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Beyond AI : ChatGPT, Web3, and the business landscape of tomorrow / / Ken Huang, Yang Wang, Feng Zhu, Xi Chen, Chunxiao Xing, editors
| Beyond AI : ChatGPT, Web3, and the business landscape of tomorrow / / Ken Huang, Yang Wang, Feng Zhu, Xi Chen, Chunxiao Xing, editors |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cham : , : Springer, , [2023] |
| Descrizione fisica | 1 online resource (xxvii, 395 pages) : illustrations |
| Disciplina | 006.3 |
| Collana | Future of Business and Finance Series |
| Soggetto topico |
Business enterprises - Technological innovations
Artificial intelligence |
| Soggetto non controllato |
BUSINESS & ECONOMICS / Information Management
BUSINESS & ECONOMICS / Business Ethics BUSINESS & ECONOMICS / Management Science COMPUTERS / Artificial Intelligence / General COMPUTERS / Security / General BUSINESS & ECONOMICS / Finance / Financial Engineering |
| ISBN | 3-031-45282-8 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgment -- Contents -- About the Editors -- PartPart10005725897 -- Chapter 1: Overview of ChatGPT, Web3, and New Business Landscape -- 1.1 Introduction to Generative AI and ChatGPT -- 1.1.1 Understanding Generative AI -- 1.1.2 The Journey of ChatGPT -- 1.1.3 The Transformative Power of ChatGPT -- 1.2 Generative AI: The New Paradigm -- 1.2.1 ChatGPT and the New Business Landscape -- 1.2.2 ChatGPT on Business Efficiency and Accuracy -- 1.3 Key Players in GenAI -- 1.3.1 Decoding the Success of Key Players in GenAI -- 1.3.2 GenAI: A Roster of the Influential Entities -- 1.4 Why Now? -- 1.4.1 The Rise of GPT and Transformer Architecture -- 1.4.2 The Rising Need for GenAI in the Age of Personalization -- 1.4.3 Data and Computational Power: Scaling GenAI -- 1.4.4 Other Reasons and Limitations -- 1.5 GenAI Horizontal Applications and Challenges -- 1.5.1 GenAI Horizontal Applications -- 1.5.2 Ongoing Challenges -- 1.6 The Future of GenAI and Integration with Web3 -- 1.6.1 The Future of GenAI -- 1.6.2 Web3 and the Need to Integrate Web3 with GenAI -- References -- Chapter 2: ChatGPT: Inside and Impact on Business Automation -- 2.1 Basics of Machine Learning and Neural Networks -- 2.1.1 What Is Machine Learning? -- 2.1.2 What Are Neural Networks? -- 2.1.3 Popular Neural Network Architectures -- 2.2 Overview of Generative AI Technology -- 2.3 Key ChatGPT Concepts -- 2.3.1 The Transformer -- 2.3.2 How Are Language Models Created? -- 2.3.3 Text to Image Generation Technology -- 2.4 Key Research Papers in GPT -- 2.5 ChatGPT and the Future of Business Automation -- 2.5.1 Business Automation Enabled by ChatGPT and its Extension -- 2.5.2 ChatGPT Plug-in Vs. Apple App Store -- 2.5.3 OpenAI ChatGPT Function Call Capability -- 2.5.4 From AIGC to AIGX -- 2.5.5 How CEOs Get Prepared for ChatGPT? -- References -- PartPart20005725898.
Chapter 3: ChatGPT and Web3 Applications -- 3.1 Introduction to Web3 Applications and ChatGPT -- 3.1.1 The Emergence of Web3 and Decentralized Networks -- 3.1.2 Overview of ChatGPT´s Role in Web3 Applications -- 3.1.3 Overview of Web3 Ecosystem´s Role in ChatGPT -- 3.2 Innovative Use Cases of ChatGPT in Web3 Applications -- 3.2.1 Enhancing Decentralized Applications (dApps) with ChatGPT -- 3.2.2 ChatGPT in Decentralized Finance (DeFi) Platforms -- 3.2.3 Leveraging ChatGPT for NFT -- 3.2.4 Leveraging ChatGPT for DAO -- 3.2.5 Leveraging ChatGPT for Metaverse -- 3.3 Impact of ChatGPT on Decentralized Networks -- 3.3.1 The Potential for Improved User Experience -- 3.3.2 Scalability Concerns and Network Effects -- 3.3.3 Addressing Trust and Governance Issues -- 3.4 Innovative Use of Web3 in ChatGPT -- 3.4.1 A New Landscape for AI Data Governance -- 3.4.2 AI Model Validation in a Decentralized Environment -- 3.4.3 Democratizing Computation Power for AI -- 3.4.4 Decentralized AI Solution Marketplace -- 3.4.5 Incentivizing User Engagement and Content Generation -- 3.4.6 New Monetization Strategies and Business Models -- 3.5 Preparing for the Future of ChatGPT and Web3 -- 3.5.1 Anticipating Technological Advancements and Trends -- 3.5.2 Nurturing Interdisciplinary Collaboration -- 3.5.3 Building a Robust Integration Strategy -- References -- Chapter 4: ChatGPT in Product Management -- 4.1 ChatGPT for Product Ideation -- 4.1.1 Identifying Market Opportunities with ChatGPT -- 4.1.2 ChatGPT in Competitive Analysis and Benchmarking -- 4.1.3 Brainstorming and Idea Generation Using ChatGPT -- 4.2 ChatGPT for Product Design -- 4.2.1 Streamlining Design and Collaboration with ChatGPT -- 4.2.2 ChatGPT-Enhanced User Experience and Interface Design -- 4.3 ChatGPT in Agile Methodologies and Project Management -- 4.3.1 ChatGPT for Target Audience Analysis and Segmentation. 