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This chapter delves into the future of the complex and evolving world of non-fungible tokens (NFTs), focusing on their potential to drive mass adoption of blockchain technology. Beginning with some historical context, the chapter explores the rapid growth of NFTs in the digital art and collectibles space, most notably during the speculative boom in 2021–2022 and the subsequent crash. The chapter then investigates how NFTs might expand beyond these initial use cases. It describes major developments in technology, business models, and financial infrastructure that will support further evolution of NFTs. Using real-world examples, the chapter then discusses emerging categories of NFT use cases, such as tokenization of physical assets, ticketing, and digital identity. It concludes by emphasizing that the true mass adoption of NFTs will occur when the technology becomes invisible and the primary draw becomes the value of use cases, not the novelty of NFTs themselves. While one should be skeptical about specific predictions for massive NFT adoption, this chapter shows that the capabilities NFTs provide are poised to add value in a wide variety of contexts.
Copyright law safeguards the exclusive rights of authors to their intellectual creations, emphasizing reproduction, public display, and adaptation. A fundamental distinction within this realm is between the intangible creative work and its tangible representations. Owning a tangible embodiment (like a painting) does not grant rights to reproduce the intellectual work it embodies. This demarcation is critical in the dynamic landscape of non-fungible tokens (NFTs), as acquiring an NFT does not automatically confer rights to the associated work. Instead, rights hinge on explicit contractual terms accompanying the NFT transaction. As the world of NFTs continues to unfold in all sorts of directions, delving deep into the intricacies of copyright law is important for artists, investors, and legal practitioners navigating the digital frontier. This chapter offers insights into the various copyright implications associated with NFTs.
In an era where two-fifths of the global population is engaged in gaming, this industry’s technological and economic evolution is of paramount importance, promising continued growth. Beyond mere entertainment, gaming has become a primary medium for social interaction, enriched by technologies like virtual, augmented, and extended reality. Gaming has increasingly become intertwined with the financial market as game developers shift their focus from gameplay enjoyment to monetization of in-game assets and some players prioritize the potential for livelihood in gaming. This transformation has been accelerated by the integration of blockchain, decentralized finance (DeFi) protocols, and non-fungible tokens (NFTs), which provide users with more control over their in-game assets and enable external trading of such assets in the secondary market. This chapter delves into this integration, examines its impact on the gaming industry, and provides a high-level overview of key related legal and ethical issues that warrant further exploration.
As blockchains in general and NFTs in particular reshape operation logistics, data creation, and data management, these technologies bring forth many legal and ethical dilemmas. This handbook offers a comprehensive exploration of the impact of these technologies in different industries and sectors, including finance, anti-money laundering, taxation, campaign-finance, and more. The book specifically provides insights and potential solutions for cutting-edge issues related to intellectual property rights, data privacy and strategy, information management, and ethical blockchain use, simultaneously presenting insights, case studies, and recommendations to help anyone seeking to shape effective, balanced regulation to foster innovation while safeguarding the interests of all stakeholders. This handbook sets out an invaluable roadmap for navigating the dynamic and evolving landscape of these new technologies.
The chapter explores the profound implications of non-fungible tokens (NFTs) within the context of the Web 3.0 movement and the burgeoning metaverse landscape. While NFTs have already found some economic traction in analog settings, their potential is most transformative in a purely digital realm. NFTs offer unparalleled provenance and tradability for digital assets, circumventing centralized intermediaries. The metamorphosis of NFTs and their role in the emergence of the metaverse will determine their full impact. As metaverse platforms evolve, enabled by NFT interoperability and consumer trust, they are poised to reshape the leisure economy and extend into education and employment. The true value of NFTs lies in their integration into an interconnected, dynamic virtual world revolutionizing various facets of society. While existing regulations provide a framework, they will inevitably adapt if and as the metaverse gains prominence, necessitating agile regulatory responses to this transformative landscape.
"Recent years have witnessed the rise of non-fungible tokens (NFTs) as vehicles for non-investment finance, including in nonprofit and political fundraising. As with other financial sectors in which NFTs have a role, the use of NFTs in financing nonprofits and political campaigns and committees has revealed gaps and ambiguities in existing legal regulatory systems. Appetite exists to evolve legal frameworks to complete and clarify applicable bodies of law and regulation.
