbps, enhancing communication bandwidth within server nodes [63]. - The design supports both direct compatibility with previous generations and progressive improvements for high-performance systems [62]. Conclusion - AMD's CDNA 4 architecture builds on the success of CDNA 3, focusing on optimizing performance for machine learning workloads while maintaining a competitive edge against Nvidia [26][27].

Core Viewpoint - The article discusses the current state and future directions of photomask manufacturing, emphasizing the importance of curved masks and advanced computational tools in extending the viability of non-EUV lithography technologies [1][3][4]. Group 1: Innovations in Photomask Technology - The use of curved photomasks is a significant innovation that leverages current writing technologies to create complex shapes previously unattainable [3]. - Advanced computational tools, such as Mask Process Correction (MPC) and high-level simulations, are increasingly used in the mask design flow, reducing the need for expensive experiments and pushing technological limits [3][6]. - The evolution of variable shape beam (VSB) writing technology to multi-beam writing technology has made curved mask shapes feasible without increasing writing time or costs [5]. Group 2: Challenges and Infrastructure Needs - There is a substantial need for infrastructure development to support the complexity of curved shapes, as traditional rectangular descriptions are simpler to manage [8]. - The transition to curved processes is seen as an exception rather than the norm, impacting economics and infrastructure, particularly in the reliance on GPU-based computing [9]. - Measurement technologies must evolve to handle the complexities of curved shapes, requiring higher resolution and faster measurement tools [11]. Group 3: EUV Masking Issues - EUV masks face challenges such as lower durability compared to 193i masks, necessitating frequent replacements that increase costs and complexity [13]. - The performance of EUV pellicles is currently suboptimal, leading to significant wafer throughput losses due to energy loss during transmission [13][15]. - The balance between using pellicles and the associated costs is contingent on the specific use case, with larger, high-value chips benefiting more from pellicles than smaller, redundant designs [16]. Group 4: Future Directions and Research - Research is ongoing into alternative materials for pellicles, such as carbon nanotube films, which could address current limitations but are not yet in mass production [17]. - The industry is exploring ways to improve the durability and transmission rates of EUV pellicles, which could lead to broader applications if successful [15][16].

弘则科技- AI 应用调研 20250616 摘要 公司于 2021 年获得项目自主审批权，加速了人工智能项目落地，此前 受制于集团公司对百万以上项目的严格审批。这一政策转变源于集团层 面统一规划的推进困难，鼓励有实力的分子公司先行探索。 传统 ERP 系统已无法满足人工智能时代的需求，需要在边缘侧增加自动 感知和数据感知能力，以实现中心侧的智能化管理。公司并行运行机器 学习和人工建模，专家经验在短周期设备分析中仍具优势。 传统地面监控系统误报率高，通过逻辑建模筛选误报，提高精确报警率， 减少现场检查工作量。机器学习通过专家知识自动学习，处理大量样本， 实现设备状态的提前预测和预防。 公司通过综合传感器数据预测设备状态，并根据不同类型的场景预设构 建相关模型。智慧运行系统告警后联动生产管控系统，由现场维护人员 或远程操作进行响应，确保及时响应。 经过对多家供应商的调研，最终选择了新环科技和第四范式，主要考虑 成本因素。第四范式因其子公司在水电领域有丰富经验，能结合人工智 能技术提供清晰解释而被选定。 Q&A 贵公司最早是如何决定使用主动学习技术的？这个决策过程是怎样的？ 我们从 2022 年开始考虑智慧企业建 ...

以下文章来源于硬氪 ，作者黄楠 硬氪 . 专注全球化、硬科技报道。36kr旗下官方账号。 已同多家头部机器人厂商展开深入合作。 文 ｜ 黄楠 编辑 ｜ 袁斯来 来源｜ 硬氪（ID：south_36kr） 封面来源 ｜企业供图 硬氪获悉，深圳留形科技有限公司（以下简称「留形科技」）近日完成数千万元Pre-A轮融资，本轮投资方包括弘毅投资等，资金 将主要用于核心零部件定制生产、产品规模化交付及市场拓展。此前，公司曾获得真格基金种子轮投资，以及君盛投资的天使轮 投资。 「留形科技」成立于2022年，聚焦智能三维感知和重建算法及产品开发应用，以赋予机器人、无人机等具身智能设备精准的空间 感知、智能记忆及主动交互能力。其创始团队孵化于香港大学MaRS实验室，MaRS实验室专注于无人机设计、导航和控制，以及 基于激光雷达的同步定位与地图构建（SLAM）技术；香港大学MaRS LAB负责人张富教授担任公司技术战略顾问，曾为大疆创 新顾问科学家及Livox激光雷达负责人之一。 近年来，随着人工智能与机器学习技术的深度融合，三维感知重建设备的精度与效率实现显著提升。部分搭载高性能并行计算能 力的商业软件，能对海量图像、点云等数 ...

已同多家头部机器人厂商展开深入合作。 文 ｜ 黄楠 编辑 ｜ 袁斯来 来源｜ 硬氪（ID：south_36kr） 封面来源 ｜企业供图 以下文章来源于硬氪 ，作者黄楠 硬氪 . 专注全球化、硬科技报道。36kr旗下官方账号。 硬氪获悉，深圳留形科技有限公司（以下简称「留形科技」）近日完成数千万元Pre-A轮融资，本轮投资方包括弘毅投资等，资金 将主要用于核心零部件定制生产、产品规模化交付及市场拓展。此前，公司曾获得真格基金种子轮投资，以及君盛投资的天使轮 投资。 「留形科技」成立于2022年，聚焦智能三维感知和重建算法及产品开发应用，以赋予机器人、无人机等具身智能设备精准的空间 感知、智能记忆及主动交互能力。其创始团队孵化于香港大学MaRS实验室，MaRS实验室专注于无人机设计、导航和控制，以及 基于激光雷达的同步定位与地图构建（SLAM）技术；香港大学MaRS LAB负责人张富教授担任公司技术战略顾问，曾为大疆创 新顾问科学家及Livox激光雷达负责人之一。 近年来，随着人工智能与机器学习技术的深度融合，三维感知重建设备的精度与效率实现显著提升。部分搭载高性能并行计算能 力的商业软件，能对海量图像、点云等数 ...