03228cam a2200529 i 4500001001300000003000600013005001700019006001900036007001500055008004100070010001700111019001500128020002600143020002300169020003100192040005900223049000900282050001000291082001800301099001300319100003100332245008000363246005900443250002000502264009900522264001100621300004500632336002600677337002600703338003600729504005100765505054300816520038101359588007001740542004201810630004701852650003201899650002101931700004101952710008401993710003402077710004902111775021102160776023502371856008002606994001202686on1376788347OCoLC20250617024401.0m     o  d        cr |||||||||||230420t20232023iluab   ob    001 0 eng    a  2023018640  a1396267830  a9781639052943q(epub)  a1639052941q(epub)  z9781639052936q(paperback)  aDLCbengerdacDLCdCLUdOCLCFdTEFODdOCLCOdYDXdBNA  aBNAM00aK150500a346.04/86223  aINTERNET1 aErstling, Jay A.,eauthor.14aThe practitioner's guide to the PCT /cJay A. Erstling and Megan M. Miller.3 aPractitioner's guide to the Patent Co-operation Treaty  aSecond edition. 1aChicago, Illinois :bABA, American Bar Association, Intellectual Property Law Section,c[2023] 4c©2023  a1 online resource :billustrations, maps  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  aIncludes bibliographical references and index.0 aAn overview of the PCT system -- PCT procedures -- International search and preliminary examination -- Post-filing procedures : publication, withdrawals, and recording changes -- Procedural safeguards : helpful options when things go wrong -- Entering the national phase -- Entry into the U.S. national stage -- Entry into the national phase in Europe, China, and elsewhere -- PCT strategies and recommendations -- WIPO resources -- Afterword: The future of the PCT -- Appendix: PCT jargon: Glossary of terms -- Glossary of abbreviations.  a"The objective of the revised edition of The Practitioner's Guide to the PCT is to continue the process of demystifying the PCT. The guide is based on the authors' many years of experience with the system, on the numerous PCT seminars and presentations they have given, and on the important lessons they have learned from the mistakes they have made"--cProvided by publisher.  aDescription based on Bloomberg Law website, viewed June 17, 2025.  dBloomberg Industry Group, Inc.g2022-00aPatent Co-operation Treatyd(1970 June 19) 0aPatents (International law) 0aPatent practice.1 aMiller, Megan M.c(Lawyer),eauthor.2 aAmerican Bar Association.bSection of Intellectual Property Law,eissuing body.2 aBloomberg Law,eissuing body.2 aBloomberg Industry Group,ecopyright holder.08iRevision of:aErstling, Jay A.tPractitioner's guide to the PCT.bFirst edition.dChicago, Illinois : American Bar Association, Section of Intellectual Property Law, [2013]z9781627220156w(OCoLC)107881581308iPrint version:aErstling, Jay A.tPractitioner's guide to the PCT.bSecond edition.dChicago, Illinois : ABA, American Bar Association, Intellectual Property Law Section, [2023]z9781639052936w(DLC)  2023018639w(OCoLC)1376789320403Bloomberg Lawuhttps://www.bloomberglaw.com/product/blaw/browser/105.566520  aC0bBNA04000cai a2200397 i 4500001001300000003000600013005001700019006001900036007001500055008004100070020002600111020002300137040002800160049000900188050002000197099001300217100003300230245006900263264004500332300002200377310002400399336002600423337002600449338003600475504004100511505248600552520020203038588006603240542004203306650004403348650003503392710003403427710004903461856008003510994001203590on1525533260OCoLC20250627020053.0m     o  d        cr |||||||||||250627c20259999vau x w o b   0    2eng d  a9781682679227q(epub)  a1682679225q(epub)  aBNAbengcBNAerdadBNA  aBNAM 4aKF1263.C65bT47  aINTERNET1 aTeppler, Steven W.,eauthor.10aCybersecurity practice guide for law firms /cSteven W. Teppler. 1aArlington, VA :bBloomberg Law,c[2025]-  a1 online resource  aUpdated irregularly  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  aIncludes bibliographical references.0 aIntroduction: The Cybersecurity Imperative for Law Firms -- The Evolving Nature of Cyber Threats: Understanding the Risks to Law Firms -- The Value of Legal Data: Why Law Firms Are Prime Targets -- Liabilities Arising from Cybersecurity Breaches: Navigating Legal and Ethical Risks -- Professional Responsibility and Ethical Duties in Cybersecurity: Navigating the Legal and Ethical Imperatives -- Building a Secure Law Firm: Technical Measures, Policies, and Best Practices -- Developing an Effective Incident Response Plan: Detection, Management, and Recovery -- Cybersecurity Insurance and Risk Management: Mitigating Financial Impact and Enhancing Security -- Legal and Ethical Implications of Handling Client Data in the Digital Millennium -- Implementing Technical Solutions for Securing Law Firm Data -- Integrating Cybersecurity with Law Firm Management Practices -- The Future of Cybersecurity in the Legal Industry: Trends, Technologies, and Strategic Adaptations -- The Impact of Digital Transformation on Law Firms: Leveraging Technology for Enhanced Client Service and Operational Efficiency -- Ethical Considerations in Legal Technology: Balancing Innovation with Professional Responsibility -- Developing a Comprehensive