Blockchain-based fundraising transforms the way issuers raise capital from the public, promising to reduce transaction costs, expand financial access, and reshape issuer-investor interactions. Despite these promises, the blockchain finance market is currently plagued by severe asymmetric information and is rife with fraudulent and low-quality issuers who exploit this friction. This chapter explores the reasons for the severe asymmetric information in this market and discusses the extent to which signaling and analysts can address it. It suggests that the effectiveness of signaling is limited due to the low costs of producing and disseminating signals and investors' inability to verify biased signals ex ante and punish biased signals ex post. These limitations make analysts a vital source for reducing asymmetric information but they, too, appear to suffer from significant problems – ranging from conflicts of interest to lack of transparency to low competence and expertise – which hinder their effectiveness in reducing asymmetric information. The chapter concludes with the policy implications arising from these observations, which can also guide policy-makers in addressing emerging blockchain-based fundraising mechanisms, such as non-fungible token (NFT) offerings.
The digital asset landscape is rapidly evolving, despite recent volatility exemplified by the collapse of FTX in 2022. However, the taxation of cryptocurrencies remains a contentious topic, raising questions about how these financial instruments should be taxed. While the IRS has not signaled any immediate changes to the tax code, arguments persist for new tax specifics. This chapter presents the case for integrating fresh tax regulations into the code, catering to both academics and practitioners. Exploring the complexities of taxing cryptocurrencies, it considers factors such as classifying tax liabilities for various digital assets and understanding the implications of crypto transactions on taxable events, and delves into the challenges faced by tax authorities in monitoring decentralized and pseudonymous cryptocurrency transactions. With a focus on bridging theory and practice, the chapter offers practical insights for implementing effective taxation policies for digital assets. It aims to guide policy-makers and taxpayers in navigating the dynamic cryptocurrency landscape. Additionally, it advocates for an updated tax code that aligns with the evolving nature of the digital asset ecosystem. By providing a comprehensive economic rationale, it contributes to ongoing discussions on cryptocurrency taxation, fostering an efficient and equitable tax framework tailored for NFTs and digital assets.
For better or worse, non-fungible tokens (NFTs) are the most peculiar and least expected art market innovations of the early twenty-first century. This chapter provides a brief history of NFTs and the NFT market, beginning with the invention of blockchain technology, through the creation of the Bitcoin, Namecoin, and Ethereum blockchains, and the NFT phenomenon. It describes a selection of NFT projects and artists and provides a theoretical account of both the art market and the NFT market.
Fueled in part by the wealth created from digital currencies, major art dealers such as Christie’s and Sotheby’s have embraced the sale of non-fungible tokens (NFTs) attached to unique digital works of art. NFTs, how they are related to the blockchain, and the evolution of the market for digital art is the subject of this chapter. Despite recent decreases in value, it appears that digital art can be added to the growing list of uses for blockchain technology, which is now becoming a part of modern life. This chapter proceeds in five sections. First, the overview of the evolutionary progression of blockchain technology in the form of NFTs. Second, a description of the emergence of the market for digital art. Third, an explanation and historical account of digital art and related recent issues. Fourth, a coverage of the abrupt decline in the market price for many NFTs. And last, a conclusion, which focuses on how the dramatic extension of blockchain and other digital technology to the world of art represents a new and exciting platform for creative expression. This chapter offers a valuable addition to the literature by providing a readable introduction and overview of what is now known about the likely impact of blockchain technology and NFTs to art. Additionally, this important development should have a significant impact on the future of innovation and property law.
The tension distribution problem of cable-driven parallel robots is inevitable in real-time control. Currently, iterative algorithms or geometric algorithms are commonly used to solve this problem. Iterative algorithms are difficult to improve in real-time performance, and the tension obtained by geometric algorithms may not be continuous. In this paper, a novel tension distribution method for four-cable, 3-DOF cable-driven parallel robots is proposed based on the wave equation. The tension calculated by this method is continuous and differentiable, without the need for iterative computation or geometric centroid calculations, thus exhibiting good real-time performance. Furthermore, the feasibility and rationality of this algorithm are theoretically proven. Finally, the real-time performance and continuity of cable tension are analyzed through a specific numerical example.
