Core Viewpoint - The article discusses the limitations of copper and fiber interconnects in next-generation data centers and introduces a third solution, e-Tube, which aims to support the growing demands of AI workloads and data bandwidth requirements [1][10][16]. Group 1: Challenges in Data Center Expansion - Data center AI accelerator clusters face increasing complexity due to the emergence of new technologies, particularly generative AI and large language models (LLMs), which are pushing data bandwidth beyond traditional interconnects, rapidly doubling to 800G and soon reaching 1.6T [1]. - The need for improved performance, cost control, and energy efficiency presents significant challenges for network operators [4]. Group 2: Limitations of Current Technologies - Data centers currently rely on 400G and 800G network equipment, using copper cables for short distances and fiber optics for long distances, but both technologies are approaching their respective limits in terabit interconnect speeds [3][6]. - Copper cables, while cost-effective and reliable for short distances, suffer from channel loss due to skin effect, limiting their transmission range and scalability in high-density data centers [3][6]. Group 3: Transition to Optical Interconnects - Large-scale enterprises are shifting towards optical interconnects, such as Active Optical Cables (AOC), which can provide connections over several kilometers but come with increased complexity, power consumption, and costs, potentially up to five times that of copper cables [8]. - Optical technologies are less reliable due to performance variations with temperature changes and the eventual failure of optical components, which can also introduce significant latency [8]. Group 4: Introduction of e-Tube Technology - The e-Tube platform offers a scalable multi-terabit interconnect solution using plastic medium waveguides to transmit radio frequency data, overcoming the limitations of copper and fiber optics [10][12]. - e-Tube cables, made from low-density polyethylene (LDPE), can efficiently transmit data without the high-frequency losses associated with copper, supporting data speeds from 56G to 224G and beyond [12]. Group 5: Advantages of e-Tube - e-Tube technology results in a tenfold increase in cable coverage, fivefold reduction in weight, twofold decrease in thickness, threefold reduction in power consumption, and a thousandfold decrease in latency, all while reducing costs by three times [14]. - This technology is positioned as an ideal alternative to copper cables as data centers transition to 1.6T and 3.2T speeds, providing unique power efficiency and compatibility with existing network infrastructure [14][16].

Applovin (APP) Q1 2025 Earnings Call May 07, 2025 05:00 PM ET Speaker0 Welcome to AppLovin's earnings call for the first quarter ended 03/31/2025. I'm David Shao, head of investor relations. Joining me today to discuss our results are Adam Frueghi, our cofounder, CEO, and chairperson, and Matt Stumpf, our CFO. Please note our SEC filings to date as well as our financial update and press release discussing our first quarter performance are available at investors.app11.com. During today's call, we will be mak ...

Core Viewpoint - By 2025, artificial intelligence (AI) has transitioned from a buzzword in tech circles to an integral part of daily life, impacting various industries through applications like image generation, coding, autonomous driving, and medical diagnosis. The evolution of AI is marked by significant breakthroughs and challenges, tracing back to the Dartmouth Conference in 1956, leading to the current technological wave driven by large models [1]. Group 1: Historical Milestones - The Dartmouth Conference in 1956 is recognized as the birth of AI, where pioneers gathered to explore machine intelligence, laying the foundation for AI as a formal discipline [2][3]. - In 1957, Frank Rosenblatt developed the Perceptron, an early artificial neural network that introduced the concept of optimizing models using training data, which became central to machine learning and deep learning [4][6]. - ELIZA, created in 1966 by Joseph Weizenbaum, was the first widely recognized chatbot, demonstrating the potential of AI in natural language processing by simulating human-like conversation [7][8]. - The rise of expert systems in the 1970s, such as Dendral and MYCIN, showcased AI's ability to perform specialized tasks in fields like chemistry and medical diagnosis, establishing its application in professional domains [9][11]. - IBM's Deep Blue defeated world chess champion Garry Kasparov in 1997, marking a significant milestone in AI's capability to outperform humans in strategic decision-making [12][14]. - The 1990s to 2000s saw a shift towards data-driven algorithms in AI, emphasizing the importance of machine learning [15]. - The emergence of deep learning in 2012, particularly through the work of Geoffrey Hinton, revolutionized AI by utilizing multi-layer neural networks and backpropagation techniques, leading to significant advancements in model training [17][18]. - The introduction of Generative Adversarial Networks (GANs) in 2014 by Ian Goodfellow transformed the field of generative models, enabling the creation of realistic synthetic data [20]. - AlphaGo's victory over Lee Sedol in 2016 highlighted AI's potential in complex games requiring intuition and strategic thinking, further pushing the boundaries of AI capabilities [22]. - The development of large language models began with the introduction of the Transformer architecture in 2017, leading to models like GPT-3, which demonstrated emergent abilities and set the stage for the current AI landscape [24][26].