4.3.2 Social Media and Content Strategy with ChatGPT -- 4.4 ChatGPT in Product Launch and Go-to-Market Strategy -- 4.4.1 ChatGPT-Driven Launch Plan Development -- 4.4.2 Sales Enablement and Training with ChatGPT -- 4.4.3 Tracking and Analyzing Performance Using ChatGPT -- 4.5 ChatGPT for Customer Support and Success -- 4.5.1 Enhancing User Onboarding with ChatGPT -- 4.5.2 ChatGPT in Customer Retention and Churn Prediction -- 4.6 ChatGPT and Product Management Frameworks -- 4.6.1 The Jobs to Be Done (JTBD) Framework -- 4.6.2 RICE Prioritization Framework -- 4.6.3 AARRR Metrics, also known as Pirate Metrics -- 4.6.4 The MoSCoW Method -- 4.7 ChatGPT Integration with Web3 for Product Management -- 4.7.1 Decentralized Knowledge Sharing and Collaboration Using ChatGPT and Web3 -- 4.7.2 Decentralized Product Feedback and Review Systems -- 4.8 Future Directions and Challenges for ChatGPT in Product Management -- 4.8.1 Addressing ChatGPT Limitations in Product Management -- 4.8.2 Ethical Considerations and Responsible AI in Product Management -- 4.8.3 Measuring ROI and Impact of ChatGPT in Product Management -- 4.8.4 Anticipating Future Developments and Trends in ChatGPT and Product Management -- References -- Chapter 5: ChatGPT and Gig Economy -- 5.1 ChatGPT for Gig Economy Platforms -- 5.1.1 Enhancing Platform User Experience with ChatGPT -- 5.1.2 ChatGPT-Driven Recruitment and Onboarding -- 5.1.3 Streamlining Project Management and Collaboration Using ChatGPT -- 5.2 ChatGPT for Freelance Professionals -- 5.2.1 ChatGPT as a Virtual Assistant for Freelancers -- 5.2.2 Enhancing Productivity and Time Management with ChatGPT -- 5.2.3 ChatGPT for Networking and Community Building -- 5.3 ChatGPT in Gig Economy Skill Development -- 5.3.1 ChatGPT for Personalized Learning and Training -- 5.3.2 ChatGPT in Mentorship and Career Guidance. 5.3.3 Skill Assessment and Gap Analysis with ChatGPT -- 5.4 ChatGPT in Gig Economy Financial Management -- 5.4.1 ChatGPT for Financial Planning and Budgeting -- 5.4.2 Invoicing and Payment Management with ChatGPT -- 5.4.3 ChatGPT in Tax Planning and Compliance -- 5.5 ChatGPT for Legal and Contract Management in gig Economy -- 5.5.1 Leveraging ChatGPT for Contract Generation and Review -- 5.5.2 ChatGPT in Dispute Resolution and Mediation -- 5.5.3 Navigating Legal and Regulatory Compliance with ChatGPT -- 5.6 Integration with Web3 -- 5.6.1 Optimizing Job Matching and Discovery with ChatGPT and Web3 -- 5.6.2 Streamlining Contract Management and Payments with ChatGPT and Web3 -- 5.6.3 Enhancing Gig Worker Support and Skill Development with ChatGPT and Web3 -- 5.6.4 Promoting Transparency, Accountability, and Fairness with ChatGPT and Web3 -- 5.7 Future Directions and Challenges for ChatGPT in Gig Economy -- 5.7.1 Addressing ChatGPT Limitations in Gig Economy Applications -- 5.7.2 Ethical Considerations and Responsible AI in the Gig Economy -- 5.7.3 Anticipating Future Developments and Trends in ChatGPT and Gig Economy -- References -- Chapter 6: ChatGPT in Nutrition Science -- 6.1 GPT Use in Generating Personalized Nutrition Recommendations -- 6.1.1 Personalized Fitness Goals and Recommendations -- 6.1.2 Meal Planning -- 6.1.3 Tracking Eating Habits with ChatGPT -- 6.1.4 ChatGPT as an Accountability Partner -- 6.1.5 ChatGPT as a Guide in the World of Supplements -- 6.1.6 Cooking Advice by GPT -- 6.2 GPT in the Development of New Foods -- 6.2.1 Revolutionizing Research and Development with ChatGPT -- 6.2.2 ChatGPT and the Innovation of Plant-Based Alternatives -- 6.2.3 ChatGPT: A Compass in the Complex Regulatory Landscape of Food Science -- 6.2.4 ChatGPT as a Facilitator for Cross-Disciplinary Collaboration. 