This study proposes a novel hybrid learning approach for developing a visual path-following algorithm for industrial robots. The process involves three steps: data collection from a simulation environment, network training, and testing on a real robot. The actor network is trained using supervised learning for 500 epochs. A semitrained network is then obtained at the $250^{th}$ epoch. This network is further trained for another 250 epochs using reinforcement learning methods within the simulation environment. Networks trained with supervised learning (500 epochs) and the proposed hybrid learning method (250 epochs each of supervised and reinforcement learning) are compared. The hybrid learning approach achieves a significantly lower average error (30.9 mm) compared with supervised learning (39.3 mm) on real-world images. Additionally, the hybrid approach exhibits faster processing times (31.7 s) compared with supervised learning (35.0 s). The proposed method is implemented on a KUKA Agilus KR6 R900 six-axis robot, demonstrating its effectiveness. Furthermore, the hybrid approach reduces the total power consumption of the robot’s motors compared with the supervised learning method. These results suggest that the hybrid learning approach offers a more effective and efficient solution for visual path following in industrial robots compared with traditional supervised learning.
Given an $n\times n$ symmetric matrix $W\in [0,1]^{[n]\times [n]}$, let ${\mathcal G}(n,W)$ be the random graph obtained by independently including each edge $jk\in \binom{[n]}{2}$ with probability $W_{jk}=W_{kj}$. Given a degree sequence $\textbf{d}=(d_1,\ldots, d_n)$, let ${\mathcal G}(n,\textbf{d})$ denote a uniformly random graph with degree sequence $\textbf{d}$. We couple ${\mathcal G}(n,W)$ and ${\mathcal G}(n,\textbf{d})$ together so that asymptotically almost surely ${\mathcal G}(n,W)$ is a subgraph of ${\mathcal G}(n,\textbf{d})$, where $W$ is some function of $\textbf{d}$. Let $\Delta (\textbf{d})$ denote the maximum degree in $\textbf{d}$. Our coupling result is optimal when $\Delta (\textbf{d})^2\ll \|\textbf{d}\|_1$, that is, $W_{ij}$ is asymptotic to ${\mathbb P}(ij\in{\mathcal G}(n,\textbf{d}))$ for every $i,j\in [n]$. We also have coupling results for $\textbf{d}$ that are not constrained by the condition $\Delta (\textbf{d})^2\ll \|\textbf{d}\|_1$. For such $\textbf{d}$ our coupling result is still close to optimal, in the sense that $W_{ij}$ is asymptotic to ${\mathbb P}(ij\in{\mathcal G}(n,\textbf{d}))$ for most pairs $ij\in \binom{[n]}{2}$.
Accurate dynamic model is essential for the model-based control of robotic systems. However, on the one hand, the nonlinearity of the friction is seldom treated in robot dynamics. On the other hand, few of the previous studies reasonably balance the calculation time-consuming and the quality for the excitation trajectory optimization. To address these challenges, this article gives a Lie-theory-based dynamic modeling scheme of multi-degree-of-freedom (DoF) serial robots involving nonlinear friction and excitation trajectory optimization. First, we introduce two coefficients to describe the Stribeck characteristics of Coulomb and static friction and consider the dependency of friction on load torque, so as to propose an improved Stribeck friction model. Whereafter, the improved friction model is simplified in a no-load scenario, a novel nonlinear dynamic model is linearized to capture the features of viscous friction across the entire velocity range. Additionally, a new optimization algorithm of excitation trajectories is presented considering the benefits of three different optimization criteria to design the optimal excitation trajectory. On the basis of the above, we retrieve a feasible dynamic parameter set of serial robots through the hybrid least square algorithm. Finally, our research is supported by simulation and experimental analyses of different combinations on the seven-DoF Franka Emika robot. The results show that the proposed friction has better accuracy performance, and the modified optimization algorithm can reduce the overall time required for the optimization process while maintaining the quality of the identification results.
This paper introduces a lower limb exoskeleton for gait rehabilitation, which has been designed to be adjustable to a wide range of patients by incorporating an extension mechanism and series elastic actuators (SEAs). This configuration adapts better to the user’s anatomy and the natural movements of the user’s joints. However, the inclusion of SEAs increases actuator mass and size, while also introducing nonlinearities and changes in the dynamic response of the exoskeletons. To address the challenges related to the human–exoskeleton dynamic interaction, a nonsingular terminal sliding mode control that integrates an adaptive parameter adjustment strategy is proposed, offering a practical solution for trajectory tracking with uncertain exoskeleton dynamics. Simulation results demonstrate the algorithm’s ability to estimate unknown parameters. Experimental tests analyze the performance of the controller against uncertainties and external disturbances.