Core Points - The article discusses the new submission guidelines for ICML 2026, which will take place from July 7 to July 12, 2026, in Seoul, South Korea [4] - The conference will implement a double-blind review process, and accepted papers will be presented at the conference [4][5] - Authors can choose whether to attend the conference in person or only have their papers included in the proceedings [7][8] Submission Requirements - Papers must be submitted as a single file, with a maximum of 8 pages for the main text [5] - Accepted papers can add an additional page in the final version [6] - The original submission version of accepted papers will be made public, and authors of rejected papers can also choose to make their original submissions public [10] Important Dates - The submission website will open on January 8, 2026, with the abstract submission deadline on January 23, 2026, and the full paper submission deadline on January 28, 2026 [15][16][17] Review Process - Each paper must meet mutual review requirements, and failure to comply may result in rejection [19] - The double-blind review policy prohibits simultaneous submissions to multiple conferences or journals [20] Ethical Guidelines - Authors must adhere to research and review ethics, including providing a potential societal impact statement with their papers [25] - A plain language summary must be submitted to communicate the research significance to the public [26] Additional Notes - Authors are allowed to use generative AI tools for writing or research but must take full responsibility for the content [23] - Violations of the submission guidelines may lead to sanctions or rejection of the paper [24]

Overview - The article discusses the transformative impact of artificial intelligence (AI) and machine learning (ML) on marketing and supply chain management, emphasizing the need for businesses to adapt to these technologies for improved decision-making and operational efficiency [1][6]. AI Terminology Overview - AI encompasses a broad field focused on creating machines capable of tasks requiring human-like intelligence, while ML is a subset of AI that enables computers to learn from data without explicit programming [2][4]. Importance of AI - AI is being rapidly adopted across industries as it directly correlates with business efficiency, profitability, and competitiveness, moving beyond experimental phases to practical applications in daily operations [6][9]. Applications of AI in Marketing - AI is utilized in marketing through personalized recommendations, customer service chatbots, and predictive analytics, enhancing customer engagement and operational effectiveness [10][12]. Marketing's Impact on Supply Chain - Marketing activities can trigger demand shocks, necessitating a responsive supply chain to avoid stockouts and missed revenue opportunities, highlighting the interconnectedness of marketing and supply chain functions [13][15]. Challenges in Modern Supply Chains - Modern supply chains face challenges such as complexity, uncertainty, speed expectations, and sustainability, driving the need for AI to enhance demand forecasting and proactive measures [19][20]. AI in Demand Forecasting and Planning - AI enhances demand forecasting and planning by integrating time series analysis with machine learning, allowing for more accurate predictions and operational actions [20][22]. AI in Inventory Optimization - AI aids in inventory management by determining optimal stock levels based on real-time data and demand forecasts, balancing availability and cost [24][26]. AI in Logistics and Transportation - AI transforms logistics by optimizing delivery routes, predicting arrival times, and enabling predictive maintenance, thus improving efficiency and reliability [27][29]. AI in Supplier and Risk Management - AI strengthens supplier and risk management through continuous performance analysis and real-time monitoring of external events, allowing for proactive risk mitigation [33][34]. AI in Warehousing and Automation - AI automates and optimizes warehousing processes, improving accuracy and efficiency in inventory handling and order fulfillment [37][38]. AI in Sustainability and ESG - AI supports sustainability efforts by optimizing processes to reduce waste and emissions, facilitating the transition to circular supply chains [38][40]. Unified Perspective on Marketing and Supply Chain - Understanding AI's value requires viewing marketing and supply chain as interconnected systems, where AI synchronizes demand creation and fulfillment [61][63]. Emerging Trends in AI-Driven Supply Chains - New trends in AI include digital twins for simulation, proactive AI agents for planning, and visual models for real-time monitoring, indicating a shift towards more autonomous and intelligent supply chain operations [66][67].

