科技日报北京5月14日电 （记者马爱平）记者14日从农业农村部环境保护科研监测所获悉，该所产地环 境监测与预警创新团队日前在国际期刊《危险材料杂志》上发表了一项重要成果，首次构建了基于机器 学习的生物炭修复农田土壤镉污染效果智能预测模型。该技术通过深入量化生物炭结构性质、土壤理化 性质与修复效果之间的复杂关联，成功实现了修复材料的精准匹配与方案优化。 "镉作为一种常见的农田土壤重金属污染物，容易通过作物富集进入食物链，从而对人类健康构成严重 威胁。生物炭因其独特的多孔结构、高稳定性及强吸附能力，被广泛用于重金属污染土壤的钝化修 复。"论文通讯作者、团队首席科学家安毅告诉科技日报记者，传统修复材料生物炭筛选主要依赖田间 试验或实验室培养的"试错法"，存在周期长、成本高、地域适配性差、机制认知模糊等痛点。 针对这些痛点，研究团队以生物炭修复农田土壤镉污染为基础，检索过去二十年中发表的同行评议文献 近1700篇，系统分析了234组试验数据，最终建立了生物炭修复土壤镉污染的案例库。 原标题：智能模型精准预测生物炭修复农田土壤镉污染效果 论文第一作者、团队副研究员杜兆林介绍，研究突破性地将机器学习算法引入修复材料设计领 ...

Summary of Align Technology Conference Call Company Overview - **Company**: Align Technology - **Industry**: Dental and Orthodontic Technology Key Points and Arguments 1. Q1 Results and Growth - Align Technology reported better-than-expected Q1 results, with growth across both orthodontic (ortho) and general practice (GP) segments, including both teen and adult markets [4][5][6] 2. Analyst Day Insights - The recent Analyst Day provided updates on innovations in digital scanning, software, and 3D printing, emphasizing the importance of technology in digitizing orthodontic practices [8][9][10] 3. Long-Range Plan (LRP) Updates - The new LRP indicates growth expectations of 5-15% from 2026 to 2028, with over 15% growth anticipated in 2029 and beyond, driven by innovations and market expansion [11][12][18] 4. Innovations Driving Growth - Key innovations include the Invisalign Palatal Expander for younger patients and the occlusal block for teenagers, addressing significant market needs [13][14][15] - The company is focusing on expanding its product portfolio to cater to various demographics and price points [17][18] 5. Market Resilience - Align Technology is confident in its ability to drive adoption and utilization globally, independent of macroeconomic conditions, particularly in the near term [19][20] 6. Direct Fabrication (DirectFab) - DirectFab is in early stages but is expected to enhance manufacturing capabilities and product offerings, with a focus on customization and efficiency [30][33][34] 7. Regional Market Insights - The U.S. market has shown stability, with a mix of GP and ortho channels, while Canada has a GP-driven model. Latin America presents unique opportunities, particularly in Brazil and Mexico, where affordability and multidisciplinary practices are key [38][48][52][56] 8. Competitive Landscape - The exit of some direct-to-consumer (DTC) competitors presents opportunities for Align to capture market share, leveraging its comprehensive product portfolio [45][46] 9. Education and Customer Engagement - Align Technology emphasizes the importance of education and peer-to-peer community building among doctors, with over 500 live education events planned in the U.S. [41][42][50] 10. Tariff Concerns - Current tariff impacts are manageable, primarily affecting iTero products from Israel, with no significant changes anticipated in the near future [59][60] 11. Doctor-Centric Approach - Align Technology focuses on building trust with doctors, emphasizing that successful outcomes depend on the confidence doctors have in Align's products and workflows [64][65] Additional Important Content - The company is leveraging technology to enhance patient awareness and treatment options, aiming for a seamless integration of orthodontic and restorative workflows [26][27][29] - Align Technology is committed to addressing market needs through continuous innovation and customer feedback, ensuring that its offerings remain relevant and effective [21][22][23]

