机器学习

Search documents
Alphatec (ATEC) - 2025 Q2 - Earnings Call Transcript
2025-07-31 21:30
Alphatec (ATEC) Q2 2025 Earnings Call July 31, 2025 04:30 PM ET Speaker0Good afternoon, everyone, and welcome to the webcast of ATEC's Second Quarter Financial Results. We would like to remind everyone that participants on the call will make forward looking statements. These statements are based on the current expectations and are subject to uncertainties that could cause actual results to differ materially. These uncertainties are detailed in documents filed regularly with the SEC. During this call, you ma ...
机器学习因子选股月报(2025年8月)-20250730
Southwest Securities· 2025-07-30 05:43
Quantitative Factors and Construction Factor Name: GAN_GRU Factor - **Construction Idea**: The GAN_GRU factor is derived by processing volume-price time-series features using a Generative Adversarial Network (GAN) model, followed by encoding these time-series features with a Gated Recurrent Unit (GRU) model to generate a stock selection factor [4][13][41] - **Construction Process**: 1. **Input Features**: 18 volume-price features such as closing price, opening price, turnover, and turnover rate are used as input data. These features are sampled every 5 trading days over the past 400 days, resulting in a feature matrix of shape (40,18) [14][17][18] 2. **Data Preprocessing**: - Outlier removal and standardization are applied to each feature over the 40-day time series - Cross-sectional standardization is performed at the stock level [18] 3. **GAN Model**: - **Generator**: An LSTM-based generator is used to preserve the sequential nature of the input features. The generator takes random noise (e.g., Gaussian distribution) as input and generates data that mimics the real data distribution [23][33][37] - **Discriminator**: A CNN-based discriminator is employed to classify real and generated data. The discriminator uses convolutional layers to extract features from the 2D volume-price time-series "images" [33][35] - **Loss Functions**: - Generator Loss: $$ L_{G} = -\mathbb{E}_{z\sim P_{z}(z)}[\log(D(G(z)))] $$ where \( z \) represents random noise, \( G(z) \) is the generated data, and \( D(G(z)) \) is the discriminator's output probability for the generated data being real [24] - Discriminator Loss: $$ L_{D} = -\mathbb{E}_{x\sim P_{data}(x)}[\log D(x)] - \mathbb{E}_{z\sim P_{z}(z)}[\log(1-D(G(z)))] $$ where \( x \) is real data, \( D(x) \) is the discriminator's output probability for real data, and \( D(G(z)) \) is the discriminator's output probability for generated data [27] 4. **GRU Model**: - Two GRU layers (GRU(128,128)) are used to encode the time-series features, followed by an MLP (256,64,64) to predict future returns [22] 5. **Factor Output**: The predicted returns (\( pRet \)) from the GRU+MLP model are used as the stock selection factor. The factor is neutralized for industry and market capitalization effects and standardized [22] Factor Evaluation - The GAN_GRU factor effectively captures the sequential and cross-sectional characteristics of volume-price data, leveraging the strengths of GANs for feature generation and GRUs for time-series encoding [4][13][41] --- Factor Backtesting Results GAN_GRU Factor Performance Metrics - **IC Mean**: 11.43% (2019-2025), 10.97% (last year), 9.27% (latest month) [41][42] - **ICIR**: 0.89 [42] - **Turnover Rate**: 0.82 [42] - **Annualized Return**: 38.52% [42] - **Annualized Volatility**: 23.82% [42] - **IR**: 1.62 [42] - **Maximum Drawdown**: 27.29% [42] - **Annualized Excess Return**: 24.86% [41][42] GAN_GRU Factor Industry Performance - **Top 5 Industries by IC (Latest Month)**: - Home Appliances: 27.00% - Non-Bank Financials: 23.08% - Retail: 20.01% - Steel: 14.83% - Textiles & Apparel: 13.64% [41][42] - **Top 5 Industries by IC (Last Year)**: - Utilities: 14.43% - Retail: 13.33% - Non-Bank Financials: 13.28% - Steel: 13.23% - Telecommunications: 12.36% [41][42] GAN_GRU Factor Long Portfolio Performance - **Top 5 Industries by Excess Return (Latest Month)**: - Textiles & Apparel: 5.19% - Utilities: 3.62% - Automobiles: 3.29% - Non-Bank Financials: 2.56% - Pharmaceuticals: 1.47% [2][43] - **Top 5 Industries by Average Monthly Excess Return (Last Year)**: - Home Appliances: 5.44% - Building Materials: 4.70% - Textiles & Apparel: 4.19% - Agriculture: 4.09% - Utilities: 3.92% [2][43]
全新岚图知音标配800V+5C启动第二工厂保产能;小米汽车公布通勤提醒专利丨汽车交通日报
创业邦· 2025-07-29 10:14