6.3 GPT Uses in Dietary Patterns and Health Outcomes -- 6.3.1 GPT Models and Dietary Patterns Analysis -- 6.3.2 Applications of GPT Technology in Nutrition Research -- 6.4 Nutrition Professional´s Use of GPT -- 6.4.1 Effective Communication -- 6.4.2 Staying Informed about the Latest Research -- 6.4.3 Creating Educational Material -- 6.4.4 Online Presence -- 6.5 ChatGPT: Nutrition Science Privacy Concerns -- 6.5.1 ChatGPT´s Privacy and Security in Nutrition Science -- 6.5.2 ChatGPT´s Ethical Considerations in Nutrition Science -- 6.5.3 ChatGPT´s Limitations in Nutrition Science -- 6.6 Integration with Web3 -- 6.6.1 ChatGPT-Web3: Tailoring Nutritional Advice -- 6.6.2 Boosting Nutrition Research with ChatGPT and Web3 -- 6.6.3 ChatGPT-Web3: Transforming Nutrition Communication -- 6.6.4 Food Traceability and Safety with ChatGPT and Web3 -- References -- Chapter 7: ChatGPT in Finance and Banking -- 7.1 Expanding Financial Services with ChatGPT -- 7.1.1 Streamlining Operational Processes of Financial Services -- 7.1.2 Enhancing Fraud Detection and Prevention -- 7.1.3 Personalizing Financial Products and Services -- 7.2 Transforming Customer Experience in Banking -- 7.2.1 Conversational Banking and Customer Support 24/7 -- 7.2.2 Conversational Banking and Financial Advice -- 7.2.3 Multilingual Support and Accessibility -- 7.3 Risk Assessment and Investment Portfolio Optimization -- 7.3.1 Credit Scoring and Risk Profiling -- 7.3.2 Portfolio Management and Asset Allocation -- 7.3.3 Predictive Analytics for Market Insights -- 7.4 Decentralized Finance (DeFi) and ChatGPT -- 7.4.1 Smart Contracts and Automated Transactions -- 7.4.2 Enhanced Security and Transparency -- 7.4.3 Financial Inclusion and Accessibility -- 7.4.4 Addressing Security and Privacy Concerns in DeFi -- 7.5 Security and Privacy Controls of ChatGPT Use in Finance and Banking. 7.5.1 Data Protection and Privacy Compliance -- 7.5.2 Mitigating Adversarial Attacks -- 7.5.3 Continuous Monitoring and Audits -- 7.6 The Future of AI in Finance and Banking -- 7.6.1 Emerging Technologies and Innovations -- 7.6.2 Ethical Considerations and Responsible AI -- 7.6.3. -- Upskilling and Workforce Adaptation -- 7.6.4 GenAI Adoption Maturity Framework for Financial Institutions -- Chapter 8. Chat GPT in Real Estate -- 8.1 AI in Today's Real Estate -- 8.2 Applications of ChatGPT and Generative AI in Real Estate -- 8.3 Challenges of ChatGPT and Generative AI in Real Estate -- 8.4 Real Estate Reimagined: ChatGPT and Web3 Synergy -- References -- Chapter 9. ChatGPT in Gaming Industry -- 9.1 Introduction to ChatGPT in Gaming -- 9.2 Integration of ChatGPT into Games -- 9.3 Applications of ChatGPT in Gaming -- 9.4 Challenges and Limitations -- 9.5 Future Trends and Opportunities -- 9.6 ChatGPT + Web3 in Gaming Industry -- References -- Chapter 10. ChatGPT in Government -- 10.1 ChatGPT for Citizen Engagement -- 10.2 ChatGPT for Public Administration Efficiency -- 10.3 Chat GPT in Policy Development and Analysis -- 10.4 ChatGPT for Government Collaboration and Innovation -- 10.5 Ethical Considerations and Challenges of ChatGPT in Government -- 10.6 ChatGPT and Web3 for Government Services. Part III: Ethical, Legal, and Security Considerations in Chat GPT -- Chapter 11. Security and Privacy concerns in ChatGPT -- 11.1 Overview -- 11.2 Security Risks in ChatGPT -- 11.3 Privacy Concerns in ChatGPT -- 11.4 User Perspectives on Security and Privacy -- 11.5 Addressing Security and Privacy Concerns -- References. Chapter 12: Legal and Ethics Responsibility of ChatGPT -- 12.1 Introduction to Legal and Ethical Concerns in ChatGPT -- 12.2 Intellectual Property Rights and ChatGPT -- 12.3 Liability and Accountability and ChatGPT -- 12.4 Ethical Considerations in ChatGPT Deployment -- 12.5 Compliance with Privacy and Data Protection Regulations -- 12.6 AI Regulations in Some Countries -- 12.7 Recommendations and Best Practices for Legal and Ethical ChatGPT Use -- References -- Appendix A: ChatGPT FAQ -- Glossary |
| Altri titoli varianti | ChatGPT, Web3, and the business landscape of tomorrow |
| Record Nr. | UNINA-9910799238403321 |
| Cham : , : Springer, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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The elements of joint learning and optimization in operations management / / edited by Xi Chen, Stefanus Jasin, and Cong Shi
| The elements of joint learning and optimization in operations management / / edited by Xi Chen, Stefanus Jasin, and Cong Shi |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (444 pages) |
| Disciplina | 658.7 |
| Collana | Springer Series in Supply Chain Management |
| Soggetto topico |
Production management
Business logistics |