Core Viewpoint - The article emphasizes the importance of constructing digital channels for businesses and individuals to stand out in a competitive environment, outlining key methods for effective digital channel development [2][4]. Group 1: Channel Planning and Positioning - Companies should develop appropriate channel strategies based on their business characteristics, market demands, and competitive landscape, clearly defining the positioning and functions of each channel [2][4]. - This includes identifying target audiences, coverage areas, and service content to ensure alignment with the company's strategic direction [2]. Group 2: Building and Optimizing Digital Channels - Establishing a diversified channel network is essential, incorporating official websites, social media platforms, and e-commerce sites to meet varied user needs [2][5]. - Enhancing user experience through improved interface design, increased interactivity, and simplified processes is crucial for boosting user satisfaction and loyalty [2]. - Strengthening brand communication via digital channels through online activities, coupons, and membership systems can attract user participation and increase channel activity and conversion rates [2]. Group 3: Data-Driven and Intelligent Operations - Utilizing big data technologies to collect and analyze user and market data is vital for understanding user needs and market changes, supporting channel optimization and decision-making [2][5]. - Implementing intelligent technologies such as artificial intelligence and machine learning can automate and enhance the efficiency and quality of digital channel operations [2]. Group 4: Channel Coordination and Integration - Effective integration of various digital channels is necessary to achieve information sharing and resource connectivity, providing consumers with a seamless shopping experience [5]. - Strengthening collaboration among different channels can enhance brand awareness and market share through online and offline interactions [5]. Group 5: Continuous Innovation and Optimization - Keeping up with technological advancements and investing in research and talent development is essential for cultivating a workforce with digital skills [5]. - Continuously optimizing channel strategies in response to market changes and user demands ensures adaptability and competitiveness in digital channel construction [5].

Core Viewpoint - The article highlights the challenges in semiconductor parameter extraction due to the complexity of device models and the inefficiencies of traditional optimization algorithms. It introduces Keysight's ML Optimizer, a machine learning-based global optimizer that significantly improves the parameter extraction process, reducing the time from days to hours and enhancing accuracy and consistency in model fitting [1]. Group 1: Challenges in Semiconductor Parameter Extraction - The complexity of semiconductor device models has made parameter extraction increasingly challenging [1]. - Traditional optimization algorithms struggle with unclear gradient changes, often getting trapped in local optima, leading to unsatisfactory extraction results [1]. - The presence of numerous interrelated parameters in modern semiconductor models further complicates the efficiency of traditional methods, requiring engineers to break down the extraction process into lengthy sub-steps [1]. Group 2: Introduction of ML Optimizer - Keysight has launched the ML Optimizer, which utilizes machine learning to provide a revolutionary solution for semiconductor parameter extraction [1]. - The ML Optimizer can process vast amounts of data and parameters in a single step, greatly simplifying the extraction workflow [1]. - The time required for parameter extraction is reduced from several days to just a few hours, significantly enhancing work efficiency [1]. Group 3: Advantages of ML Optimizer - The ML Optimizer excels in navigating non-convex parameter spaces, overcoming the limitations of traditional methods [1]. - It employs advanced machine learning algorithms to more accurately identify global optima, improving the precision of parameter extraction [1]. - The overall consistency of model fitting is enhanced, providing a solid foundation for the accurate construction of semiconductor device models [1].