Group 1 - The core viewpoint of the article highlights significant developments in the automotive industry, including local production initiatives, new technology patents, and capacity expansion efforts by various companies [1][2][3] Group 2 - Haval M6 has officially launched full localization production in Russia, with a rental agreement signed for the PSMA Rus factory in Kaluga, aiming for full production by the end of 2024. The assembly process began in February, with modern upgrades to welding and painting facilities [1] - Lantu Motors announced that the new Lantu Zhiyin will feature 5C ultra-fast charging and an 800V platform, with over 20,000 pre-orders for the FREE+ model. To enhance production capacity and ensure timely delivery, Lantu will initiate a second factory with greater capacity than the first [1] - Changan Automobile has disclosed a patent for a driving behavior prediction method, utilizing advanced machine learning to accurately forecast driving behaviors, which will support advanced driver-assistance systems (ADAS) and intelligent driving technologies [1] - Xiaomi Auto has published a patent for a commuting reminder system that provides traffic status updates based on preset conditions, enhancing user experience by allowing for better planning of departure times [1]
金工周报-20250729
China Post Securities· 2025-07-29 07:29
- NVIDIA launched the OpenReasoning-Nemotron reasoning model series in July 2025, based on the Qwen2.5 architecture, distilled from the 671 billion-parameter DeepSeek R1 0528 model, and available in four parameter scales: 1.5B, 7B, 14B, and 32B. The model aims to support structured tasks such as mathematics, science, and code generation efficiently [12] - The core innovation of OpenReasoning-Nemotron lies in its data distillation strategy, leveraging the NeMo Skills framework to generate 5 million high-quality data trajectories covering mathematical proofs, scientific derivations, and programming solutions. The training process uses supervised fine-tuning (SFT) instead of reinforcement learning, ensuring logical consistency and precision in symbolic reasoning [12] - The model employs the GenSelect algorithm to implement a "heavy reasoning mode," which involves parallel generation of candidate solutions by multiple agents and selecting the optimal answer. For example, the GenSelect@64 on the 32B model improved HMMT math competition scores from 73.8 to 96.7 and enhanced LiveCodeBench scores from 70.2 to 75.3 in code generation tasks [13] - The OpenReasoning-Nemotron series achieved record-breaking results in benchmarks such as GPQA, MMLU-PRO, and AIME24. The 32B model scored 89.2 on AIME24, surpassing OpenAI's o3-high model, while the 7B model scored 78.2, representing a nearly 20% improvement over its predecessor. However, the 1.5B model showed performance degradation to 45.6 due to inconsistencies in handling 32K tokens [15] - The Qwen3-Coder model, developed by Alibaba Cloud's Tongyi Qianwen team, was officially open-sourced in July 2025. It features a 480 billion parameter scale with a native 256K context window and employs a sparse MoE design, activating only 35 billion parameters per inference. The model was trained on a 7.5 trillion token corpus, with 70% of the data being code, covering over 80 programming languages and 20 markup languages [19][20] - Qwen3-Coder achieved a HumanEval pass@1 accuracy of 93.7%, surpassing Claude 3.5's 92.4%. On the SWE-Bench Verified benchmark, it achieved a 31.4% task success rate, exceeding GPT-4's 30.9%. Key innovations include extending the native 256K context to 1M tokens using YaRN technology and integrating execution feedback mechanisms to validate and reward generated code [20] - The GitLab Duo platform, launched in public beta in July 2025, virtualizes traditional software development team roles into specialized AI agent clusters. These agents handle tasks such as requirement planning, code writing, security analysis, testing, and operations management, forming a dynamic collaboration network. The platform automates workflows through the "Flows" feature, enabling developers to input functional descriptions and have agents complete tasks like requirement decomposition, code generation, and testing [33][36] - GitLab Duo integrates with mainstream development environments like VS Code and JetBrains IDEs and plans to introduce a "knowledge graph" feature to enhance agents' understanding of code context. The platform also emphasizes security, employing end-to-end encryption and sandbox environments for code validation [36][37]
西南交通大学最新论文登上Cell头条
生物世界· 2025-07-29 00:00