| ISBN | 3-031-01926-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Editors and Contributors -- About the Editors -- Contributors -- Part I Generic Tools -- 1 The Stochastic Multi-Armed Bandit Problem -- 1.1 Introduction -- 1.2 The N-Armed Bandit Problem -- 1.2.1 Upper Confidence Bound (UCB) Algorithm -- 1.2.2 Thompson Sampling (TS) -- 1.3 Contextual Bandits -- 1.4 Combinatorial Bandits -- References -- 2 Reinforcement Learning -- 2.1 Introduction -- 2.2 Markov Decision Process and Dynamic Programming -- 2.2.1 Finite-Horizon Markov Decision Process -- 2.2.1.1 Dynamic Programming Solution -- 2.2.2 Discounted Markov Decision Process -- 2.2.2.1 Value Iteration -- 2.2.2.2 Policy Iteration -- 2.3 Reinforcement Learning Algorithm Design -- 2.3.1 Reinforcement Learning Problem Formulation -- 2.3.1.1 Episodic Reinforcement Learning in Finite-Horizon MDP -- 2.3.1.2 Reinforcement Learning in Discounted MDP -- 2.3.2 Model-Based vs. Model-Free Reinforcement Learning -- 2.3.2.1 Model-Based Reinforcement Learning -- 2.3.2.2 Q-Learning and SARSA -- 2.3.2.3 Policy Gradient -- 2.3.3 Exploration in Reinforcement Learning -- 2.3.3.1 Exploration Schemes -- 2.3.3.2 Deep Exploration -- 2.3.4 Approximate Solution Methods and Deep Reinforcement Learning -- 2.4 Conclusion and Further Reading -- References -- 3 Optimal Learning and Optimal Design -- 3.1 Introduction -- 3.2 Statistical Design of Experiments -- 3.3 The Ranking and Selection Problem -- 3.3.1 Model -- 3.3.2 Large Deviations Analysis -- 3.3.3 Example: Normal Sampling Distributions -- 3.3.4 Optimal Allocations -- 3.4 Sequential Algorithms -- 3.4.1 Value of Information Methods -- 3.4.2 Thompson Sampling -- 3.4.3 Rate-Balancing Methods -- 3.4.4 Discussion -- 3.5 Recent Advances -- 3.5.1 A New Optimal Design for Linear Regression -- 3.5.2 Optimal Budget Allocation in Approximate Dynamic Programming -- 3.6 Conclusion -- References.
Part II Price Optimization -- 4 Dynamic Pricing with Demand Learning: Emerging Topics and State of the Art -- 4.1 Introduction -- 4.2 Model -- 4.3 Asymptotically Optimal Pricing Policies -- 4.3.1 Parametric Approaches -- 4.3.1.1 Model and Estimation -- 4.3.1.2 Certainty-Equivalence Pricing and Incomplete Learning -- 4.3.1.3 Asymptotically Optimal Policies -- 4.3.1.4 Extensions to Generalized Linear Models -- 4.3.1.5 Extensions to Multiple Products -- 4.3.2 Nonparametric Approaches -- 4.3.3 Extensions and Generalizations -- 4.4 Emerging Topics and Generalizations -- 4.4.1 Product Differentiation -- 4.4.2 Online Marketplaces -- 4.4.3 Continuous-Time Approximations -- References -- 5 Learning and Pricing with Inventory Constraints -- 5.1 Introduction -- 5.2 Single Product Case -- 5.2.1 Dynamic Pricing Algorithm -- 5.2.2 Lower Bound Example -- 5.3 Multiproduct Setting -- 5.3.1 Preliminaries -- 5.3.2 Parametric Case -- 5.3.3 Nonparametric Case -- 5.4 Bayesian Learning Setting -- 5.4.1 Model Setting -- 5.4.2 Thompson Sampling with Fixed Inventory Constraints -- 5.4.3 Thompson Sampling with Inventory Constraint Updating -- 5.4.4 Performance Analysis -- 5.5 Remarks and Further Reading -- References -- 6 Dynamic Pricing and Demand Learning in Nonstationary Environments -- 6.1 Introduction -- 6.2 Problem Formulation -- 6.3 Exogenously Changing Demand Environments -- 6.3.1 Change-Point Detection Models -- 6.3.2 Finite-State-Space Markov Chains -- 6.3.3 Autoregressive Models -- 6.3.4 General Changing Environments -- 6.3.5 Contextual Pricing -- 6.4 Endogenously Changing Demand Environments -- 6.4.1 Reference-Price Effects -- 6.4.2 Competition and Collusion -- 6.4.3 Platforms and Multi-Agent Learning -- 6.4.4 Forward-Looking and Patient Customers -- References -- 7 Pricing with High-Dimensional Data -- 7.1 Introduction. 7.2 Background: High-Dimensional Statistics -- 7.3 Static Pricing with High-Dimensional Data -- 7.3.1 Feature-Dependent Choice Model -- 7.3.2 Estimation Method -- 7.3.3 Performance Guarantees -- 7.4 Dynamic Pricing with High-Dimensional Data -- 7.4.1 Feature-Dependent Demand Model -- 7.4.2 Learning-and-Earning Algorithm -- 7.4.3 A Universal Lower Bound on the Regret -- 7.4.4 Performance of ILQX -- 7.4.5 Discussion -- 7.5 Directions for Future Research -- References -- Part III Assortment Optimization -- 8 Nonparametric Estimation of Choice Models -- 8.1 Introduction -- 8.2 General Setup -- 8.3 Estimating the Rank-Based Model -- 8.3.1 Estimation via the Conditional