Risk Management and Compliance Framework in a Technology-Driven Law Firm -- Adapting to Remote Work and Hybrid Law Firm Models: Ensuring Security, Collaboration, and Client Service -- Legal Technology and Innovation Management: Strategies for Effective Integration and Fostering a Culture of Innovation -- Leveraging Advanced Data Analytics in Legal Practice: Optimizing Case Strategies, Enhancing Client Service, and Improving Decision-Making -- Cybersecurity Insurance: A Critical Risk Management Tool for Law Firms -- Building a Cybersecurity-Conscious Law Firm Culture: Integrating Policies, Training, and Compliance -- Artificial Intelligence (AI) in Law Firms: Legal, Ethical, and Practical Considerations -- The Intersection of Cybersecurity and Intellectual Property (IP) Law: Protecting IP Assets in a Digital World -- Navigating Cross-Border Data Protection Regulations: Strategies for Compliance and Risk Management -- Privacy Technologies and Their Impact on Legal Compliance and Cybersecurity Strategy -- Ethical Considerations and Professional Responsibility in the Use of Technology in Legal Practice -- Diversity, Equity, and Inclusion (DEI) in Law Firms: Leveraging Technology to Build an Inclusive and Effective Legal Practice.  a"Cybersecurity Practice Guide for Law Firms is a Guide for legal professionals on cybersecurity, data privacy, and digital ethics addressing the intersection of technology, ethics, and regulation."  aTitle from Bloomberg Law title screen (viewed June 27, 2025).  dBloomberg Industry Group, Inc.g2022- 0aComputer securityxLaw and legislation. 0aCyberspacexSecurity measures.2 aBloomberg Law,eissuing body.2 aBloomberg Industry Group,ecopyright holder.403Bloomberg Lawuhttps://www.bloomberglaw.com/product/blaw/browser/105.566334  aC0bBNA11342nam a2200433 i 4500001001300000003000600013005001700019006001900036007001500055008004100070010001700111020002600128020003600154020003600190020003300226040005400259049000900313050001500322082003200337099001300369100004400382245007100426264004200497300002200539336002600561337002600587338003600613500002000649520090300669588006501572505900901637542003510646650002310681650002910704710003410733710004910767856008010816994001210896on1478248947OCoLC20250630103525.0m     o  d        cr |||||||||||250630s2025    nju     o     001 0 eng d  a  2024049423  a9781394279302q(epub)  a9781394279319q(electronic bk.)  a9781394279326q(electronic bk.)  a1394279329q(electronic bk.)  aDLCbengerdacDLCdOCLCOdORMDAdN$TdIEEEEdBNA  aBNAM00aQA76.9.A2500a174/.90063223/eng/20241209  aINTERNET1 aIslam, Rayq(Mohammad Rubyet),eauthor.10aGenerative AI, cybersecurity, and ethics /cMohammad Rubyet Islam. 1aHoboken, New Jersey :bWiley,c[2025]  a1 online resource  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  aIncludes index.  a"Generative AI (GenAI) is set to revolutionize cybersecurity by greatly improving threat detection, risk analysis, and response strategies, thus enhancing digital security. Governments and private organizations worldwide are increasingly recognizing the need for specific ethical and regulatory policies tailored to AI and cybersecurity. However, GenAI presents unique challenges not found in other AI sectors. The emergence of technologies like ChatGPT and advanced deepfake creation, which showcase GenAI's potential in cybersecurity, highlights the importance of targeted focus in this field. As GenAI continues to advance, it is expected that corresponding ethical and regulatory frameworks will also evolve, addressing critical issues such as data privacy, consent, and accountability that are particularly relevant to the rapid progress and implementation of GenAI."--cProvided by publisher.  aTitle from Bloomberg Law title screen, viewed June 30, 2025.0 aList of Figures xxiii -- List of Tables xxv -- Endorsements xxvii -- About the Author xxxi -- Preface xxxiii -- Acknowledgements xxxv -- 1 Introduction 1 -- 1.1 Artificial Intelligence (AI) 1 -- 1.1.1 Narrow AI (Weak AI) 2 -- 1.1.2 General AI (Strong AI) 2 -- 1.2 Machine Learning (ML) 3 -- 1.3 Deep Learning 3 -- 1.4 Generative AI 4 -- 1.4.1 GenAI vs. Other AI 5 -- 1.5 Cybersecurity 6 -- 1.6 Ethics 7 -- 1.7 AI to GenAI: Milestones and Evolutions 8 -- 1.7.1 1950s: Foundations of AI 8 -- 1.7.2 1960s: Early AI Developments 9 -- 1.7.3 1970s-1980s: AI Growth and AI Winter 9 -- 1.7.4 1990s: New Victory 9 -- 1.7.5 2010s: Rise of GenAI 10 -- 1.8 AI in Cybersecurity 10 -- 1.8.1 Advanced Threat Detection and Prevention 10 -- 1.8.2 Real-Time Adaptation and Responsiveness 11 -- 1.8.3 Behavioral Analysis and Anomaly Detection 11 -- 1.8.4 Phishing Mitigation 11 -- 1.8.5 Harnessing Threat Intelligence 11 -- 1.8.6 GenAI in Cybersecurity 12 -- 1.9 Introduction to Ethical Considerations in GenAI 12 -- 1.9.1 Bias and Fairness 12 -- 1.9.2 Privacy 12 -- 1.9.3 Transparency and Explainability 13 -- 1.9.4 Accountability and Responsibility 13 -- 1.9.5 Malicious Use 13 -- 1.9.6 Equity and Access 13 -- 1.9.7 Human Autonomy and Control 14 -- 1.10 Overview of the Regional Regulatory Landscape for GenAI 14 -- 1.10.1 North America 14 -- 1.10.2 Europe 15 -- 1.10.3 Asia 15 -- 1.10.4 Africa 15 -- 1.10.5 Australia 15 -- 1.11 