转自：北京日报客户端 记者近日从京东获悉，今年该公司将面向2025届毕业生提供1.8万余个岗位。数据显示，截至4月30日， 京东体系员工总数已超过72万人，其中快递小哥、运输司机、分拣员工等一线员工总数超过50万人。 "非常惊喜！能在实习后通过转正述职，提前锁定正式校招offer（入职通知）。"去年正式入职京东的晓 韦说，公司为大学生人才设置了快速成长通道，他在入职后的短短一年间连获两次晋升，成长为一名能 够独当一面的采销人员。 京东集团雇主品牌负责人石玉介绍，公司在连续三年累计面向在校生提供5万多个岗位的基础上，今年 面向2025届毕业生再提供1.8万余个岗位，核心岗位薪资提升20%。同时，今年5月，京东启动了面向全 球技术人才招聘的"顶尖青年技术天才计划"，在新兴领域持续提供更多优质岗位，涵盖多模态大模型与 应用、机器学习、搜索推荐广告、空间与具身智能、高性能与云计算、大数据等前沿领域。 新技术催生新职业，公司近年来增添了许多新岗位，例如"大模型+"广告智能投放岗、"AI+"医疗服务 岗、家用机器人研发岗、无人机飞行师等等。 "有了'五险一金'，心里踏实也更有奔头。"今年3月成为京东外卖全职骑手的杨晶泽说 ...

Core Viewpoint - The article discusses the challenges faced by traditional and emerging companies in the NPU (Neural Processing Unit) market, emphasizing the need for a more integrated approach to matrix and general computing rather than relying on separate engines [1][4]. Group 1: Market Dynamics - The NPU IP licensing market is crowded with competitors offering various solutions, with many traditional CPU, DSP, and GPU IP providers entering the NPU accelerator space to maintain competitiveness [1][2]. - Leading IP companies have created similar AI subsystems that combine traditional cores with hardwired accelerators, resulting in a lack of differentiation in their offerings [2][4]. Group 2: Architectural Limitations - The existing architectures require algorithm partitioning to run on two engines, which works well for a limited number of algorithms but struggles with newer models like Transformers that require a broader set of graph operators [4][5]. - Traditional IP companies opted for short-term solutions by integrating matrix accelerators with existing processors, which has led to a technological trap as they now face the need for more advanced solutions [4][5]. Group 3: Long-term Challenges - The shift towards a programmable NPU capable of handling a wide range of graph operators is necessary but requires significant investment and time, which traditional companies have been reluctant to commit to [5]. - The "innovator's dilemma" is highlighted, where traditional companies must reconcile the need for new architectures with the legacy value of their existing IP cores, leading to a cycle of outdated solutions [5].

# SynBio团队 | 中科院苏州医工所马富强 在人工生命体精准编程的"黄金时代"，合成生物学作为融合工程学、计算机科学与分子生物学的交叉学科， 正通过"设计-构建-测试"的循环模式重塑生物制造范式。这一领域不仅被列为全球科技竞争的战略高地，更在 医药研发、碳中和、农业升级等关乎国计民生的赛道上展现出颠覆性潜力。 其核心突破点聚焦 三大支柱 ： 三大支柱技术如同精密咬合的齿轮，共同决定了细胞合成工厂的效率 。 【SynBioCon】 获 悉，近日， 苏州医工所马富强研究团队 围绕上述合成生物学三大支柱技术开展了系统工 作： 图2. 利用深度学习辅助生成新型启动子的两种方法 ： 左 图 ， 通过对现有启动子引入突变或随机生成新序列 来创建新的序列。这些新生成的序列通过启动子识别模型进行筛选，以验证其功能 ；右 图 ， 使用扩散模型 或生成对抗网络（GANs）来生成新的启动子。扩散模型通过添加高斯噪声逐步生成启动子序列。GANs 由生 成器和判别器组成，生成器负责生成假样本，而判别器用于区分真实样本和生成的假样本。通过训练过程不 断优化生成器的性能，使其能够生成更逼真的启动子序列。 工作1 ： 新型酶资源 的 ...