Quantitative Models and Construction 国证 2000 Index Enhancement Strategy - **Model Name**: 国证 2000 Index Enhancement Strategy - **Model Construction Idea**: Focused on the small-cap stock rotation phenomenon in A-shares, aiming to select stocks effectively within 国证 2000 index components to enhance returns [11] - **Model Construction Process**: - Selected factors such as technical, reversal, and idiosyncratic volatility, which showed strong performance on 国证 2000 index components [12] - Addressed high correlation among factors by regressing volatility factors on technical and reversal factors to obtain residual volatility factors [12] - Combined all major factors equally and performed industry and market capitalization neutralization to construct the 国证 2000 enhancement factor [12] - Formula: Residual volatility factor = Volatility factor - Regression(Technical factor, Reversal factor) [12] - **Model Evaluation**: Demonstrated strong predictive performance with an IC mean of 12.63% and T-statistic of 12.70 [12] - **Strategy Construction**: - Monthly rebalancing at the end of each month, buying the top 10% ranked stocks based on factor values, constructing an equal-weighted long portfolio [15] - Backtesting period: April 2014 to present, benchmarked against 国证 2000 index, with a transaction fee rate of 0.2% per side [15] Machine Learning Index Enhancement Strategy - **Model Name**: TSGRU+LGBM Machine Learning Index Enhancement Strategy - **Model Construction Idea**: Improved machine learning stock selection model by integrating TimeMixer framework with GRU and LightGBM, leveraging multi-scale mixing and seasonal/trend decomposition mechanisms [21] - **Model Construction Process**: - Original strategy used GBDT and NN models trained on different feature datasets and prediction labels, but showed signs of failure due to market style adjustments [21] - Enhanced model incorporated TimeMixer framework into GRU, combined LightGBM with TSGRU latent vectors and traditional quantitative factors [21] - Optimized portfolio construction by controlling tracking error and individual stock weight deviation to maximize factor exposure [25] - **Model Evaluation**: Improved ability to capture recent market information, showing strong performance [21] Dividend Style Timing + Dividend Stock Selection Strategy - **Model Name**: Dividend Style Timing + Dividend Stock Selection Strategy - **Model Construction Idea**: Leveraged the long-term stability and high dividend characteristics of dividend stocks to reduce risk during weak market conditions [36] - **Model Construction Process**: - Used 10 indicators related to economic growth and monetary liquidity to construct a dynamic event factor system for dividend index timing [36] - Applied AI models to test stock selection within 中证红利 index components, achieving stable excess returns [36] - **Model Evaluation**: Demonstrated significant stability improvement compared to 中证红利 index total return [36] --- Model Backtesting Results 国证 2000 Index Enhancement Strategy - **IC Mean**: 12.63% [12] - **Latest Month IC**: 25.34% [12] - **Annualized Excess Return**: 13.30% [16] - **Information Ratio (IR)**: 1.73 [16] - **Tracking Error**: 7.68% [19] - **October Excess Return**: 2.92% [16] TSGRU+LGBM Machine Learning Index Enhancement Strategy - **沪深 300 Index**: - **Annualized Excess Return**: 6.96% [26] - **Information Ratio (IR)**: 1.40 [26] - **Tracking Error**: 4.97% [26] - **October Excess Return**: 2.25% [26] - **中证 500 Index**: - **Annualized Excess Return**: 10.11% [30] - **Information Ratio (IR)**: 1.96 [30] - **Tracking Error**: 5.16% [30] - **October Excess Return**: -0.59% [30] - **中证 1000 Index**: - **Annualized Excess Return**: 13.52% [35] - **Information Ratio (IR)**: 2.37 [35] - **Tracking Error**: 5.70% [35] - **October Excess Return**: 2.63% [35] Dividend Style Timing + Dividend Stock Selection Strategy - **Stock Selection Strategy**: - **Annualized Return**: 18.98% [38] - **Sharpe Ratio**: 0.90 [38] - **October Return**: 2.52% [38] - **Timing Strategy**: - **Annualized Return**: 13.83% [38] - **Sharpe Ratio**: 0.90 [38] - **October Return**: 3.28% [38] - **固收+ Strategy**: - **Annualized Return**: 7.39% [38] - **Sharpe Ratio**: 2.19 [38] - **October Return**: 0.92% [38]