Tomorrow 15 -- 2 Cybersecurity: Understanding the Digital Fortress 17 -- 2.1 Different Types of Cybersecurity 17 -- 2.1.1 Network Security 17 -- 2.1.2 Application Security 19 -- 2.1.3 Information Security 20 -- 2.1.4 Operational Security 21 -- 2.1.5 Disaster Recovery and Business Continuity 22 -- 2.1.6 Endpoint Security 22 -- 2.1.7 Identity and Access Management (IAM) 23 -- 2.1.8 Cloud Security 24 -- 2.1.9 Mobile Security 24 -- 2.1.10 Critical Infrastructure Security 24 -- 2.1.11 Physical Security 25 -- 2.2 Cost of Cybercrime 25 -- 2.2.1 Global Impact 25 -- 2.2.2 Regional Perspectives 27 -- 2.2.2.1 North America 27 -- 2.2.2.2 Europe 28 -- 2.2.2.3 Asia 28 -- 2.2.2.4 Africa 28 -- 2.2.2.5 Latin America 29 -- 2.3 Industry-Specific Cybersecurity Challenges 30 -- 2.3.1 Financial Sector 30 -- 2.3.2 Healthcare 30 -- 2.3.3 Government 31 -- 2.3.4 E-Commerce 31 -- 2.3.5 Industrial and Critical Infrastructure 32 -- 2.4 Current Implications and Measures 32 -- 2.5 Roles of AI in Cybersecurity 33 -- 2.5.1 Advanced Threat Detection and Anomaly Recognition 33 -- 2.5.2 Proactive Threat Hunting 34 -- 2.5.3 Automated Incident Response 34 -- 2.5.4 Enhancing IoT and Edge Security 34 -- 2.5.5 Compliance and Data Privacy 35 -- 2.5.6 Predictive Capabilities in Cybersecurity 35 -- 2.5.7 Real-Time Detection and Response 35 -- 2.5.8 Autonomous Response to Cyber Threats 36 -- 2.5.9 Advanced Threat Intelligence 36 -- 2.6 Roles of GenAI in Cybersecurity 36 -- 2.7 Importance of Ethics in Cybersecurity 37 -- 2.7.1 Ethical Concerns of AI in Cybersecurity 37 -- 2.7.2 Ethical Concerns of GenAI in Cybersecurity 38 -- 2.7.3 Cybersecurity-Related Regulations: A Global Overview 39 -- 2.7.3.1 United States 39 -- 2.7.3.2 Canada 39 -- 2.7.3.3 United Kingdom 41 -- 2.7.3.4 European Union 42 -- 2.7.3.5 Asia-Pacific 42 -- 2.7.3.6 Australia 43 -- 2.7.3.7 India 43 -- 2.7.3.8 South Korea 43 -- 2.7.3.9 Middle East and Africa 43 -- 2.7.3.10 Latin America 44 -- 2.7.4 UN SDGs for Cybersecurity 45 -- 2.7.5 Use Cases for Ethical Violation of GenAI Affecting Cybersecurity 46 -- 2.7.5.1 Indian Telecom Data Breach 46 -- 2.7.5.2 Hospital Simone Veil Ransomware Attack 46 -- 2.7.5.3 Microsoft Azure Executive Accounts Breach 46 -- 3 Understanding GenAI 47 -- 3.1 Types of GenAI 48 -- 3.1.1 Text Generation 49 -- 3.1.2 Natural Language Understanding (NLU) 49 -- 3.1.3 Image Generation 49 -- 3.1.4 Audio and Speech Generation 50 -- 3.1.5 Music Generation 50 -- 3.1.6 Video Generation 50 -- 3.1.7 Multimodal Generation 50 -- 3.1.8 Drug Discovery and Molecular Generation 51 -- 3.1.9 Synthetic Data Generation 51 -- 3.1.10 Predictive Text and Autocomplete 51 -- 3.1.11 Game Content Generation 52 -- 3.2 Current Technological Landscape 52 -- 3.2.1 Advancements in GenAI 52 -- 3.2.2 Cybersecurity Implications 52 -- 3.2.3 Ethical Considerations 54 -- 3.3 Tools and Frameworks 54 -- 3.3.1 Deep Learning Frameworks 54 -- 3.4 Platforms and Services 56 -- 3.5 Libraries and Tools for Specific Applications 58 -- 3.6 Methodologies to Streamline Life Cycle of GenAI 60 -- 3.6.1 Machine Learning Operations (MLOps) 60 -- 3.6.2 AI Operations (AIOps) 62 -- 3.6.3 MLOps vs. AIOps 63 -- 3.6.4 Development and Operations (DevOps) 65 -- 3.6.5 Data Operations (DataOps) 66 -- 3.6.6 ModelOps 67 -- 3.7 A Few Common Algorithms 67 -- 3.7.1 Generative Adversarial Networks 67 -- 3.7.2 Variational Autoencoders (VAEs) 69 -- 3.7.3 Transformer Models 70 -- 3.7.4 Autoregressive Models 70 -- 3.7.5 Flow-Based Models 71 -- 3.7.6 Energy-Based Models (EBMs) 71 -- 3.7.7 Diffusion Models 71 -- 3.7.8 Restricted Boltzmann Machines (RBMs) 72 -- 3.7.9 Hybrid Models 72 -- 3.7.10 Multimodal Models 72 -- 3.8 Validation of GenAI Models 73 -- 3.8.1 Quantitative Validation Techniques 73 -- 3.8.2 Advanced Statistical Validation Methods 76 -- 3.8.3 Qualitative and Application-Specific Evaluation 77 -- 3.9 GenAI in Actions 78 -- 3.9.1 Automated Journalism 78 -- 3.9.2 Personalized Learning Environments 78 -- 3.9.3 Predictive Maintenance in Manufacturing 79 -- 3.9.4 Drug Discovery 79 -- 3.9.5 Fashion Design 80 -- 3.9.6 Interactive Chatbots for Customer Service 80 -- 3.9.7 Generative Art 80 -- 4 GenAI in Cybersecurity 83 -- 4.1 The Dual-Use Nature of GenAI in Cybersecurity 83 -- 4.2 Applications of GenAI in Cybersecurity 84 -- 4.2.1 Anomaly Detection 84 -- 4.2.2 Threat Simulation 85 -- 4.2.3 Automated Security Testing 86 -- 4.2.4 Phishing Email Creation for Training 86 -- 4.2.5 Cybersecurity Policy Generation 86 -- 4.2.6 Deception Technologies 86 -- 4.2.7 Threat Modeling and Prediction 87 -- 4.2.8 Customized Security Measures 87 -- 4.2.9 Report Generation and Incident Reporting Compliance 87 -- 4.2.10 Creation of Dynamic Dashboards 87 -- 4.2.11 Analysis of Cybersecurity Legal Documents 88 -- 4.2.12 Training and Simulation 88 -- 4.2.13 GenAI for Cyber Defense for Satellites 88 -- 4.2.14 Enhanced Threat Detection 88 -- 4.2.15 Automated Incident Response 89 -- 4.3 Potential Risks and Mitigation Methods 89 -- 4.3.1 Risks 89 -- 4.3.1.1 AI-Generated Phishing Attacks 89 -- 4.3.1.2 Malware Development 89 -- 4.3.1.3 Adversarial Attacks Against AI Systems 90 -- 4.3.1.4 Creation of Evasive Malware 91 -- 4.3.1.5 Deepfake Technology 91 -- 