Core Viewpoint - The automotive industry is facing increasing challenges in ensuring the reliability and quality of integrated circuits and systems, particularly as vehicles become more reliant on advanced driver-assistance systems (ADAS) and software-defined functionalities [2][4][19]. Group 1: Challenges in Automotive Chip Development - The traditional development cycle for automotive chips is five to seven years, but the shift towards ADAS and complex infotainment systems has accelerated this process [2][4]. - Achieving automotive-grade quality with a defect rate below one part per million (DPPM) is a significant challenge, necessitating innovative testing methods [2][4]. - Manufacturers are under pressure to maintain low testing costs while ensuring high quality, creating a delicate balance [2][4][5]. Group 2: Advances in ADAS and Software-Defined Vehicles - ADAS has driven the automotive industry towards smaller technology nodes and more complex systems, transitioning to fully software-defined vehicles (SDVs) [4][5]. - The shift to advanced nodes below 5nm presents reliability and safety challenges, particularly for systems expected to operate for extended periods [4][5][19]. - Most new vehicles are currently at Level 2 or Level 3 automation, with increasing safety standards required for higher levels of automation [7][8]. Group 3: Testing and Quality Assurance - Automotive chips must undergo rigorous testing at three temperature extremes to simulate operational conditions, as defined by AEC-Q100 standards [9]. - Machine learning-based anomaly detection methods are increasingly used to enhance quality levels close to zero DPPM [9][10]. - Advanced fault models are being developed to better simulate common defects in silicon, improving testing accuracy [10]. Group 4: Virtual Testing and Predictive Maintenance - Virtual testing is becoming essential to reduce the complexity of real-world testing, allowing for parallel development and faster time-to-market [8][19]. - Continuous monitoring and feedback throughout the vehicle's lifecycle are critical, especially as more advanced nodes are introduced [19]. - On-chip monitoring and machine learning are being utilized to track performance degradation and predict failures [18][19]. Group 5: Future Directions in Automotive Testing - The industry is moving towards chiplet-based designs to improve yield and reuse rates while managing the complexity of advanced packaging [12][13]. - Acoustic and optical technologies are being employed to analyze inter-chip bonding characteristics, which are crucial for reliability [14]. - System-level testing is becoming a standard requirement to ensure that both hardware and software meet functional and non-functional requirements [16].

Core Viewpoint - The article emphasizes the integration of AI and multi-omics analysis, highlighting the importance of machine learning in biological data analysis and the educational offerings to enhance skills in this area [1][2][3]. Group 1: Course Features - The course is designed for beginners, providing a comprehensive introduction to R programming for bioinformatics analysis [1]. - It covers various popular directions in multi-omics research, including metabolomics, proteomics, microbiomics, and transcriptomics, keeping pace with scientific advancements [1]. - The teaching model includes one-on-one guidance and a flexible learning pace with live classes and recorded sessions [3]. Group 2: Course Content Overview - The first session focuses on interpreting CNS papers using Deepseek for efficient reading and summarizing multi-omics data analysis methods [2]. - Subsequent sessions cover the design of multi-omics research projects, programming basics in R, and machine learning applications in metabolomics, proteomics, and microbiomics [2][4][6]. - Advanced topics include the use of various machine learning models like xgboost, lasso, and random forests for intelligent data analysis [3][10]. Group 3: Practical Applications - The course includes hands-on experience with real CNS article source codes, allowing participants to replicate high-level research [3]. - It emphasizes the application of machine learning techniques in analyzing metabolomics and proteomics data, including regression models and network analysis [4][9]. - The integration of multi-omics data for comprehensive analysis is highlighted, showcasing the potential for significant insights in biomedical research [12][14].