Core Points - The ICML 2026 conference will take place from July 7 to July 12, 2026, in Seoul, South Korea, with a double-blind review process for all submitted papers [4] - Authors of accepted papers can choose whether to attend the conference in person or only have their papers included in the proceedings [7] - The original submission versions of accepted papers will be made publicly available, and authors of rejected papers can also choose to make their original submissions public [10] Submission Requirements - Papers must be submitted as a single file, with a maximum of 8 pages for the main text, while references, impact statements, and appendices have no page limit [5] - There will be no separate submission deadline for supplementary materials, and authors can add one extra page to the final version of accepted papers [6] - Papers that do not comply with the submission requirements will be rejected without review [11] Important Dates - The submission website will open on January 8, 2026, with the abstract submission deadline on January 23, 2026, and the full paper submission deadline on January 28, 2026 [14][15] Review Process - Authors are required to participate in the review process, with specific mutual review requirements for both papers and authors [17] - The double-blind review policy prohibits simultaneous submissions to multiple conferences or journals [18] - All submissions must be anonymized and should not contain any information that could reveal the authors' identities [21] Ethical Guidelines - Each paper must include a potential societal impact statement, which should be placed at the end of the paper and will not count towards the page limit [23] - Authors must submit a plain language summary to communicate the significance of their research to the public [24] - Violations of the review process or ethical guidelines may result in sanctions or rejection of the submission [22][23]

LendingClub (NYSE: LC) 2025 Investor Day Summary Company Overview - **Company**: LendingClub - **Event**: 2025 Investor Day - **Date**: November 5, 2025 - **Significance**: First Investor Day since becoming a bank Key Points and Arguments Strategic Transformation - LendingClub has transformed significantly since acquiring a bank charter, unlocking strategic potential and targeting a valuable audience for lifetime lending and banking [6][12] - The company emphasizes a clear strategy: acquire customers through lending, engage them, and provide additional products and services [7][10] Competitive Advantages - **Underwriting**: LendingClub utilizes advanced machine learning and AI for underwriting, with a data advantage from over $1.6 trillion in validated loan demand and 150 billion data cells [8][36] - **Performance Metrics**: - 40% lower delinquency rates compared to competitors [37] - One of the lowest fraud loss rates in the industry [49] - 25% better recovery rates through smart collections [37] Target Customer Base - Focus on the "motivated middle" (income between $50,000 and $200,000), representing 32% of the U.S. population but nearly half of the non-mortgage credit wallet [16][18] - This demographic is digitally savvy, value-conscious, and has a higher-than-average income, leading to better access to credit [17] Market Dynamics - Shift from branch-based banking to mobile banking, with LendingClub positioned to capitalize on this trend [21][22] - The average credit card interest rate is 23%, while LendingClub offers unsecured credit at 30% lower rates [22][23] - Savings accounts at large banks yield minimal interest, while LendingClub offers 420 basis points with no minimum balance [24] Customer Engagement and Retention - 83% of customers express a desire to engage more with LendingClub, indicating strong customer loyalty [27] - Repeat borrowers account for 50% of annual issuance, with many returning multiple times [68][69] Growth Projections - Anticipated growth in personal loans from $10 billion to $18-22 billion over the medium term, driven by funnel efficiency, marketing channel expansion, and product innovation [79][95] - Major purchase financing and home improvement financing are identified as significant growth opportunities, with a combined addressable market of $250 billion [89][95] Product Innovations - Introduction of a top-up loan product allowing customers to consolidate existing loans and add new expenses, achieving a 93% customer satisfaction rate [77][78] - Expansion into home improvement financing through strategic partnerships and technology acquisitions [91][92] Customer Experience Enhancements - Development of a mobile app that has increased digital engagement from 25% to 42% of borrowers, leading to a significant rise in loan issuance through the app [105][108] - Introduction of DebtIQ, providing free debt and credit insights to encourage ongoing engagement [110] Additional Important Insights - The company has a strong focus on data-driven decision-making and continuous improvement through high-frequency testing and real-time adjustments to credit strategies [45][46] - LendingClub's approach to collections is proactive, utilizing AI to identify potential delinquencies early and engage customers effectively [51][54] This summary encapsulates the core themes and insights from LendingClub's 2025 Investor Day, highlighting the company's strategic direction, competitive advantages, and growth potential in the evolving financial landscape.