Gradient Algorithm -- 8.3.1.1 Solving the Support Finding Step -- 8.3.1.2 Solving the Proportions Update Step -- 8.3.1.3 Initialization and Stopping Criterion -- 8.3.2 Convergence Guarantee for the Estimation Algorithm -- 8.4 Estimating the Nonparametric Mixture of Closed Logit (NPMXCL) Model -- 8.4.1 Estimation via the Conditional Gradient Algorithm -- 8.4.1.1 Solving the Support Finding Step -- 8.4.1.2 Solving the Proportions Update Step -- 8.4.1.3 Initialization and Stopping Criterion -- 8.4.2 Convergence Guarantee for the Estimation Algorithm -- 8.4.3 Characterizing the Choice Behavior of Closed Logit Types -- 8.5 Other Nonparametric Choice Models -- 8.6 Concluding Thoughts -- References -- 9 The MNL-Bandit Problem -- 9.1 Introduction -- 9.2 Choice Modeling and Assortment Optimization -- 9.3 Dynamic Learning in Assortment Selection -- 9.4 A UCB Approach for the MNL-Bandit -- 9.4.1 Algorithmic Details -- 9.4.2 Min-Max Regret Bounds -- 9.4.3 Improved Regret Bounds for ``Well Separated'' Instances -- 9.4.4 Computational Study -- 9.4.4.1 Robustness of Algorithm 1 -- 9.4.4.2 Comparison with Existing Approaches -- 9.5 Thompson Sampling for the MNL-Bandit -- 9.5.1 Algorithm. 9.5.2 A TS Algorithm with Independent Beta Priors -- 9.5.3 A TS Algorithm with Posterior Approximation and Correlated Sampling -- 9.5.4 Regret Analysis -- 9.5.5 Empirical Study -- 9.6 Lower Bound for the MNL-Bandit -- 9.7 Conclusions and Recent Progress -- References -- 10 Dynamic Assortment Optimization: Beyond MNL Model -- 10.1 Overview -- 10.2 General Utility Distributions -- 10.2.1 Model Formulation and Assumptions -- 10.2.2 Algorithm Design -- 10.2.3 Theoretical Analysis -- 10.2.4 Bibliographic Notes and Discussion of Future Directions -- 10.3 Nested Logit Models -- 10.3.1 Model Formulation and Assumptions -- 10.3.2 Assortment Space Reductions -- 10.3.3 Algorithm Design and Regret Analysis -- 10.3.4 Regret Lower Bound -- 10.3.5 Bibliographic Notes and Discussion of Future Directions -- 10.4 MNL Model with Contextual Features -- 10.4.1 Model Formulation and Assumptions -- 10.4.2 Algorithm Design: Thompson Sampling -- 10.4.3 Algorithm Design: Upper Confidence Bounds -- 10.4.4 Lower Bounds -- 10.4.5 Bibliographic Notes and Discussion of Future Directions -- 10.5 Conclusion -- References -- Part IV Inventory Optimization -- 11 Inventory Control with Censored Demand -- 11.1 Introduction -- 11.2 Regret Lower Bound for Inventory Models with Censored Demand -- 11.2.1 Model Formulation -- 11.2.2 Strictly Convex and Well-Separated Cases -- 11.2.3 Worst-Case Regret Under General Demand Distributions -- 11.3 Censored Demand Example: Perishable Inventory System -- 11.3.1 Model Formulation -- 11.3.2 Challenges and Preliminary Results -- 11.3.3 Learning Algorithm Design: Cycle-Update Policy -- 11.3.4 Regret Analysis of CUP Algorithm -- 11.3.5 Strongly Convex Extension -- 11.4 Lead Times Example: Lost-Sales System with Lead Times -- 11.4.1 Model Formulation -- 11.4.2 Base-Stock Policy and Convexity Results -- 11.4.3 Challenges from Lead Times. 11.4.4 Gradient Methods -- 11.4.5 A Ternary Search Method -- 11.5 High Dimensionality Example: Multiproduct Inventory Model with Customer Choices -- 11.5.1 Inventory Substitution -- 11.5.2 Numerical Example -- References -- 12 Joint Pricing and Inventory Control with Demand Learning -- 12.1 Problem Formulation in General -- 12.2 Nonparametric Learning for Backlogged Demand -- 12.3 Nonparametric Learning for Lost-Sales System -- 12.3.1 Algorithms and Results in chen2017nonparametric -- 12.3.2 Algorithms and Results in chen2020optimal -- 12.3.2.1 Concave G(·) -- 12.3.2.2 Non-Concave G(·) -- 12.4 Parametric Learning with Limited Price Changes -- 12.4.1 Well-Separated Demand -- 12.4.2 General Demand -- 12.5 Backlog System with Fixed Ordering Cost -- 12.6 Other Models -- References -- 13 Optimization in the Small-Data, Large-Scale Regime -- 13.1 Why Small Data? -- 13.1.1 Structure -- 13.2 Contrasting the Large-Sample and Small-Data, Large-Scale Regimes -- 13.2.1 Model -- 13.2.2 Failure of Sample Average Approximation (SAA) -- 13.2.3 Best-in-Class Performance -- 13.2.4 Shortcomings of Cross-Validation -- 13.3 Debiasing In-Sample Performance -- 13.3.1 Stein Correction -- 13.3.2 From Unbiasedness to Policy Selection -- 13.3.3 Stein Correction in the Large-Sample Regime -- 13.3.4 Open Questions -- 13.4 Conclusion -- References -- Part V Healthcare Operations -- 14 Bandit Procedures for Designing Patient-Centric Clinical Trials -- 14.1 Introduction -- 14.2 The Bayesian Beta-Bernoulli MABP -- 14.2.1 Discussion of the Model -- 14.3 Metrics for Two-Armed Problem (Confirmatory Trials) -- 14.3.1 Accurate and Precise Estimation -- 14.3.2 Statistical Errors -- 14.3.3 Patient Benefit -- 14.3.4 Trial Size -- 14.3.5 Multiple Metrics -- 14.4 Illustrative Results for Two-Armed Problem -- 14.5 Discussion -- 14.5.1 Safety Concerns -- 14.5.2 Prior Distributions. 14.5.3 Delayed Responses. |