Core Viewpoint - MHmarkets aims to enhance forex trading efficiency through innovative technologies, focusing on smart algorithms, real-time data analysis, and user experience optimization. Group 1: Technology Innovation - Smart algorithms optimize trading strategies, improving market adaptability and data analysis accuracy [4][7] - Automation systems and low-latency technology ensure efficient trade execution [5][10] - Artificial intelligence and blockchain drive technological innovation, enhancing market competitiveness [7][15] Group 2: Data Analysis and Market Prediction - Big data analysis allows for more accurate market trend predictions, reducing uncertainty in decision-making [8][9] - Real-time data analysis improves market transparency and optimizes trading decisions [8][9] Group 3: User Experience Enhancement - User interface improvements and security measures enhance overall user experience [6][12] - Simplified trading processes increase operational efficiency for users [12] Group 4: Security Measures - Advanced data encryption techniques ensure user information security during transactions [13] - Account security management focuses on password complexity and two-factor authentication [13][15] - Continuous updates to security protocols help prevent hacking attempts [15] Group 5: Customer Support and Service - Multi-channel customer communication enhances customer experience and satisfaction [17] - 24/7 technical support ensures timely resolution of customer issues [18] - Personalized customer service strengthens loyalty and satisfaction [18] Group 6: Future Directions - Focus on the potential applications of AI and blockchain in forex trading [19] - Strategies for optimizing user experience and expanding into global markets [19][20]

Core Insights - In 2025, investors face unprecedented complexities due to global economic fluctuations, Federal Reserve policy shifts, and geopolitical conflicts, leading to a revaluation of gold and the dollar [1] - Velos Markets is leveraging technology-driven investment strategy optimization tools to provide professional investors with pathways to certainty in opportunities [1] Market Trends - The gold market serves as a safe haven during economic turmoil, with price fluctuations driven by multiple factors, including Federal Reserve monetary policy signals [4] - Historical data analysis by Velos Markets indicates a 78% probability of gold price increases within three months when the U.S. core PCE price index rises over 3% year-on-year [4] - Velos Markets employs a unique three-dimensional analysis model to track central bank gold reserves, analyze CME gold futures positions, and monitor geopolitical risk indices [4] Strategy Logic - Velos Markets integrates machine intelligence with investment wisdom, offering dynamic asset allocation engines that automatically generate asset portfolios based on user risk assessments [5] - Conservative strategy includes 40% allocation to gold spot contracts, complemented by treasury futures and money market funds, while aggressive strategy allocates 25% to cryptocurrency derivatives [5] - The system dynamically adjusts strategies based on market conditions, such as increasing gold hedge positions when the VIX index rises over 15% in a day, demonstrating effective risk management [5] Tool Evaluation - Defensive tools include gold spot contracts optimized for liquidity, with cost savings comparable to a 15% discount on highway tolls during peak times [7] - Balanced tools utilize volatility-neutral combinations of gold ETFs and dollar index futures, enhancing market noise filtering efficiency to 82% during high-frequency events [8] - Aggressive tools integrate 13 technical indicators and fundamental factors to generate visual trading signals, improving the Sharpe ratio of trend strategies from 1.3 to 2.1 [9] Operational Guidelines - The first step involves account diagnosis, where users input existing holdings and risk tolerance to generate a "health score" [11] - The second step is strategy simulation, allowing users to observe performance during historical crises [12] - The third step involves selecting a main strategy framework based on diagnostic results [12] - The fourth step focuses on real-time monitoring of key warning indicators [12] - The final step emphasizes dynamic optimization and iteration of strategies based on the latest market data [12] Conclusion - Velos Markets aims to help investors establish a "probability advantage" by transforming ambiguous experiential judgments into executable signal systems [13] - The integration of technology and investment wisdom is redefining the keys to certainty in opportunities [14]