Core Viewpoint - The article discusses a guide by Neel Nanda from Google DeepMind on how to write high-quality machine learning papers, emphasizing the importance of clarity, narrative, and evidence in research writing [2][3][7]. Group 1: Writing Essentials - The essence of an ideal paper lies in its narrative, which should tell a concise, rigorous, evidence-based technical story that includes key points of interest for the reader [8]. - Papers should compress research into core claims supported by rigorous empirical evidence, while also clarifying the motivation, problems, and impacts of the research [11]. Group 2: Key Writing Elements - Constructing a narrative involves distilling interesting, important, and unique results into 1-3 specific novel claims that form a coherent theme [13]. - Timing in writing is crucial; researchers should list their findings, assess their evidential strength, and focus on the highlights before entering the writing phase [14]. - Novelty should be highlighted by clearly stating how the results expand knowledge boundaries and differentiating from previous work [15]. - Providing rigorous evidence is essential, requiring experiments that can distinguish hypotheses and maintain reliability, low noise, and statistical rigor [16]. Group 3: Paper Structure - The abstract should spark interest, succinctly present core claims and research impact, and explain key claims and their basis [18]. - The introduction should outline the research background, key contributions, core evidence, and significance in a list format [26]. - The main body should cover background, methods, and results, explaining relevant terms and detailing experimental methods and outcomes [26]. - The discussion should address research limitations and explore broader implications and future directions [26]. Group 4: Writing Process and Common Issues - The writing process should begin with compressing research content to clarify core claims, motivations, and key evidence, followed by iterative expansion [22]. - Common issues include excessive focus on publication, overly complex content, and neglecting the writing process; solutions involve prioritizing research, using clear language, and managing time effectively [24].

Core Viewpoint - The article discusses the importance of foundational knowledge in machine learning education, emphasizing that understanding core algorithms and mathematical principles is crucial for long-term success in the field, especially in the context of rapidly evolving technologies like LLMs [2][20][23]. Group 1: Course Design and Focus - The machine learning course designed by Kyunghyun Cho for the 2025 academic year focuses on foundational algorithms like Stochastic Gradient Descent (SGD) while intentionally avoiding large language models (LLMs) [2][7]. - Other prestigious institutions, such as Stanford and MIT, also emphasize foundational theories and classic models in their machine learning curricula, indicating a broader trend in academia [2][4]. - The course encourages students to study classic papers to understand the historical development of machine learning theories, which is seen as beneficial for critical thinking [7][23]. Group 2: Theory vs. Practice - There is a tension between the academic focus on foundational principles and the practical skills required in industry, where rapid deployment and iteration are often prioritized [9][20]. - Some universities are addressing this gap by offering bridge courses or practical projects, such as Stanford's CS329S, which focuses on building deployable machine learning systems [9][11]. - CMU's machine learning doctoral program includes a practical course where students must build and deploy a complete machine learning system, highlighting the importance of hands-on experience [11][13]. Group 3: Importance of Foundational Knowledge - The article argues that a strong foundation in machine learning is essential for adapting to new technologies and for fostering innovation in research [17][20][23]. - Geoffrey Hinton emphasizes that the breakthroughs in deep learning were built on decades of foundational research, underscoring the value of understanding core algorithms [23]. - The article posits that practical skills should be built upon a solid understanding of underlying principles, suggesting that foundational knowledge is a long-term asset in the tech industry [20][23]. Group 4: Course Content Overview - The course syllabus includes comprehensive topics such as energy functions, basic classification algorithms, neural network components, and probabilistic machine learning [26]. - Advanced topics covered in the course include reinforcement learning, ensemble methods, and Bayesian machine learning, indicating a thorough approach to machine learning education [27]. Group 5: Classic Papers and Their Impact - The article references several classic papers that have significantly influenced machine learning, such as the REINFORCE algorithm and the introduction of Variational Autoencoders (VAEs) [30][32][34]. - These foundational works are crucial for understanding modern machine learning techniques and their applications [30][32].