| Record Nr. | UNINA-9910595059703321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Event-Trigger Dynamic State Estimation for Practical WAMS Applications in Smart Grid [[electronic resource] /] / by Zhen Li, Sen Li, Tyrone Fernando, Xi Chen
| Event-Trigger Dynamic State Estimation for Practical WAMS Applications in Smart Grid [[electronic resource] /] / by Zhen Li, Sen Li, Tyrone Fernando, Xi Chen |
| Autore | Li Zhen |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (294 pages) |
| Disciplina | 621.3191 |
| Soggetto topico |
Electronic circuits
Signal processing Image processing Speech processing systems Energy systems Circuits and Systems Signal, Image and Speech Processing Energy Systems |
| ISBN | 3-030-45658-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Event-trigger Design for Linear Filtering Event-trigger Strategies -- State Estimation of Doubly Fed Induction Generator (DFIG) Wind Turbine (WT) in Smart Grid -- Event-trigger Particle Filter Design under Limited Communication Bandwidth -- Event-trigger Heterogeneous Nonlinear Filter Design under Limited Computational Burden -- Event-trigger Robust Nonlinear Filter Design under Non-Gaussian Noises -- Event-trigger Robust Nonlinear Filter Design with Packet Dropout -- Discussion on Other Practical Design. |
| Record Nr. | UNINA-9910407734203321 |
Li Zhen
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Five-layer intelligence of the machine brain : system modelling and simulation / / Wen-Feng Wang, Xi Chen, Tuozhong Yao
| Five-layer intelligence of the machine brain : system modelling and simulation / / Wen-Feng Wang, Xi Chen, Tuozhong Yao |
| Autore | Wang Wenfeng |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (223 pages) |
| Disciplina | 006.31 |
| Collana | Research on Intelligent Manufacturing |
| Soggetto topico | Artificial intelligence |
| ISBN | 981-19-0272-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Structure of a machine brain The first intelligence layer - environments sensing The second intelligence layer - active learning The third intelligence layer - cognitive computing The fourth intelligence layer - intelligent decisions making The fifth intelligence layer - automatized execution Applications in Face Recognition Access Control Manufacturing |
| Record Nr. | UNINA-9910552748503321 |
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Wang Wenfeng
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| Singapore : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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The Geographical Sciences During 1986—2015 [[electronic resource] ] : From the Classics To the Frontiers / / by Shuying Leng, Xizhang Gao, Tao Pei, Guoyou Zhang, Liangfu Chen, Xi Chen, Canfei He, Daming He, Xiaoyan Li, Chunye Lin, Hongyan Liu, Weidong Liu, Yihe Lü, Shilong Piao, Qiuhong Tang, Fulu Tao, Lide Tian, Xiaohua Tong, Cunde Xiao, Desheng Xue, Linsheng Yang, Linwang Yuan, Yuanming Zheng, Huiyi Zhu, Liping Zhu
| The Geographical Sciences During 1986—2015 [[electronic resource] ] : From the Classics To the Frontiers / / by Shuying Leng, Xizhang Gao, Tao Pei, Guoyou Zhang, Liangfu Chen, Xi Chen, Canfei He, Daming He, Xiaoyan Li, Chunye Lin, Hongyan Liu, Weidong Liu, Yihe Lü, Shilong Piao, Qiuhong Tang, Fulu Tao, Lide Tian, Xiaohua Tong, Cunde Xiao, Desheng Xue, Linsheng Yang, Linwang Yuan, Yuanming Zheng, Huiyi Zhu, Liping Zhu |
| Autore | Leng Shuying |
| Edizione | [1st ed. 2017.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2017 |
| Descrizione fisica | 1 online resource (LII, 596 p. 211 illus., 209 illus. in color.) |
| Disciplina | 910.951 |
| Collana | Springer Geography |
| Soggetto topico |
Geography
Environment Human geography Climate change Hydrology Geography, general Environment, general Human Geography Climate Change/Climate Change Impacts Hydrology/Water Resources |