Summary of SPS Commerce (SPSC) FY Conference Call - June 10, 2025 Company Overview - SPS Commerce operates a cloud-based network connecting retailers and suppliers for efficient supply chain information exchange, primarily focused on the ordering process [4][5] - The company has the largest EDI (Electronic Data Interchange) network in North America, emphasizing community enablement to connect suppliers digitally to retailers [6][10] Key Points Industry and Market Position - SPS Commerce's total addressable market (TAM) is now estimated at $11 billion, up from a previous estimate of $5 billion, reflecting growth in the EDI market [14][15] - The U.S. accounts for approximately $6.5 billion of the TAM, with a potential global supplier base of about 275,000, including 147,000 in the U.S. [15] Revenue Growth and Customer Acquisition - The company reported 300 net new customers in Q1, indicating a reacceleration in network expansion, driven by community enablement programs [10][12] - Revenue growth can come from new customer acquisition or increasing average revenue per customer, influenced by the nature of community enablement programs [11][12] Pricing Model - The core EDI pricing model is based on the number of connections suppliers have within the network, with a fixed fee per connection making up about 80% of revenue [21][23] - A small variable component is based on the number of documents exchanged, which remains stable despite fluctuations in consumer spending [21][23] Research and Development (R&D) - R&D spending has been consistent at about 10% of sales, focusing on enhancing existing products and internal tools, with a growing emphasis on AI and machine learning [24][26] - The company is also developing rule books to help retailers manage their supply chain processes more effectively [26] New Product Development - SPS recently launched a manufacturing supply chain performance suite aimed at co-packers and manufacturers, addressing upstream supply chain needs [28][29] - The company has also acquired revenue recovery software to help suppliers manage chargebacks and deductions from retailers, presenting a significant cross-selling opportunity [32][36] Financial Metrics and Goals - SPS aims for adjusted EBITDA margins of at least 35%, with current margins in the upper twenties [47] - The company targets gross margins in the low to mid-seventies, with ongoing investments in customer experience expected to drive improvements [48][45] International Growth - Currently, 17% of sales are international, with growth ambitions focused on expanding direct sales and community enablement in Europe following the acquisition of Thai Kinetics [51][52] Data Monetization Opportunities - SPS is exploring ways to monetize the data generated from transactions on its network, potentially offering insights for demand planning and forecasting [73] Additional Insights - The company has a strong focus on community enablement programs, which not only drive supplier connections but also enhance revenue opportunities through existing customer relationships [56] - The integration of recent acquisitions is ongoing, with efforts to standardize pricing and service delivery models [40][41] This summary encapsulates the key points discussed during the SPS Commerce FY Conference Call, highlighting the company's strategic initiatives, market position, and financial outlook.

Revvity (RVTY) FY Conference Summary Company Overview - **Company**: Revvity (formerly PerkinElmer) - **Industry**: Life Sciences Tools and Diagnostics Key Points and Arguments Market Environment and Company Adaptation - The current market is dynamic with uncertainties due to policy changes, tariffs, and challenges in pharma, biotech, and academia sectors [3][4][5] - Revvity's diverse portfolio has demonstrated resilience amidst these challenges, with 60% of revenue now coming from diagnostics and software [4][5][8] Financial Performance and Growth - Revvity's growth rate is at the top end of its publicly traded peer group, with a long-range plan (LRP) growth target of 6-8%, compared to 3-5% previously [4][7] - The company has shifted to a recurring revenue model, with over 80% of revenue now from recurring sources, improving margins from 18-20% to 28% [7][8] - Organic growth guidance for the year is set at 3-5%, with confidence in achieving this despite market uncertainties [9][12] Life Sciences Segment Challenges - The life sciences segment faces challenges due to funding shifts towards clinical work, impacting preclinical discovery [14][15] - Revvity's differentiated product portfolio in consumables is expected to maintain growth despite these challenges [15][20] Software Business - Revvity's software business is unique, functioning as an ERP for researchers, with 48 out of the top 50 pharma companies using its software [26][28] - The software segment is expected to grow significantly, contributing to overall margin improvement [56] Diagnostics and Growth Opportunities - Immunodiagnostics represent a significant growth opportunity in the U.S., with current market penetration at 15-20% compared to a historical 35-40% [30][31] - The company is focusing on expanding its offerings in reproductive health and rare disease testing, leveraging partnerships for growth [40][41] Competitive Landscape in China - Revvity faces intense competition in China, but differentiates itself through proprietary assays and a focus on complex diseases [35][36] - The company is adapting to local market conditions and regulatory environments to maintain growth and profitability [36][39] Capital Allocation and M&A Strategy - Revvity has been active in M&A, completing 13 acquisitions in 22 months to enhance its portfolio [60][62] - Current capital allocation focuses on share buybacks, with a balanced approach to future M&A opportunities [64][68] Long-term Outlook - The company aims for mid-30s operating margins in a normalized market environment, with equal opportunities for margin expansion across life sciences and diagnostics [54][56] - Revvity's strong portfolio and execution strategy position it well for long-term growth despite current macroeconomic challenges [72][74] Additional Important Insights - The company emphasizes innovation and automation in its product offerings to enhance researcher productivity, especially in a budget-constrained environment [47] - Revvity's software and diagnostics segments are seen as critical to its long-term success, with ongoing efforts to improve customer stickiness and market penetration [25][28][70]