Core Viewpoint - The TIOBE Index for May 2025 highlights Python's dominance in the programming language landscape, achieving a market share of 25.35%, a significant increase of 2.2% from the previous month, marking a rare and substantial lead over its closest competitor, C++ [1][3]. Programming Language Rankings - The top programming languages in May 2025 are as follows: 1. Python: 25.35% (+9.02%) 2. C++: 9.94% (+0.41%) 3. C: 9.71% (-0.27%) 4. Java: 9.31% (+0.62%) 5. C: 4.22% (-2.27%) 6. JavaScript: 3.68% (+0.66%) [2][24]. Python's Market Position - Python's market share surpasses C++ by over 15%, showcasing a dominant position that is uncommon in the programming language rankings [3]. - The historical context indicates that only Java in 2001 had a higher market share than Python currently does [1]. Limitations of Python - Despite its popularity, Python has two main limitations: 1. Performance issues due to being an interpreted language, which inherently runs slower [6]. 2. Higher frequency of runtime errors, as many bugs are only discovered during execution [6][4]. - Critical applications, such as aerospace control systems, still rely on languages like C++ and Java due to these limitations [5][4]. Factors Contributing to Python's Popularity - Python's simplicity and ease of learning have made it the preferred language for many entering the programming field, especially as the demand for programming talent grows amid digital transformation [11]. - The language's extensive ecosystem, including libraries like NumPy, Pandas, TensorFlow, and PyTorch, has further solidified its position in various domains [12][13]. Application Areas of Python - Python is widely used in several fields: - Data analysis: 50% of respondents use Python for this purpose [16]. - Web development: 49% [16]. - DevOps and automation: 35% [16]. - Machine learning: 31% [16]. - Educational purposes: 28% [16]. - Software testing: 26% [16]. - The language is also utilized in scientific computing, numerical simulations, and web development frameworks like Django and Flask [22].

一个年仅18岁的高中生，独立操刀人生首个科研项目，就作为唯一作者在天文学领域的顶刊发表论文，并且凭此夺得重要科学奖项。这不是励志故事中的文 学情节，而是现实世界中的真人真事。本文来自微信公众号：返朴 （ID：fanpu2019），作者：小叶 18岁高中生Matteo Paz的科研生涯才刚刚开始，但他已经取得了令人羡艳的成就：自己的首个科研成果发表在天体物理领域颇具影响力《天文学杂志》 （The Astronomical Journal）上。该刊创刊于1849年，是天文学领域历史最悠久、持续出版的专业期刊之一，长期以来积累了极高的学术声誉。 而且，凭借这一成果，Paz摘得了曾被美国前总统乔治·布什誉为"美国科学界超级碗"的再生元科学人才奖（Regeneron Science Talent Search，以下简称STS） 桂冠，并荣获高达25万美元的奖金。 求知若渴的学生 Matteo Paz就读于美国加州帕萨德纳市的一所高中，世界最顶尖的百年理工类科学研究型高等学府——加州理工学院（以下简称"加州理工"）便坐落于该 市，整座城市洋溢着浓厚的学术氛围。 从小学开始，Paz的母亲就定期带他去参加加州理工举办的免费天 ...

推动关键基础设施 向软件定义平台迁移 Paul Parkinson 航空航天与国防领域现场工程总监，EMEA 执行摘要 关键基础设施中使用的嵌入式系统目前正在经历一场巨大的变革。历史 上，这些系统具有不同程度的网络连接性，执行固定的功能，并且可能在 现场手动升级，作为长服役周期的一部分。如今，无处不在的网络连接加 速了智能边缘嵌入式系统的创新。这推动了对增强应用功能的需求，这些 功能不仅执行传统的自动化和控制功能，还增加了更高的智能性。 多个行业对设备支持更高智能的需求日益增长，以实现从自动化系统向自 主系统的过渡。这推动了对基于开放标准的软件定义架构的技术需求，以 便将多个应用（包括具有不同安全等级和使用多种操作系统的应用）整合 到通用计算平台上。这种方法支持应用迁移、可移植性和互操作性，避免 了被锁定在专有架构中。 风河开物 Hypervisor 通过提供支持基于开放标准的软件定义架构的灵活虚 拟化平台，满足了这些共性需求。它基于经过验证的软件技术，能够满足 航空航天、汽车、国防、工业和医疗市场的安全认证要求。 目录 | 执行摘要 2 | | | --- | --- | | 嵌入式系统从自动化向自主性的演 ...