| ISBN | 981-10-1884-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction: Interpreting the Geographical Sciences from a Perspective of Bibliometric Analysis -- Chapter 1 An Overview of Development in the Geographical Sciences -- Chapter 2 Trends in the Development of the Four Branches of the Geographical Sciences -- Chapter 3 Strategic Research Issues on the Geographical Sciences -- Chapter 4 A Review and Outlook of Research Fields on the Geographical Sciences Regarding NSFC. |
| Record Nr. | UNINA-9910254015003321 |
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Leng Shuying
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| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Mechanical Self-Assembly [[electronic resource] ] : Science and Applications / / edited by Xi Chen
| Mechanical Self-Assembly [[electronic resource] ] : Science and Applications / / edited by Xi Chen |
| Edizione | [1st ed. 2013.] |
| Pubbl/distr/stampa | New York, NY : , : Springer New York : , : Imprint : Springer, , 2013 |
| Descrizione fisica | 1 online resource (212 p.) |
| Disciplina | 621 |
| Soggetto topico |
Mechanics
Mechanics, Applied Nanotechnology Surfaces (Physics) Interfaces (Physical sciences) Thin films Biomedical engineering Theoretical and Applied Mechanics Surface and Interface Science, Thin Films Biomedical Engineering and Bioengineering |
| Soggetto non controllato |
Engineering
Mechanics, applied Biomedical engineering Nanotechnology Theoretical and Applied Mechanics Surface and Interface Science, Thin Films |
| ISBN |
1-283-93363-2
1-4614-4562-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Mechanical Self-Assembly in Nature -- Mechanical Self-Assembly vs. Morphogenesis -- Shaping by Active Deformation of Soft Elastic Sheets -- Ion Beam Induced Self-Assembled Wrinkles -- A Kinetics Approach to Surface Wrinkling of Elastic Thin Films -- Crease Instability on the Surface of a Solid -- Buckling Delamination of Compressed Thin Films -- Delaminated Film Buckling Microchannels -- Mechanical Self-Assembly on Curved Substrates. . |
| Record Nr. | UNINA-9910437889103321 |
| New York, NY : , : Springer New York : , : Imprint : Springer, , 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Reading Development and Difficulties in Monolingual and Bilingual Chinese Children [[electronic resource] /] / edited by Xi Chen, Qiuying Wang, Yang Cathy Luo
| Reading Development and Difficulties in Monolingual and Bilingual Chinese Children [[electronic resource] /] / edited by Xi Chen, Qiuying Wang, Yang Cathy Luo |
| Edizione | [1st ed. 2014.] |
| Pubbl/distr/stampa | Dordrecht : , : Springer Netherlands : , : Imprint : Springer, , 2014 |
| Descrizione fisica | 1 online resource (264 p.) |
| Disciplina | 495.1 |
| Collana | Literacy Studies, Perspectives from Cognitive Neurosciences, Linguistics, Psychology and Education |
| Soggetto topico |
Literacy
Applied linguistics Language and education Psycholinguistics Applied Linguistics Language Education |
| ISBN | 94-007-7380-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Foreword -- Preface -- Psycholinguistic Study of Reading Chinese. Morphological Awareness and Learning to Read Chinese and English -- Visual, Phonological and Orthographic Strategies in Learning to Read Chinese -- How Character Reading Can Be Different from Word Reading in Chinese and Why It Matters for Chinese Reading Development -- Fostering Reading Comprehension and Writing Composition in Chinese Children -- Exploring the Relationship of Parental Influences, Motivation for Reading and Reading Achievement in Chinese First Graders -- Reading Disability in Chinese Children. Helping Children with Reading Disability in Chinese: The Response to Intervention Approach with Effective Evidence-Based Curriculum -- Rapid Automatized Naming and Its Unique Contribution to Reading: Evidence from Chinese Dyslexia -- Bilingual and Biliteracy Development in Chinese and English. L1-Induced Facilitation in Biliteracy Development in Chinese and English -- Effect of Early Bilingualism on Metalinguistic Development and Language Processing: Evidence from Chinese-speaking Bilingual Children -- Contributions of Phonology, Orthography, and Morphology in Chinese-English Biliteracy Acquisition: A One-year Longitudinal Study -- Children’s literature in Chinese. Chinese Children’s Literature in North America -- China and Chinese as Mirrored in Multicultural Youth Literature: A Study of Award-Winning Picture Books Featuring Ethnic Chinese from 1993 to 2009. |