Core Viewpoint - A new wearable all-in-one obstructive sleep apnea management system has been developed, integrating flexible piezoelectric monitoring and soft magnetoelastic stimulation, addressing the limitations of traditional polysomnography (PSG) in cardiovascular parameter monitoring [4][11]. Group 1: Research Development - The research was a collaboration between Southwest Jiaotong University, City University of Hong Kong, and West China Hospital of Sichuan University [2]. - The developed system features a customized piezoelectric composite sensor for continuous physiological signal monitoring and a soft magnetoelastic actuator for non-invasive mechanical stimulation [7][11]. - The system utilizes a machine learning algorithm to achieve a 92.7% accuracy rate in real-time detection of sleep apnea events [8]. Group 2: Clinical Validation - Rigorous laboratory and clinical studies demonstrated that the developed apnea management system (AMS) is comparable to the clinical gold standard, PSG, in identifying apnea events [9]. - Parallel comparison signals from AMS and PSG confirmed the effectiveness of feedback stimulation [11]. Group 3: System Features and Benefits - The AMS integrates continuous physiological monitoring and non-invasive mechanical stimulation, providing a closed-loop system for sleep apnea management [11]. - This system not only addresses the limitations of traditional PSG in tracking cardiovascular responses but also offers a scalable and user-friendly platform for personalized sleep health care at home [11][12].
2024年美国投资公司白皮书(重点摘要)
Sou Hu Cai Jing· 2025-07-27 09:22
Industry Overview - The asset management industry is crucial in the global financial system, managing funds for individual and institutional investors through diverse investment strategies aimed at maximizing returns while controlling risks [1] - The industry serves a wide range of clients, including individuals, corporations, pension funds, insurance companies, and governments, offering various investment products such as stocks, bonds, mutual funds, ETFs, hedge funds, private equity, and real estate [1] - There is a distinction between active management, which relies on continuous market analysis, and passive management, which primarily tracks market indices [1] - Asset management companies typically charge management fees based on a percentage of assets under management (AUM) or performance-based fees, and the industry is subject to strict regulation by bodies like the SEC and FCA to protect investor rights and ensure market fairness [1] Key Players - The asset management sector comprises numerous companies of varying sizes, from large multinational firms serving global clients to small boutique firms focusing on local markets [2] - Large banks and financial institutions dominate the market with their extensive client bases and strong capital, while independent asset management firms attract investors through unique investment strategies and high-quality services [2] - Emerging fintech companies are introducing advanced technologies into the asset management space, enhancing competition and bringing new vitality to the industry [2] Investment Strategies and Products - Investment strategies and products are diverse, including traditional investments like stocks for capital appreciation, bonds for fixed income and principal safety, and mutual funds for risk diversification [2] - Alternative investment products such as private equity, real estate, and hedge funds employ various complex strategies to seek profit opportunities in different market environments [2] - Different investment strategies include value investing, which focuses on undervalued assets, growth investing, which emphasizes future growth potential, and diversification to reduce risk through asset allocation [2] Market Dynamics and Challenges - The asset management industry is expanding due to global economic growth and increasing household wealth, with technological innovations like AI and big data enhancing efficiency in investment decisions and risk management [3] - However, the industry faces challenges such as increasing fee pressure due to heightened competition and investor cost sensitivity, competition from low-cost passive investment products, evolving regulatory policies, and changing investor preferences towards sustainable and socially responsible investments [3] - Future trends indicate that technology will continue to drive industry transformation, with intelligent investment decisions and personalized services becoming mainstream, and a growing focus on sustainable investments [3] Future Outlook - The asset management industry is expected to continue innovating and adjusting to meet the diverse needs of investors in a complex and changing environment [3] - There is a trend of consolidation within the industry, with large asset management firms pursuing mergers and acquisitions to enhance scale and competitiveness, while smaller boutique firms leverage specialized services to establish a foothold in niche markets [3]