Core Insights - Nvidia has launched its H100 GPU into space, marking a significant milestone in space-based AI infrastructure, while Google has announced its own initiative, Project Suncatcher, to utilize solar energy for AI processing in space [1][3] - Project Suncatcher aims to create a scalable AI infrastructure using a constellation of satellites equipped with TPUs and free-space optical communication links, leveraging the vast energy from the sun [6][8] Project Overview - Project Suncatcher is designed to explore the potential of solar-powered satellite constellations to enhance machine learning capabilities in space, with the sun's energy being 1 trillion times greater than human electricity production [6][8] - Google plans to launch two prototype satellites in early 2027 in collaboration with Planet, addressing engineering challenges such as thermal management and system reliability in orbit [3][18] Technical Challenges - The system will require high-bandwidth, low-latency inter-satellite links to distribute machine learning workloads effectively, aiming for performance comparable to ground data centers [8][10] - Google has developed models to analyze the orbital dynamics of tightly clustered satellites, ensuring they can maintain stable orbits with minimal propulsion [10][12] TPU Radiation Resistance - Google's Trillium TPU has undergone radiation testing, demonstrating resilience to total ionizing dose and single-event effects, making it suitable for space applications [14][13] Economic Viability - Historical data suggests that satellite launch costs could drop below $200 per kilogram by the mid-2030s, making space-based data centers economically feasible [15][18] - The analysis indicates that the operational costs of space-based data centers could become comparable to terrestrial counterparts in terms of energy costs [15] Future Directions - The next milestone for Google involves executing a "learning mission" to test TPU hardware and models in space, paving the way for potential gigawatt-scale satellite constellations [18][19]

Core Insights - The commercial big data service industry in China is experiencing significant growth, with a projected market size of 60.5 billion yuan in 2024, representing a year-on-year increase of 20.76% [1][6] - The industry is divided into general commercial big data services, accounting for approximately 29%, and specialized commercial big data services, which make up about 71% of the market [1][6] Overview - Commercial big data services involve the use of vast structured and unstructured data to provide insights, decision support, business optimization, and risk management for enterprises [2] - The core value lies in transforming data into actionable business strategies, enhancing operational efficiency, identifying market opportunities, and strengthening core competitiveness across various business processes [2] Industry Entry Barriers - The commercial big data service industry has high technical requirements, including data analysis, processing, resource integration, and algorithm development [4] - Companies must invest significant time, effort, and capital to develop core technical capabilities and keep pace with rapid technological advancements to avoid market obsolescence [4] Market Policies - The industry is recognized as a strategic emerging industry in China, supported by various government policies aimed at fostering its development [5] - Key policies include the "Digital China Construction Overall Layout Plan" and the "Three-Year Action Plan for Data Elements (2024-2026)" among others, creating a favorable policy environment for the industry [5] Industry Chain - The upstream of the commercial big data service industry includes data sources, technology sources, and servers, while the midstream consists of service providers, and the downstream encompasses application markets across various sectors [5] Competitive Landscape - The industry is segmented into general and specialized commercial big data services, with companies like Anshuo Information and Yuxin Technology focusing on customized analysis for specific industries [8] - General commercial big data service providers, such as Qichacha and Tianyancha, offer standardized products and services that cater to a wide range of client needs [9] Company Analysis - Anshuo Information specializes in credit risk management and has generated 380 million yuan in revenue in the first half of 2025, with 71.09% from credit management systems [10] - Hehe Information focuses on AI and big data technology, achieving 843 million yuan in revenue in the first half of 2025, with a significant portion from B-end services [11] Future Trends - The integration of AI and machine learning with big data services is expected to enhance data processing efficiency and decision-making capabilities [11] - Data security and privacy protection will become critical as the importance of data increases, leading companies to adopt advanced technologies for secure data collaboration [11]

Core Viewpoint - The China Actuarial Society has released the "Experience Life Table of China's Life Insurance Industry (2025)", which reflects the latest mortality trends and provides a scientific basis for life insurance product pricing and risk management [1][2]. Group 1: Background of Life Table Compilation - The previous life table, published in December 2016, was outdated due to changes in mortality rates and life expectancy in China, necessitating a new table to enhance risk management and service levels in the life insurance industry [2]. Group 2: Main Achievements of the Life Table Compilation - A new experience life table has been created, reflecting the latest mortality rates and providing a scientific reference for life insurance product pricing [3]. - The first single life table has been compiled, allowing for cross-company and cross-insurance type mortality research, enhancing comparability with population mortality rates [3]. - A comprehensive report and educational materials on population aging will be produced to present the findings to various audiences [3]. Group 3: Highlights of the Life Table Compilation - The data collection for this life table achieved full industry coverage, incorporating all life insurance policies with death or survival benefits [4]. - Data processing efficiency improved, with a 40% reduction in processing time compared to the previous table, and the use of AI and machine learning minimized manual data entry errors [4]. - Innovative methods were employed to address missing death status data, ensuring a more accurate mortality rate without discarding valuable data [4]. - Trend factors were established based on historical insurance industry data, providing significant insights for understanding mortality trends [5]. - A two-step method for high-age extrapolation was introduced, ensuring that mortality rates reflect natural life patterns while maintaining risk characteristics [5]. - A multi-dimensional analysis of mortality rates was conducted, examining various factors such as age, gender, and region, and comparing the new table with previous versions and external data [5]. Group 4: Future Work - The Actuarial Society plans to conduct promotional and training activities to disseminate the findings of the new life table, along with completing the related reports [6][7].