4.3.1.6 Automated Vulnerability Discovery 91 -- 4.3.1.7 AI-Generated Disinformation 91 -- 4.3.2 Risk Mitigation Methods for GenAI 91 -- 4.3.2.1 Technical Solutions 92 -- 4.3.2.2 Incident Response Planning 94 -- 4.4 Infrastructure for GenAI in Cybersecurity 96 -- 4.4.1 Technical Infrastructure 96 -- 4.4.1.1 Computing Resources 96 -- 4.4.1.2 Data Storage and Management 98 -- 4.4.1.3 Networking Infrastructure 99 -- 4.4.1.4 High-Speed Network Interfaces 100 -- 4.4.1.5 AI Development Platforms 101 -- 4.4.1.6 GenAI-Cybersecurity Integration Tools 102 -- 4.4.2 Organizational Infrastructure 104 -- 4.4.2.1 Skilled Workforce 104 -- 4.4.2.2 Training and Development 105 -- 4.4.2.3 Ethical and Legal Framework 106 -- 4.4.2.4 Collaboration and Partnerships 107 -- 5 Foundations of Ethics in GenAI 111 -- 5.1 History of Ethics in GenAI-Related Technology 111 -- 5.1.1 Ancient Foundations 111 -- 5.1.2 The Industrial Era 112 -- 5.1.3 20th Century 113 -- 5.1.4 The Rise of Computers and the Internet 113 -- 5.1.5 21st Century: The Digital Age 113 -- 5.1.6 Contemporary Ethical Frameworks 113 -- 5.2 Basic Ethical Principles and Theories 113 -- 5.2.1 Metaethics 114 -- 5.2.2 Normative Ethics 114 -- 5.2.3 Applied Ethics 115 -- 5.3 Existing Regulatory Landscape: The Role of International Standards and Agreements 115 -- 5.3.1 ISO/IEC Standards 116 -- 5.3.1.1 For Cybersecurity 116 -- 5.3.1.2 For AI 117 -- 5.3.1.3 Loosely Coupled with GenAI 118 -- 5.3.2 EU Ethics Guidelines 118 -- 5.3.3 UNESCO Recommendations 119 -- 5.3.4 OECD Principles on AI 119 -- 5.3.5 G7 and G20 Summits 121 -- 5.3.6 IEEE's Ethically Aligned Design 121 -- 5.3.7 Asilomar AI Principles 121 -- 5.3.8 AI4People's Ethical Framework 122 -- 5.3.9 Google's AI Principles 123 -- 5.3.10 Partnership on AI 123 -- 5.4 Why Separate Ethical Standards for GenAI? 124 -- 5.5 United Nation's Sustainable Development Goals 125 -- 5.5.1 For Cybersecurity 125 -- 5.5.2 For AI 125 -- 5.5.3 For GenAI 127 -- 5.5.4 Alignment of Standards with SDGs for AI, GenAI, and Cybersecurity 127 -- 5.6 Regional Approaches: Policies for AI in Cybersecurity 128 -- 5.6.1 North America 128 -- 5.6.1.1 The United States of America 128 -- 5.6.1.2 Canada 131 -- 5.6.2 Europe 131 -- 5.6.2.1 EU Cybersecurity Strategy 131 -- 5.6.2.2 United States vs. EU 134 -- 5.6.2.3 United Kingdom 134 -- 5.6.3 Asia 135 -- 5.6.3.1 China 135 -- 5.6.3.2 Japan 136 -- 5.6.3.3 South Korea 136 ...  dBloomberg Industry Group, Inc. 0aComputer security. 0aArtificial intelligence.2 aBloomberg Law,eissuing body.2 aBloomberg Industry Group,ecopyright holder.403Bloomberg Lawuhttps://www.bloomberglaw.com/product/blaw/browser/105.566720  aC0bBNA11361nam a2200469 i 4500001001300000003000600013005001700019006001900036007001500055008004100070010001700111020002600128020003600154040005900190049000900249050002500258082003100283099001300314100003800327245008700365264004200452300002200494336002600516337002600542338003600568500002000604520042100624588007301045505187001118505188802988505185304876505187706729505188108606542003510487650002810522650002210550710003410572710004910606776014410655856008010799994001210879on1453287522OCoLC20250630123447.0m     o  d        cr |||||||||||250630s2025    nju     o     001 0 eng d  a  2024035507  a9781394275908q(epub)  a9781394275915q(electronic bk.)  aDLCbengerdacDLCdOCLCOdN$TdORMDAdUKAHLdDXUdBNA  aBNAM 4aQA76.9.B56bD56 202500a005.75/88223/eng/20240823  aINTERNET1 aDi Maggio, Marco,d1985-eauthor.10aBlockchain, crypto and DeFi :bbridging finance and technology /cMarco Di Maggio. 1aHoboken, New Jersey :bWiley,c[2025]  a1 online resource  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  aIncludes index.  a"This book is a definitive, exhaustive resource on blockchain technology and crypto/DeFi that bridges academic theory with real-world applications and coding practices. There is a growing need for a such a book that could be used to teach a course on blockchain. This one offers the flexibility to cater towards different majors, from economics and finance students to more technical ones."--cProvided by publisher.  aDescription based on Bloomberg Law title page, viewed June 30, 2025.0 aCover -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgments -- Chapter 1 Chain Reactions: From Basement Miners to Blockchain Revolutionaries -- Preface -- 1. Introduction -- 2. Blockchain in 16 Questions -- 1. Isn't blockchain just a database like an Excel file? -- 2. What Is a Hash? -- 3. What Is a Block? -- 4. What Is a Merkle Tree? How Is the Merkle Root Calculated? -- 5. Why Are Transactions in Blocks? -- 6. What Is a Node and a P2P Network? -- 7. What Is a Consensus Mechanism? -- 8. How Does Bitcoin Fit into This? -- 9. Who Are Miners? And What Do They Do? -- 10. Why Are They Doing All of This Work? -- 11. Putting All of These Elements Together, How Does PoW Work? -- 12. What Happens If Two Blocks Are Mined at the Same Time? -- 13. What Prevents People from Double Spending Their Digital Currency? -- 14. What Would It Take to Tamper with a PoW Blockchain? -- 15. How Are Transactions Selected to Be Included in a Block? -- 16. What Happens If the Wrong Transaction Is Submitted? -- 3. Where It All Started: Bitcoin -- History of Bitcoin: Early Concepts -- Who Is Satoshi Nakamoto? -- Timeline of Bitcoin -- 2009: Creation -- 2010-2012: Early Days of Bitcoin -- 2013-2016: Bitcoin's Expansion and Turbulence -- 2017-2021: Mainstream Adoption and Market Volatility -- 2022: Regulatory Challenges and Market Corrections -- 2023: Resilience and Institutional Integration -- Is Bitcoin the digital gold? -- Ownership and Identity -- 4. Isn't It Too Slow and Clunky to Be Used for Payments? Enter the Lighting Network -- 5. Energy Consumption -- 6. Concluding Remarks -- Coding Exercises -- Creating Blockchain on Python -- End-of-Chapter Questions -- Multiple-Choice Questions -- Open Questions -- Chapter 2 Ethereum: The "Windows" to the Blockchain Universe-Now Loading Smart Contracts and Oracle Magic -- Preface -- 1. Introduction.8 aHistorical Background -- 2. Ethereum 1.0: Key Concepts -- 3. Ethereum 2.0 -- The Beacon Chain -- The Merge: When Two Become One -- Sharding -- How Sharding Works -- Consensus Algorithm: Proof of Stake -- Staking: The High Roller's Game -- Consensus Mechanism: The Art of Blockchain Politics -- Rewards and Penalties: The Carrot and the Stick -- The Ghost of Inaction: Nothing at Stake Problem -- The Battleground of Network Security -- Economic Incentives -- Unintended Consequences: The Aristocracy of Cryptocurrency -- Ethereum Improvement Proposals (EIPs) -- 4. Burn-and-Mint Model -- Transaction Fees Before EIP-1559 -- The Mechanics of EIP-1559 -- Example of Burn and Mint in EIP-1559 -- Impact of EIP-1559 -- Dynamic Burn-and-Mint -- 5. Ethereum as an Operating System: Smart Contracts -- Development Platforms -- The Birth of Smart Contracts -- 6. Important Components of Smart Contracts: Oracles -- Beyond the Veil of the Blockchain -- The Trust Conundrum -- What can go wrong? -- 7. Latest Developments in Ethereum: Account Abstraction -- What Is Account Abstraction? -- 8. Concluding Remarks -- End-of-Chapter Questions -- Multiple-Choice Questions -- Open Questions -- Blockchain Explorer Project -- Exercise 1: Viewing Blocks -- Exercise 2: Viewing Transactions -- Exercise 3: Viewing Accounts -- Exercise 4: Using the API for Data Analysis -- Chapter 3 Beyond Ethereum: A Gas-Guzzling Escape to the Holy Grail of Scalability -- Preface -- 1. Introduction -- 1.1 Layer 1 Scalability Solutions -- 1.2 Layer 2 Scalability Solutions -- 1.3 Blockchain 3.0 -- 2. Layer 2 Solutions: How Do They Work? -- 2.1 State Channels -- 2.2 Plasma: Scaling Ethereum Through Hierarchical Blockchains -- 2.3 Sidechains -- 2.4 Rollups -- 2.5 Zero-Knowledge Rollups (ZK-Rollups) -- 3. Alternative Blockchains (Ethereum Competitors) -- 3.1 Polygon -- 3.2 Solana -- 3.3 Cardano -- 3.4 Polkadot.8 a4. Cross-chain Interoperability -- 4.1 Bridges -- 4.2 Layer Zero -- 5. Concluding Remarks -- End-of-Chapter Questions -- Multiple-Choice Questions -- Open Questions -- Exercise 1: State Channel Implementation -- Exercise 2: Rollup Contract for Batch Transactions -- Exercise 3: Cross-chain Asset Transfer -- Exercise 4: Plasma Chain Contract -- Exercise 5: Implementing a DAO with Layer 2 Scaling -- Chapter 4 Riding the Crypto Rollercoaster: How Stablecoins Keep Their Cool -- Preface -- 1. Introduction -- 2. Stability Mechanisms -- 2.1 Gold-Backed Stablecoins -- 2.2 Crypto-Backed Stablecoins -- 2.3 Algorithmic Stablecoins -- 2.4 Fiat-Backed Stablecoins -- 3. Concluding remarks -- End-of-Chapter Questions -- Multiple-Choice Questions -- Open Questions / Numerical Exercises -- Coding Exercises -- Exercise 1: Implement a Basic Stablecoin Smart Contract -- Exercise 2: Analyzing Stablecoin Price Stability -- Exercise 3: Simulate a Stablecoin Collateralization Mechanism -- Exercise 4: Smart Contract for Algorithmic Stablecoin -- Exercise 5: Stablecoin Transaction Analysis -- Chapter 5 The CBDC Saga: Rewriting the Rules of Money -- Preface -- 1. Introduction -- 2. How to Design it? -- 2.1 Retail CBDCs: Democratizing Digital Currency Access -- 2.2 Wholesale CBDCs: Streamlining Interbank Operations -- 2.3 Single-Tier or Two-Tier Systems -- 2.4 Account-Based or Token-Based CBDC -- 2.5 Balancing Act: Navigating the Trade-Offs -- 3. Detailed Examples of CBDC Initiatives -- 3.1 Different Approaches and Challenges -- 4. Technical Challenges in Implementing CBDCs -- 5. New Monetary Policy Transmission -- 6. Who Opposes the Introduction of CBDCs? -- 7. Concluding Remarks -- End-of-Chapter Questions -- Multiple-Choice Questions -- Open Questions -- Coding Exercises -- Exercise 1: Creating a CBDC with a Variable Interest Rate (Solidity).8 aExercise 2: Privacy-Preserving CBDC Transactions (Solidity) -- Exercise 3: Implementing a Tiered Interest Rate System for CBDC (Solidity) -- Chapter 6 Money Grows on Distributed Trees: The DeFi Forest of DAOs and DApps -- Preface -- 1. Introduction -- 2. Key Benefits of Making Finance Decentralized -- 3. Overview of DeFi Protocols -- 4. Decentralized Autonomous Organizations (DAOs) -- 4.1 A Little Bit of History: The DAO -- 4.2 How Does a DAO Work? -- 5. Traditional Finance vs. DeFi Lending -- 5.1 Real Examples and Impacts -- 5.2 Utilization Rate and Interest Models -- 6. Case Study: Lending in the Digital Age: The Compound