| Record Nr. | UNINA-9910484887203321 |
| Dordrecht : , : Springer Netherlands : , : Imprint : Springer, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Reliability based airframe maintenance optimization and applications / / He Ren, Yong Chen, Xi Chen
| Reliability based airframe maintenance optimization and applications / / He Ren, Yong Chen, Xi Chen |
| Autore | Ren He |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | London, England : , : Academic Press, , 2017 |
| Descrizione fisica | 1 online resource (237 pages) |
| Disciplina | 629.1346 |
| Soggetto topico |
Airplanes - Maintenance and repair
Airframes |
| ISBN | 0-12-812669-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910583370003321 |
|
Ren He
|
||
| London, England : , : Academic Press, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Tensor Network Contractions [[electronic resource] ] : Methods and Applications to Quantum Many-Body Systems / / by Shi-Ju Ran, Emanuele Tirrito, Cheng Peng, Xi Chen, Luca Tagliacozzo, Gang Su, Maciej Lewenstein
| Tensor Network Contractions [[electronic resource] ] : Methods and Applications to Quantum Many-Body Systems / / by Shi-Ju Ran, Emanuele Tirrito, Cheng Peng, Xi Chen, Luca Tagliacozzo, Gang Su, Maciej Lewenstein |
| Autore | Ran Shi-Ju |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham, : Springer Nature, 2020 |
| Descrizione fisica | 1 online resource (XIV, 150 p. 68 illus., 65 illus. in color.) |
| Disciplina | 530.15 |
| Collana | Lecture Notes in Physics |
| Soggetto topico |
Physics
Quantum physics Quantum optics Statistical physics Machine learning Elementary particles (Physics) Quantum field theory Mathematical Methods in Physics Quantum Physics Quantum Optics Statistical Physics and Dynamical Systems Machine Learning Elementary Particles, Quantum Field Theory |
| Soggetto non controllato |
Physics
Quantum physics Quantum optics Statistical physics Machine learning Elementary particles (Physics) Quantum field theory |
| ISBN | 3-030-34489-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Tensor Network: Basic Definitions and Properties -- Two-Dimensional Tensor Networks and Contraction Algorithms -- Tensor Network Approaches for Higher-Dimensional Quantum Lattice Models -- Tensor Network Contraction and Multi-Linear Algebra -- Quantum Entanglement Simulation Inspired by Tensor Network -- Summary. |
| Record Nr. | UNISA-996418171403316 |
|
Ran Shi-Ju
|
||
| Cham, : Springer Nature, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Tensor Network Contractions [[electronic resource] ] : Methods and Applications to Quantum Many-Body Systems / / by Shi-Ju Ran, Emanuele Tirrito, Cheng Peng, Xi Chen, Luca Tagliacozzo, Gang Su, Maciej Lewenstein
| Tensor Network Contractions [[electronic resource] ] : Methods and Applications to Quantum Many-Body Systems / / by Shi-Ju Ran, Emanuele Tirrito, Cheng Peng, Xi Chen, Luca Tagliacozzo, Gang Su, Maciej Lewenstein |
| Autore | Ran Shi-Ju |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham, : Springer Nature, 2020 |
| Descrizione fisica | 1 online resource (XIV, 150 p. 68 illus., 65 illus. in color.) |
| Disciplina | 530.15 |
| Collana | Lecture Notes in Physics |
| Soggetto topico |
Physics
Quantum physics Quantum optics Statistical physics Machine learning Elementary particles (Physics) Quantum field theory Mathematical Methods in Physics Quantum Physics Quantum Optics Statistical Physics and Dynamical Systems Machine Learning Elementary Particles, Quantum Field Theory |
| Soggetto non controllato |
Physics
Quantum physics Quantum optics Statistical physics Machine learning Elementary particles (Physics) Quantum field theory |
| ISBN | 3-030-34489-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Tensor Network: Basic Definitions and Properties -- Two-Dimensional Tensor Networks and Contraction Algorithms -- Tensor Network Approaches for Higher-Dimensional Quantum Lattice Models -- Tensor Network Contraction and Multi-Linear Algebra -- Quantum Entanglement Simulation Inspired by Tensor Network -- Summary. |
| Record Nr. | UNINA-9910372747203321 |
|
Ran Shi-Ju
|
||
| Cham, : Springer Nature, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