Quantitative Models and Construction Methods GAN_GRU Model - **Model Name**: GAN_GRU - **Model Construction Idea**: The GAN_GRU model utilizes Generative Adversarial Networks (GAN) for processing volume-price time series features and then uses the GRU model for time series feature encoding to derive the stock selection factor[2][9]. - **Model Construction Process**: 1. **GRU Model**: - **Basic Assumptions**: The GRU+MLP neural network stock return prediction model includes 18 volume-price features such as closing price, opening price, trading volume, turnover rate, etc[10][13][15]. - **Training Data and Input Features**: All stocks' past 400 days of 18 volume-price features, sampled every 5 trading days. The feature sampling shape is 40*18, using the past 40 days of volume-price features to predict the cumulative return of the next 20 trading days[14]. - **Training and Validation Set Ratio**: 80%:20%[14]. - **Data Processing**: Extreme value removal and standardization in the time series for each feature within the 40 days, and cross-sectional standardization at the stock level[14]. - **Model Training Method**: Semi-annual rolling training, i.e., training the model every six months and using it to predict the returns for the next six months. Training dates are June 30 and December 31 each year[14]. - **Stock Selection Method**: Select all stocks in the cross-section, excluding ST and stocks listed for less than six months[14]. - **Training Sample Selection Method**: Exclude samples with empty labels[14]. - **Hyperparameters**: batch_size is the number of stocks in the cross-section, optimizer Adam, learning rate 1e-4, loss function IC, early stopping rounds 10, maximum training rounds 50[14]. - **Model Structure**: Two GRU layers (GRU(128, 128)) followed by MLP layers (256, 64, 64). The final output predicted return pRet is used as the stock selection factor[18]. 2. **GAN Model**: - **Introduction**: GANs consist of a generator and a discriminator. The generator aims to generate realistic data, while the discriminator aims to distinguish between real and generated data[19]. - **Generator**: - **Loss Function**: $$L_{G}\,=\,-\mathbb{E}_{z\sim P_{z}(z)}[\log(D(G(z)))]$$ where \(z\) represents random noise (usually Gaussian distributed), \(G(z)\) represents the data generated by the generator, and \(D(G(z))\) represents the probability that the discriminator judges the generated data as real[20][21]. - **Training Process**: Generate noise data, convert noise data to generated data using the generator, calculate generator loss, and update generator parameters through backpropagation[21][22]. - **Discriminator**: - **Loss Function**: $$L_{D}=-\mathbb{E}_{x\sim P_{d a t a}(x)}[\log\!D(x)]-\mathbb{E}_{z\sim P_{z}(z)}[\log(1-D(G(z)))]$$ where \(x\) is real data, \(D(x)\) is the probability that the discriminator judges the real data as real, and \(D(G(z))\) is the probability that the discriminator judges the generated data as real[23]. - **Training Process**: Sample real data, generate fake data, calculate discriminator loss, and update discriminator parameters through backpropagation[24][25]. - **GAN Training Process**: Alternately train the generator and discriminator until convergence[25][26]. 3. **GAN Feature Generation Model Construction**: - **LSTM Generator + CNN Discriminator**: To retain the time series nature of the input features, the LSTM model is used as the generator. The CNN model is used as the discriminator to match the two-dimensional volume-price time series features[29][30][33]. - **Feature Generation Process**: Input original volume-price time series features (Input_Shape=(40,18)), output volume-price time series features processed by LSTM (Input_Shape=(40,18))[33]. Model Evaluation - **Evaluation**: The GAN_GRU model effectively combines GAN and GRU to process and encode volume-price time series features, providing a robust stock selection factor[2][9]. Model Backtest Results - **GAN_GRU Model**: - **IC Mean**: 11.73%[37][38] - **Annualized Excess Return**: 24.89%[37][38] - **Latest IC**: 0.22% (as of April 28, 2025)[37][38] - **IC Mean in the Past Year**: 11.44%[37][38] - **Annualized Return**: 36.06%[38] - **Annualized Volatility**: 23.80%[38] - **Information Ratio (IR)**: 1.66[38] - **Maximum Drawdown**: 27.29%[38] - **Turnover Rate**: 0.83[38] - **ICIR**: 0.90[38] Quantitative Factors and Construction Methods GAN_GRU Factor - **Factor Name**: GAN_GRU - **Factor Construction Idea**: The GAN_GRU factor is derived from the GAN_GRU model, which processes volume-price time series features using GAN and encodes them using GRU[2][9]. - **Factor Construction Process**: The factor is generated by the GAN_GRU model, which includes the steps of feature processing by GAN and encoding by GRU as described in the model construction process[2][9][33]. - **Factor Evaluation**: The GAN_GRU factor shows strong performance in stock selection, with high IC values and significant excess returns[2][9]. Factor Backtest Results - **GAN_GRU Factor**: - **IC Mean**: 11.73%[37][38] - **Annualized Excess Return**: 24.89%[37][38] - **Latest IC**: 0.22% (as of April 28, 2025)[37][38] - **IC Mean in the Past Year**: 11.44%[37][38] - **Annualized Return**: 36.06%[38] - **Annualized Volatility**: 23.80%[38] - **Information Ratio (IR)**: 1.66[38] - **Maximum Drawdown**: 27.29%[38] - **Turnover Rate**: 0.83[38] - **ICIR**: 0.90[38]