Blockchain Solution/with permission of President &amp -- Fellowsof Harvard College -- 7. AAve -- 7.1 How Flash Loans Work -- 8. Concluding Remarks -- End-of-Chapter Questions -- Multiple-Choice Questions -- Numerical Exercises -- Exercise 1: Understanding Over-Collateralization -- Exercise 2: Flash Loan Arbitrage Opportunity -- Open Questions -- Coding Exercises -- Exercise 1: Deploy a Simple Lending Contract -- Exercise 2: Implement a Borrowing Functionality with Collateral -- Exercise 3: Create a DAO for Lending Protocol Governance -- Chapter 7 The AMM Time Machine: Back to the Future of Finance -- Preface -- 1. Introduction -- Traditional Financial Markets -- 2. The Core of AMM Mechanics -- 2.1 Advantages of AMMs -- 2.2 Market Manipulation and Attacks -- 2.3 Liquidity Providers and Impermanent Loss -- 3. What Is Better: AMMs or Traditional Markets? -- 3.1 Limitations on Price Discovery -- 3.2. Adverse Selection and Liquidity Providers -- 3.3 Which Is Better Then? -- 4. Prominent Protocol: Uniswap -- 4.1 Uniswap V3: Concentrated Liquidity and Innovation -- 4.2 Uniswap V4 -- 4.3 Additional Trading Strategies -- 5. Other Prominent AMMs -- 5.1 Prominent Protocol: Curve Finance -- 5.2 Prominent Protocol: Balancer Overview.8 a6. Concluding Remarks -- End-of-Chapter Questions -- Multiple-Choice Questions -- Open Questions -- Numerical Exercises -- Exercise 1: Calculating Price Impact in an AMM -- Exercise 2: Understanding Impermanent Loss -- Coding Exercises -- Appendix -- Chapter 8 Liquidity Pools: Dive Deep into the Ocean of DeFi (Lifebuoys Not Included) -- Preface -- 1. Introduction -- 1.1 The Bigger Picture Within DeFi -- 1.2 The Advent of Liquidity Provision -- 1.3 The Emergence of Yield Farming -- 2. How to Build a Liquid Market? -- 2.1 Differences and Complementarities in a Nutshell -- 3. The Curve Wars -- 3.1 More Details about the Underlying Economic Incentives -- 3.2 The Bribing System and Its Implications -- 4. Economic Implications and Game Theory -- 4.1 Tokenomics Implications -- 4.2 Renting vs. Owning Liquidity -- 4.3 Sustainability of Rewards -- 4.4 Impact on Token Distribution -- 5. Liquid Staking -- Summary -- 6. Concluding Remarks -- End-of-Chapter Questions -- Multiple-Choice Questions -- Open Questions -- Coding Exercises -- Exercise 1: Liquidity Mining Rewards Distribution Smart Contract -- Exercise 2: Yield Farming Strategy Simulation -- Exercise 3: Liquid Staking Contract with Reward Optimization -- Chapter 9 The Tokenization Transformation from Wall Street to Your Street -- Preface -- 1. Introduction -- 2. How does it work? -- 2.1 Key Benefits -- 2.2 What is the achievable market? -- 2.3 The MakerDAO Transition toward RWA -- 2.4 Art Tokenization Example -- 3. Completing the Security Market Line -- 3.1 Isn't this just a fancy way of reinventing ETFs? -- 3.2 Private Markets -- 4. On-Chain vs. Off-Chain Tokenization -- 4.1 Mortgages -- 4.2 Saving the Environment -- 4.3 Tokenizing IP -- 4.4 Synthetic Assets -- 5. Satoshi's Vision? -- 6. The Path Forward -- End-of-Chapter Questions -- Multiple-Choice Questions -- Open Questions -- Coding Exercises.  dBloomberg Industry Group, Inc. 0aBlockchains (Databases) 0aCryptocurrencies.2 aBloomberg Law,eissuing body.2 aBloomberg Industry Group,ecopyright holder.08iPrint version:aDi Maggio, Marco, 1985-tBlockchain, crypto and DeFidHoboken, New Jersey : Wiley, [2025]z9781394275892w(DLC)  2024035506403Bloomberg Lawuhttps://www.bloomberglaw.com/product/blaw/browser/105.566760  aC0bBNA04108nam a2200493 i 4500001001300000003000600013005001700019006001900036007001500055008004100070010001700111020003500128020003200163020004200195020003900237020004400276020004700320040008500367049000900452050002600461082002800487099001300515100003500528245014700563264004200710300005400752336002600806337002600832338003600858504005100894505055100945520155401496588010403050542003503154650002103189650003903210650001603249655002203265710003403287710004903321776015203370856008003522994001203602on1437537051OCoLC20250630124840.0m     o  d        cr cnu---unuuu250630s2024    njua    ob    001 0 eng d  a  2024020705  a9781394308590qelectronic book  a1394308590qelectronic book  a9781394274840qelectronic book (epub)  a139427484Xqelectronic book (epub)  a1394274858qelectronic book (Adobe PDF)  a9781394274857qelectronic book (Adobe PDF)  aDLCbengerdaepncDLCdOCLCOdYDXdN$TdUKAHLdDG1dCLOUDdSFBdOCLCQdMUUdBNA  aBNAM04aHF5387b.C6666 2024eb00a174/.4223/eng/20240601  aINTERNET1 aCooper, Andrew C. M.,eauthor.14aThe ethical imperative :bleading with conscience to shape the future of business /cAndrew C.M. Cooper ; foreword by Deborah Pollack-Milgate. 1aHoboken, New Jersey :bWiley,c[2024]  a1 online resource (x, 242 pages) :billustrations  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  aIncludes bibliographical references and index.0 aIntroduction: The conflagration -- The burning house. Forgotten towns ; Forgotten people ; Adapt today, thrive tomorrow -- The conscientious executive. Move beyond comfort ; Good habits and the price of renewal ; Dig deep for inspiration ; Value the invisible ; Attenborough's lesson ; Buckner's law -- Level up the leadership power curve. Move 1 : dynamic omnidirectional relationship investment ; Move 2 : human capital investment ; Move 3 : habitat discordance ; Move 4 : the maximization default ; Move 5 : a reliable brand ; Virtuous cycles.  