餐饮配送→智能家居→工业制造,突破!国产机器人加速“出海”收获众多海外“粉丝”
Yang Shi Wang· 2025-07-26 05:55
Core Insights - In 2024, China's industrial robot export market share is expected to rise to the second largest globally, with a 61.5% growth in exports in the first half of 2025 [1] - The export destinations are expanding from Southeast Asia and Africa to high-end markets in Europe and North America, indicating a comprehensive enhancement in the competitiveness of Chinese robotic products [1] - The application scenarios for Chinese robots are diversifying, with significant growth in sectors such as food delivery, smart homes, industrial manufacturing, and medical assistance since 2025 [9] Group 1: Export Growth and Market Expansion - In the first five months, China's industrial robot export market share in Europe reached nearly 30%, showing significant growth [1] - The export value of China's smart lawn mowers in the first quarter of 2025 reached $1.01 billion, with a nearly 60% year-on-year increase, particularly strong in Germany and the UK [13] - In the automotive manufacturing sector, 34% of China's industrial robot exports in the first half of 2025 were attributed to this industry, with the electronics sector accounting for 28% [15] Group 2: Technological Advancements and Innovation - China has become the largest robot market and producer globally, holding over 190,000 effective patents, which account for about two-thirds of the global total [20] - Continuous technological breakthroughs and the expansion of application scenarios position China to potentially transform from the largest robot market to the most innovative hub for robot technology development [22] - The industrial robot industry is projected to have a compound annual growth rate of no less than 10% from 2025 to 2030, focusing on areas such as artificial intelligence, collaborative robots, and digital twins [26] Group 3: International Competitiveness - Over 60 Chinese companies showcased advanced robotic equipment and technology at the World Automation Exhibition in Munich, competing with top global automation firms [15] - Chinese companies are making significant inroads into the automotive supply chain, providing parts and welding solutions for European companies like BMW [17]
BCEIA 2025即将盛大启幕,四十年辉煌历史,业界共襄全球分析科学与仪器盛会
仪器信息网· 2025-07-25 03:02
Core Viewpoint - The 21st Beijing Conference on Analytical Testing (BCEIA 2025) will be held from September 10 to 12, 2025, at the China International Exhibition Center in Beijing, focusing on the theme "Forty Glorious Years, A New Chapter" to commemorate the 40th anniversary of BCEIA [2][15]. Group 1: Event Overview - BCEIA 2025 will feature nearly a thousand academic reports and over 4,000 domestic and international experts and scholars in attendance [2][5]. - The event aims to showcase the latest achievements in analytical science and instrumentation, focusing on industry hot topics and technological trends [2][5]. Group 2: Academic and Technical Highlights - The conference will include a keynote speech by Academician Jiang Guibin, reviewing the 40-year development of BCEIA and exploring future directions in analytical testing science [5]. - Topics of interest will include genomics, single-cell sequencing, and artificial intelligence, with 18 specialized academic sub-sessions covering key areas such as mass spectrometry, chromatography, and environmental analysis [5][6]. Group 3: Exhibition Details - The exhibition will cover an area of 53,400 square meters, showcasing nearly 10,000 products and attracting over 30,000 professional visitors [10]. - More than 700 renowned companies from home and abroad are expected to participate, displaying cutting-edge analytical instruments, new materials, and innovative applications across various sectors including healthcare, environmental protection, and advanced manufacturing [12][14]. Group 4: Networking and Collaboration Opportunities - The event will host a series of networking activities, including a forum for young scientists and meetings with editors of prestigious journals, aimed at fostering cross-disciplinary innovation and international collaboration [6][12]. - BCEIA 2025 serves as a platform for the integration of analysis testing with key fields such as intelligent manufacturing and biomedicine, emphasizing the importance of machine learning and artificial intelligence in the industry [15][16].