Core Concept - The article discusses the evolution and impact of algorithms in various fields, highlighting their increasing complexity and the balance between transparency and power [1][3]. Group 1: Social Media Algorithms - Facebook's algorithm influences the content seen by its 2.8 billion monthly users, utilizing a complex system that evaluates around 100,000 factors to rank posts [5]. - The lack of transparency in Facebook's algorithm has raised concerns about its prioritization of sensational content over socially beneficial information [5]. Group 2: Weather Forecasting Algorithms - The UK's weather forecasting relies on the Unified Model algorithm, which processes data from meteorological stations and satellites, achieving a 92% accuracy rate for temperature predictions within 2°C [6]. Group 3: Image Compression Algorithms - The JPEG compression algorithm allows for efficient image sharing online by reducing data size while maintaining quality, based on human visual perception [7][9]. Group 4: Search Engine Algorithms - Google's PageRank algorithm revolutionized search by ranking web pages based on the quantity and quality of links, though it has evolved into a more complex system analyzing hundreds of factors [10][12]. Group 5: Financial Algorithms - Algorithms dominate financial trading, with high-frequency trading leveraging microsecond differences across global exchanges to execute numerous trades for small profits [13][15]. - More complex algorithms are now incorporating AI and machine learning, analyzing a wider range of variables beyond traditional market data [15]. Group 6: Encryption Algorithms - The RSA algorithm enables secure communication by using a pair of keys for encryption and decryption, relying on the difficulty of factoring large prime numbers [16][17]. Group 7: Healthcare Algorithms - Algorithms are increasingly used in healthcare for triaging patients and diagnosing conditions, with some systems outperforming human doctors in interpreting medical images [18]. Group 8: Internet Protocol Algorithms - The Internet Protocol Suite governs data exchange over the internet, ensuring reliable communication even when certain routes are disrupted [19][21]. Group 9: Scientific Research Algorithms - The Monte Carlo algorithm, developed during WWII, simulates complex physical phenomena to predict outcomes, showcasing the power of computational methods in scientific research [22].

Core Points - The third United Nations Ocean Conference, co-hosted by France and Costa Rica, opened in Nice, France on June 9 [1] - The China National Ocean Information Center led a side event titled "Smart Ocean: Innovative Science Leading Action for a Sustainable Future" [1] - The Ministry of Natural Resources of China publicly released the China Global Ocean Fusion Dataset 1.0, which integrates over 40 different data sources and includes China's independent ocean observations [1] Summary by Categories Dataset Features - The China Global Ocean Fusion Dataset (CGOF1.0) has a time span of up to 60 years and a spatial resolution of 10 kilometers [1] - The dataset incorporates advanced AI technologies such as deep learning, transfer learning, and machine learning, resulting in improved accuracy compared to mainstream foreign datasets [1] International Collaboration - The event highlights China's commitment to international collaboration in ocean data sharing and sustainable ocean management [1] - The integration of diverse data sources reflects a global effort to enhance oceanic research and monitoring [1]

Core Insights - Quantitative stock investment is expected to perform exceptionally well in 2025 after years of stagnation, driven by models based on factors and machine learning that have shown strong performance amid market volatility and political noise [1][6] Group 1: Strategy Recovery - Traditional long/short factor models and newer machine learning-based strategies are experiencing a revival, with the global bottom-up stock factor strategy rising over 9% this year, successfully navigating market volatility [2][3] - The top-down factor indices covering regions like Europe, the US, and Japan have also shown robust growth, particularly value and momentum strategies outside the US [2] Group 2: Regional and Strategy Performance - Europe has been the leading region for factor performance in 2025, with value strategies achieving the best relative and absolute returns, although valuation gaps have narrowed significantly [3] - Machine learning models from Société Générale have performed strongly, with a newly launched mean-reversion strategy yielding a return of 4.1%, outperforming basic reversal models [3] Group 3: Investment Themes and Strategy Outlook - Société Générale is optimistic about defensive stock income strategies, focusing on companies with strong balance sheets and high dividend yields, particularly in utilities, telecom, and energy sectors [4] - The US small-cap value strategy, excluding distressed stocks, has outperformed benchmark indices, emphasizing the importance of balance sheet strength as credit conditions tighten [4] - The "strong balance sheet" trade is supported as an alternative hedging strategy against high-yield credit risk, maintaining positive growth in 2025 [4] Group 4: Outlook for the Second Half of 2025 - Despite the strong performance of European value strategies, a cautious outlook is held for the second half of 2025 due to rising market volatility and valuation spreads nearing historical norms [5] - The easy gains from European value stocks may be over, influenced by geopolitical uncertainties and increasing earnings risks [5]