a"The Ethical Imperative challenges business leaders to take an active role in the preservation of today's free market by embracing leadership on wealth inequality, rural economic decay, and climate policy. Leveraging over twenty academic studies spanning more than 50 years, The Ethical Imperative paints a compelling picture of the rising threat that widespread public apathy towards institutions poses to business as we know it. And with engaging, erudite, authentic and personal language, it outlines the moves that matter to avoid the evident catastrophe. The Ethical Imperative strikes a blow against archaic, profit-centric corporate culture that has proved detrimental to not only corporations but also to their own consumers, employees, and surrounding communities. Taking lessons from McKinsey & Company, notable executive leaders and corporate examples, personal stories, and more, millennial and Fortune 500 executive Andrew Cooper gives today's business leaders five strategies to position themselves, their teams, and their companies to thrive in the age of both social media dominance and low trust in corporations. The Ethical Imperative details how the profit-centric philosophy that predominates today's corporate decision-making is actually hurting the very communities needed for businesses to thrive in tomorrow's economy. And, it proffers that a new kind of success is instead based on long-term community wellbeing that results in continual prosperity and transforms business into a more human model."--cProvided by publisher.  aDescription based on online resource; title from digital title page (Bloomberg Law, June 30, 2025).  dBloomberg Industry Group, Inc. 0aBusiness ethics. 0aSocial responsibility of business. 0aLeadership. 0aElectronic books.2 aBloomberg Law,eissuing body.2 aBloomberg Industry Group,ecopyright holder.08iPrint version:aCooper, Andrew C. M.tEthical imperative.dHoboken, New Jersey : Wiley, [2024]z9781394274833w(DLC)  2024020704w(OCoLC)1436910755403Bloomberg Lawuhttps://www.bloomberglaw.com/product/blaw/browser/105.566724  aC0bBNA06954cam a2200433   4500001001300000003000600013005001700019006001900036007001500055008004100070020001800111020001500129040006200144049000900206050002000215082002000235099001300255100004000268245009300308246007200401264004100473300002200514336002600536337002600562338003600588588002600624520258100650505290803231542003506139650005306174650004406227650002206271655002206293700003006315710003406345710004906379856008006428994001206508on1518352741OCoLC20250701110606.0m     o  d        cr un|||||||||250503s2025    nju     o     000 0 eng d  a9781394266999  a1394266995  aUKKRTbengcUKKRTdCLOUDdEBLCPdCLOUDdOCLCOdUKAHLdBNA  aBNAM 4aQ335b.M43 202504a332.6028563223  aINTERNET1 aMedina Ruiz, Hamlet Jesse,eauthor.10aGenerative AI for trading and asset management /cHamlet Jesse Medina Ruiz, Ernest Chan.3 aGenerative artificial intelligence for trading and asset management 1aHoboken, New Jersey :bWiley,c2025.  a1 online resource  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  aPrint Version Record.  aExpert guide on using AI to supercharge traders' productivity, optimize portfolios, and suggest new trading strategies Generative AI for Trading and Asset Management is an essential guide to understand how generative AI has emerged as a transformative force in the realm of asset management, particularly in the context of trading, due to its ability to analyze vast datasets, identify intricate patterns, and suggest complex trading strategies. Practically, this book explains how to utilize various types of AI: unsupervised learning, supervised learning, reinforcement learning, and large language models to suggest new trading strategies, manage risks, optimize trading strategies and portfolios, and generally improve the productivity of algorithmic and discretionary traders alike. These techniques converge into an algorithm to trade on the Federal Reserve chair's press conferences in real time. Written by Hamlet Medina, chief data scientist Criteo, and Ernie Chan, founder of QTS Capital Management and Predictnow.ai, this book explores topics including: How large language models and other machine learning techniques can improve productivity of algorithmic and discretionary traders from ideation, signal generations, backtesting, risk management, to portfolio optimization The pros and cons of tree-based models vs neural networks as they relate to financial applications. How regularization techniques can enhance out of sample performance Comprehensive exploration of the main families of explicit and implicit generative models for modeling high-dimensional data, including their advantages and limitations in model representation and training, sampling quality and speed, and representation learning. Techniques for combining and utilizing generative models to address data scarcity and enhance data augmentation for training ML models in financial applications like market simulations, sentiment