德勤阿里云:2025年金融行业数字化转型白皮书(英文版)
Sou Hu Cai Jing· 2025-07-24 08:45
Industry Trends and Demand Analysis - Digital transformation is a core strategy for financial institutions to enhance competitiveness, with a focus on regional growth disparities and technological advancements [1][2] - Emerging markets in Asia are projected to grow at 3.7%, while mature economies are expected to grow at only 1.4%, influencing the digital transformation paths of financial institutions [17][20] - Asian banks are leading in mobile payment solutions and embedded finance, while Western banks focus on wealth management automation [20][21] - The Fintech market in the Asia-Pacific region is expected to exceed $325.95 billion by 2032, driven by innovations in digital payment systems [1][2] Financial Technology Development Trajectories - The Fintech industry is entering a maturity phase, with APAC markets driving innovations through AI and machine learning [38] - AI technologies are transforming wealth advisory services and improving credit assessment times by 60% [38][39] - Financial institutions are increasingly integrating advanced technologies to meet changing customer expectations and enhance operational efficiency [27][39] Risk Management and Compliance - AI and cloud computing are enhancing risk management practices, with real-time monitoring systems achieving 89% accuracy in predicting supply chain risks [2][30] - Regulatory frameworks are evolving to address AI transparency and data privacy, with initiatives like the EU's DORA and Hong Kong's "Fintech 2025" strategy [2][30] - The integration of RegTech solutions is transforming compliance from a cost center to a strategic advantage, enabling faster product launches and improved accuracy [32][34] Digital Transformation Solutions - Alibaba Cloud offers financial digital-native solutions that enhance service experience and security through AI-driven technologies [2][8] - Financial institutions are encouraged to adopt a strategic approach to digital transformation, balancing innovation with compliance through pilot programs and rapid execution [2][8] - Successful digital transformation requires a holistic redesign of business architecture, leading to improved customer engagement and operational efficiency [50][51]
2025-2031年实验室自动化设备行业全景深度分析及投资战略可行性评估预测报告-中金企信发布
Sou Hu Cai Jing· 2025-07-24 03:42
Core Viewpoint - The laboratory automation equipment industry is experiencing rapid growth driven by advancements in life sciences and testing sectors, with a focus on automation, standardization, and integration of technologies such as machine learning and digital twins [7][11]. Industry Overview - Laboratory automation refers to the use of technology to automate laboratory processes, enhancing efficiency and accuracy across various applications [2]. - The industry can be categorized into four stages of automation: single device automation, workstation automation, assembly line automation, and intelligent automation [2]. Development Trends - High-throughput, automated, and information-driven laboratory workflows are becoming the future standard [7]. - The integration of laboratory automation with technologies like machine learning and computer vision is expected to lead to smarter decision-making and adaptive processes [7]. - The domestic market is benefiting from supportive policies and an increased focus on public health, leading to rapid development and improvement in the integration and intelligence of domestic laboratory automation equipment [7][8]. Technical Barriers - Significant technical barriers exist in the industry, including: - **Equipment and Instrumentation**: High technical requirements for system integration and manufacturing of sequencing instruments, involving multiple disciplines [9]. - **Reagents and Consumables**: High-quality reagents are essential for accurate sequencing, with stringent production processes [10]. - **Data Analysis and Software Development**: The need for advanced bioinformatics to process large volumes of sequencing data presents a major challenge [10] [11]. Economic Indicators - The report outlines the economic indicators of the laboratory automation equipment industry in China from 2019 to 2024, including profitability, operational capacity, and debt repayment ability [11][12]. - The industry is characterized by a growing number of enterprises and increasing market scale, with a focus on enhancing production and sales efficiency [11][12]. Market Environment - The industry is influenced by various factors, including policy support, macroeconomic conditions, and social demand trends [11][12]. - The competitive landscape features both domestic and international players, with established companies in overseas markets leading in technology and market channels [7][11]. Future Outlook - The laboratory automation equipment market is projected to continue its growth trajectory, with forecasts indicating significant increases in market size and demand from 2025 to 2031 [11][12].