Core Insights - Google has launched Project Suncatcher, a space-based scalable AI infrastructure system designed to utilize solar energy for AI applications, with the potential to harness energy that exceeds human electricity production by 100 trillion times [8][11][29] - The project aims to deploy a constellation of satellites equipped with Tensor Processing Units (TPUs) and free-space optical communication links to enhance machine learning capabilities in space [7][9][10] Project Overview - Project Suncatcher is a significant exploration initiative that envisions a satellite constellation powered by solar energy, aimed at expanding the computational scale of machine learning in space [7][8] - The first satellite launch is scheduled for early 2027, in collaboration with Planet, to test the feasibility of the proposed system [3][29] Technical Challenges - The project faces several engineering challenges, including thermal management, high-bandwidth inter-satellite communication, and system reliability in orbit [28][29] - Achieving data center-scale inter-satellite links is crucial, requiring connections that support tens of terabits per second [13][14] - The satellites will operate in a dawn-dusk sun-synchronous low Earth orbit to maximize solar energy collection [13][21] TPU Radiation Tolerance - Google's Trillium TPU has undergone radiation testing, demonstrating resilience to total ionizing dose (TID) and single-event effects (SEEs), making it suitable for space applications [21][22] Economic Viability - Historical data suggests that launch costs for satellite systems may decrease to below $200 per kilogram by the mid-2030s, making space-based data centers economically feasible [23][24] - The operational costs of space-based data centers could become comparable to terrestrial counterparts in terms of energy costs [24] Future Directions - The initial analysis indicates that the core concept of space-based machine learning computing is not hindered by fundamental physics or insurmountable economic barriers [28] - The next milestone involves launching two prototype satellites to validate Google's models and TPU hardware in space [29][30]

Core Viewpoint - Lepu Medical's newly approved rechargeable implantable deep brain stimulation (DBS) device marks a significant breakthrough in China's neuroregulation field, traditionally dominated by international giants, providing new treatment options for Parkinson's disease patients [2][3][8] Product Approval - Lepu Medical announced that its subsidiary has received NMPA registration approval for its rechargeable implantable DBS system, which includes the stimulator, electrode components, and extension lead kit, aimed at assisting late-stage primary Parkinson's disease patients whose symptoms are not effectively controlled by medication [3] - The DBS device is expected to contribute to revenue growth in the coming year, with plans for additional products like the implantable cardiac contractility modulator (CCM) to be submitted for approval in early 2024 [3] Treatment Mechanism - Deep Brain Stimulation (DBS) involves implanting electrodes in specific brain areas to deliver electrical pulses, helping to alleviate symptoms of Parkinson's disease, which affects over 5 million patients in China as of 2021 [4] - The new product is positioned as a key component of Lepu Medical's neuroregulation business, expected to drive performance growth [4] Market Potential - The global deep brain stimulation system market is projected to grow from approximately $1.738 billion in 2024 to $3.919 billion by 2031, with a CAGR of 12.5% from 2025 to 2031 [6] - The rechargeable implantable DBS market is expected to reach around 690 million yuan in 2024, with a projected CAGR of 4.7% until 2031 [6] Domestic Market Landscape - The global DBS market is currently dominated by companies like Boston Scientific, Medtronic, and Abbott, while domestic competitors include Lepu Medical and Beijing Pinchi Medical [7] - The neuroprosthetics market is anticipated to grow at a CAGR of 13% from 2025 to 2031, driven by the rising prevalence of neurological diseases due to an aging population [7] Industry Trends - The increasing incidence of neurological diseases such as Parkinson's and Alzheimer's is expected to boost demand for neuroprosthetic devices [7] - Future advancements in neuroprosthetic devices are likely to incorporate AI and machine learning technologies to enhance functionality and precision [7]