analysis, risk management, and more. Application of generative AI models for processing fundamental data to develop trading signals. Exploration of efficient methods for deploying large models into production, highlighting techniques and strategies to enhance inference efficiency, such as model pruning, quantization, and knowledge distillation. Using existing LLMs to translate Federal Reserve Chair's speeches to text and generate trading signals. Generative AI for Trading and Asset Management earns a well-deserved spot on the bookshelves of all asset managers seeking to harness the ever-changing landscape of AI technologies to navigate financial markets.0 aCover -- Half Title Page -- Title Page -- Copyright -- Contents -- Preface -- Acknowledgments -- About the Authors -- Part I: Generative AI for Trading and Asset Management: A No-code Introduction -- Chapter 1: No-code Generative AI for Basic Quantitative Finance -- 1.1 Retrieving Historical Market Data -- 1.2 Computing Sharpe Ratio -- 1.3 Data Formatting and Analysis -- 1.4 Translating Matlab Codes to Python Codes -- 1.5 Conclusion -- Chapter 2: No-code Generative AI for Trading Strategies Development -- 2.1 Creating Codes from a Strategy Specification -- 2.2 Summarizing a Trading Strategy Paper and Creating Backtest Codes from It -- 2.3 Searching for a Portfolio Optimization Algorithm Based on Machine Learning -- 2.4 Explore Options Term Structure Arbitrage Strategies -- 2.5 Conclusion -- 2.6 Exercises -- 2A.1 Computing Next-day's Return -- 2A.2 Uploading the Fama-French Factors -- 2A.3 Combining Fama-French Factors with Next-day's Returns -- Chapter 3: Whirlwind Tour of ML in Asset Management -- 3.1 Unsupervised Learning -- 3.1.1 Hierarchical Risk Parity (HRP) -- 3.1.2 Principal Component Analysis (PCA) -- 3.1.3 Cluster-based Feature Selection (cMDA) -- 3.1.4 Hidden Markov Model (HHM) -- 3.2 Supervised Learning -- 3.2.1 Linear and Logistic Regressions -- 3.2.2 L1 and L2 Regularizations -- 3.2.3 Hyperparameter Optimization, Validation and Cross-validation -- 3.2.4 Performance Metrics -- 3.2.5 Classification and Regression Trees, Random Forest, and Boosted Trees -- 3.2.6 Neural Networks -- 3.2.7 Recurrent Neural Network -- 3.3 Deep Reinforcement Learning -- 3.4 Data Engineering -- 3.4.1 Unique Company Identifiers -- 3.4.2 Dividend and Split Adjustments -- 3.4.3 Survivorship Bias -- 4.4 Look-ahead Bias -- 3.5 Feature Engineering -- 3.5.1 Stationarity -- 3.5.2 Merging Time Series with Different Frequencies -- 3.5.3 Time-series Versus Cross-sectional Features -- 3.5.4 Validating Third-party Features -- 3.5.5 Generative AI as a Feature Generator -- 3.5.6 Features Importance Ranking and Selection -- 3.6 Conclusion -- Part II: Deep Generative Models for Trading and Asset Management -- Chapter 4: Understanding Generative AI -- 4.1 Why Generative Models -- 4.2 Difference with Discriminative Models -- 4.3 How Can We Use Them? -- 4.3.1 Probability Density Estimation -- 4.3.2 Generating New Data -- 4.3.3 Learning New Data Representations -- 4.4 Illustrating Generative Models with ChatGPT -- 4.4.1 Language Modeling -- 4.4.2 Sampling: How Generative Models Create New Data -- 4.4.3 Conditional Language Generation: Asking ChatGPT a Question -- 4.4.4 A Few Words on Representation Learning with ChatGPT -- 4.5 Hybrid Modeling: Combining Generative and Discriminative Models -- 4.6 Taxonomy of Generative Models -- 4.7 Conclusion -- Chapter 5: Deep Autoregressive Models for Sequence Modeling -- 5.1 Representation Complexity -- 5.2 Representation and Complexity Reduction.  dBloomberg Industry Group, Inc. 0aArtificial intelligencexFinancial applications. 0aInvestmentsxTechnological innovations. 0aAsset allocation. 0aElectronic books.1 aChan, Ernest P.,eauthor.2 aBloomberg Law,eissuing body.2 aBloomberg Industry Group,ecopyright holder.403Bloomberg Lawuhttps://www.bloomberglaw.com/product/blaw/browser/105.566762  aC0bBNA01344nai a2200385 i 4500001001300000003000600013005001700019006001900036007001500055008004100070040002300111049000900134099001300143130003600156245002000192246003500212246003300247264005300280300002200333310002500355336002600380337002600406338003600432362002400468500003700492506005100529542003500580588009500615651002700710651004800737710003400785710004900819856007800868994001200946on1526334368OCoLC20250702105516.0m     o  d        cr |||||||||||250702c20259999vaukr w o     0   a2eng    aBNAbengcBNAerda  aBNAM  aINTERNET0 aDelaware brief (Arlington, Va.)10aDelaware brief.1 aBloomberg Law's Delaware brief1 aBloomberg Law Delaware brief 1a[Arlington, Virginia] :bBloomberg Law,c[2025]-  a1 online resource  aContinuously updated  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier1 aBegan in June 2025.  aAt head of title: Bloomberg Law.1 aAccess restricted to subscribing institutions.  dBloomberg Industry Group, Inc.0 aDescription based on online resource title screen (Bloomberg Law, viewed on July 2, 2025). 0aDelawarevPeriodicals. 0aDelawarexLaw and legislationvPeriodicals.2 aBloomberg Law,eissuing body.2 aBloomberg Industry Group,ecopyright holder.40uhttps://www.bloomberglaw.com/product/blaw/bloomberglawnews/delaware-brief  aC0bBNA