Core Insights - A joint team from Lanzhou University and Ohio State University has developed a high-integration, self-powered, wireless smart insole capable of real-time foot pressure monitoring and precise identification of multiple exercise states [1][2] Group 1: Technology and Innovation - The smart insole integrates 22 pressure sensors, creating a dense sensing network that captures minute pressure changes on the foot [2] - The insole utilizes a machine learning model based on support vector machines to accurately identify eight common exercise states, including sitting, standing, walking, running, jumping, and squatting [2] Group 2: Applications and Benefits - The smart insole can help monitor the gait of elderly individuals, allowing family members to receive real-time updates on their walking data and alerting them to any abnormalities or fall risks [3] - For fitness enthusiasts, the insole can analyze foot pressure distribution during activities like running and jumping, aiding in correcting improper exercise postures to prevent injuries [3] - Parents can use the smart insole to monitor their children's gait, enabling early detection of potential foot issues [3] Group 3: Energy Solutions - To address the power supply challenge, the research team developed a flexible perovskite solar cell that can be attached to the shoe's exterior, efficiently converting sunlight into electrical energy [3] - This solar cell allows the smart insole to operate for extended periods without frequent recharging, eliminating dependence on external power sources [3]

今年是"德勤高科技高成长"项目进入中国的第20年。本届评选以"创新无界，韧性生长"为主题，强调企 业正不断打破技术边界，以跨域融合的创新势能重构产业竞争格局。 德勤高科技高成长项目全国主管合伙人赵锦东表示，德勤通过30年的全球评选经验发现，真正的行业领 军者往往具备持续重视研发、学习AI等先进技术、坚持可持续发展和推动生态协同等特质，这些维度 都将成为本届评选的关键观测点。(完) 程中表示，在当今全球经济快速变革的时代，科技创新已成为推动新一轮产业革命和经济增长的核心动 力。国家正以前所未有的力度推动科技自立自强。根据德勤中国"高科技高成长"报告，人工智能与机器 学习、云计算、大数据等新兴技术正在深刻改变企业的运营模式和价值创造方式。 但程中也提醒，在这一过程中，企业面临诸多挑战，包括技术研发投入成本高、人才短缺、技术更新迭 代快等。面对这些挑战，企业需要以长期战略视角，从外到内、自上而下审视评估，并通过数字化底座 建立快速响应机制。同时，可持续发展已成为企业核心战略的重要组成部分，将绿色低碳融入业务流 程，不仅是履行社会责任的体现，更是企业实现高质量增长的关键路径。 （文章来源：中国新闻网） 中新网北京5月 ...

在当前贸易战带来的市场波动中，投资者如果想要寻求安全避风港，网络安全股或许是一个不错的选 择，BCA Research表示。 由于特朗普政府宣布加征关税，近几周市场剧烈震荡，标普500指数一度从2月创下的历史高点回落近 20%。但随着贸易紧张局势有缓和迹象，标普自4月8日以来反弹13%，目前年内仅下跌约5%。 尽管如此，由于缺乏实质性的进展，许多投资者仍不敢完全相信这次4月中旬的反弹。在最近的一份报 告中，BCA Research向希望对冲波动的投资者提出了一个解决方案：投资网络安全股。 "这是一个以本土市场为主的服务型行业，具有更防御性的特质，市场波动性（Beta值）低于整个科技 行业，"首席策略师Irene Tunkel写道，"它受到关税的影响较小，甚至可能从地缘政治紧张中受益，因为 客户为了防范国际网络攻击和网络犯罪而寻求保护。" Global X Cybersecurity ETF（BUG）：2025年以来上涨6%，管理费0.51%，资产规模10.5亿美元。 Amplify Cybersecurity ETF（HACK）：今年迄今上涨2%，管理费0.6%，资产接近20亿美元。 本文源自：金融界 展望未 ...