在商业领域，现代化是指持续调整和升级组织的各个方面，以适应当代技术、运营和市场趋势的过程。拥抱现代化并培育前瞻性文化有 助于企业保持竞争力和相关性。 IT 现代化服务市场份额（按类型划分）（市场份额基于 2024 年收入，持续更新） IT 现代化服务是指更新和升级组织的信息技术基础设施、系统和流程，以符合当前行业标准和最佳实践的过程。这可能涉及将遗留系统 迁移到云平台、实施新的软件和硬件解决方案、增强网络安全措施以及提高整体效率和性能。通过现代化 IT 环境，组织可以提高生产 力、降低成本、增强安全性，并为未来的增长和创新做好更充分的准备。最终，现代化使企业能够提高效率、敏捷性和满足客户期望的 能力，同时在行业变革和新兴机遇面前保持韧性和适应性。 根据 QYResearch 发布的最新市场研究报告《 2025-2031 年全球 IT 现代化服务市场报告 》， IT 现代化服务市场近年来经历了显著的增长 和转型。就市场规模而言，预计全球 IT 现代化服务市场规模将从 2024 年的 307.3 亿美元增长到 2031 年的 647.1 亿美元，预测期内的复 合年增长率 (CAGR) 为 11.38% 。这一增长 ...

Investment Rating - The report maintains a "Recommended" rating for the smart home industry [8][27]. Core Insights - The smart home industry is experiencing rapid growth driven by three main factors: continuous release of demand for consumption upgrades and elderly-friendly renovations, technological innovations, and strong policy support [27]. - Recent policy changes in Fujian Province have expanded the scope of the old-for-new appliance program, providing a 15% subsidy on the final sales price for 21 categories of home appliances [3][18]. - The first quarter saw a 19.3% year-on-year growth in the retail sales of household appliances and audio-visual equipment, indicating the effectiveness of the consumption upgrade policies [4][19]. Summary by Sections Market Review - In the past two weeks (April 14-25, 2025), the Shanghai Composite Index rose by 1.76%, while the smart home index (399996.SZ) increased by 1.06%, underperforming the Shanghai Composite by 0.69 percentage points [2][13]. - Year-to-date, the smart home index has gained 0.22%, outperforming the Shanghai Composite by 1.91 percentage points [13][14]. Industry Policy Tracking - On April 22, 2025, Fujian Province announced an adjustment to the old-for-new appliance policy, expanding the subsidy to 21 categories of appliances, with a maximum subsidy of 2000 yuan per product [3][18]. Industry News Tracking - The first quarter's retail sales of household appliances and audio-visual equipment showed a significant increase of 19.3% year-on-year, reflecting the positive impact of the old-for-new policy [4][19]. - Gree Electric's board of directors has undergone a leadership change, with Dong Mingzhu re-elected as chairperson [20]. - Cixi's small home appliance sector has shown resilience against U.S. tariff pressures, with domestic sales growing over 30% [21]. Investment Recommendations - Leading home appliance companies like Haier, Midea, Gree, and Hisense are demonstrating strong resilience due to their globalized operations and localized production capabilities [5][26]. - The report emphasizes that the smart home industry is set to benefit from the ongoing consumption upgrade and technological advancements, maintaining a "Recommended" rating for the industry [27].