Regulation and welfare efficiency: evidence from China

Along with rapid economic growth, China has started to face the challenge of environment degradation. And, the integration of the Chinese economy into global markets has put pressure on the government to address the issue of intellectual property infringement. The government has begun to enforce copyright protection and address environmental issues by imposing regulatory policies on related markets. In this dissertation, I focus on two policies: the anti-piracy campaign enforcing music copyright protection in 2015 and the vehicle license lottery policy started in 2011 in Beijing. I empirically assess the impact of those regulations on market competition, allocative efficiency and consumer welfare. Copyright enforcement in China since 2015 has heightened competition among music streaming services for obtaining exclusive licenses. The competition is driven by the existence of multi-homing and switching costs for consumers in choosing among services. I specify and estimate a structural model that allows consumers to tradeoff between multi-homing and switching. I use estimates to simulate market outcomes had a compulsory licensing provision been enforced. I find that with compulsory licensing, the market will evolve to a ``tipping'' equilibrium in which all users choose to exclusively subscribe to a same service that is of better quality. Although providing more music content, smaller services would lose significant market shares. This is because multi-homing users of smaller services would switch away from their services when the music content were less differentiated from others. The result suggests that a compulsory provision does not benefit the smaller services and may lead to a higher market concentration. In the next chapter, I study a vehicle license lottery in Beijing. Although the static inefficiency from misallocation under a lottery is well-known, I introduce the concept of a dynamic inefficiency due to agents’ suboptimal timing of entering the lottery. Using a structural empirical model, I find that households on average participate in the lottery system at least four years earlier than they would in a counterfactual environment with no quantity constraint. Dynamic inefficiency accounts for the majority of the welfare loss from using the lottery policy. In the last chapter, I formalize the concept of dynamic inefficiency via a simple theoretical model. I show that, with reasonable assumptions, an equilibrium with dynamic misallocation always exits. Consumers with lower willingness to pay for the resource will enter the lottery early in order to increase the chance of winning, although they may receive a negative utility if they win the lottery before their valuation for the resource increases

Nonlinear wave interactions with submerged obstacles with or without current

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Ocean Engineering, 1994.Includes bibliographical references (leaves 209-219).by Yuming Liu.Ph.D

Spline wavelets in ℓ2(Z)

AbstractCompared with the spline wavelet decomposition for the discrete power growth space F given by Pevnyi and Zheludev, this paper deals with spline wavelet decompositions for the Hilbert space ℓ2(Z). We characterize RTB splines and RTB wavelets, because the space ℓ2(Z) can be represented by them. It turns out that the representation is stable and the convergence is much stronger than the pointwise convergence in F. Finally, a family of symmetric RTB wavelets with finite supports are constructed

DIP: Differentiable Interreflection-aware Physics-based Inverse Rendering

We present a physics-based inverse rendering method that learns the illumination, geometry, and materials of a scene from posed multi-view RGB images. To model the illumination of a scene, existing inverse rendering works either completely ignore the indirect illumination or model it by coarse approximations, leading to sub-optimal illumination, geometry, and material prediction of the scene. In this work, we propose a physics-based illumination model that explicitly traces the incoming indirect lights at each surface point based on interreflection, followed by estimating each identified indirect light through an efficient neural network. Furthermore, we utilize the Leibniz's integral rule to resolve non-differentiability in the proposed illumination model caused by one type of environment light -- the tangent lights. As a result, the proposed interreflection-aware illumination model can be learned end-to-end together with geometry and materials estimation. As a side product, our physics-based inverse rendering model also facilitates flexible and realistic material editing as well as relighting. Extensive experiments on both synthetic and real-world datasets demonstrate that the proposed method performs favorably against existing inverse rendering methods on novel view synthesis and inverse rendering

Can Outward Foreign Direct Investment in Producer Services Alleviate Premature Deindustrialization?

Deindustrialization is considered as a necessary path for industrial transformation in developed countries. In recent years, deindustrialization has also occurred in developing countries in the process of industrial transformation. However, many developing countries have started deindustrialization prematurely by transitioning to a service economy without experiencing full industrialization, which can negatively affect economic growth and even fall into a middle-income trap. Firstly, this paper analyzes whether there is premature deindustrialization in China. Secondly, it is argued that producer services outward foreign direct investment can promote the productivity improvement of manufacturing through reverse technology spillover effect and market competition effect. Finally, through the transmission path that producer services outward foreign direct investment leads to the increase of labor productivity of industry, and the increase of labor productivity of industry can alleviate the premature deindustrialization, this paper demonstrates the view that producer services outward foreign direct investment can alleviate the premature deindustrialization in China

Contextual Dictionary Lookup for Knowledge Graph Completion

Knowledge graph completion (KGC) aims to solve the incompleteness of knowledge graphs (KGs) by predicting missing links from known triples, numbers of knowledge graph embedding (KGE) models have been proposed to perform KGC by learning embeddings. Nevertheless, most existing embedding models map each relation into a unique vector, overlooking the specific fine-grained semantics of them under different entities. Additionally, the few available fine-grained semantic models rely on clustering algorithms, resulting in limited performance and applicability due to the cumbersome two-stage training process. In this paper, we present a novel method utilizing contextual dictionary lookup, enabling conventional embedding models to learn fine-grained semantics of relations in an end-to-end manner. More specifically, we represent each relation using a dictionary that contains multiple latent semantics. The composition of a given entity and the dictionary's central semantics serves as the context for generating a lookup, thus determining the fine-grained semantics of the relation adaptively. The proposed loss function optimizes both the central and fine-grained semantics simultaneously to ensure their semantic consistency. Besides, we introduce two metrics to assess the validity and accuracy of the dictionary lookup operation. We extend several KGE models with the method, resulting in substantial performance improvements on widely-used benchmark datasets

Study of imbibition in various geometries using phase field method

Phase field method has been widely utilized to study multiphase flow problems, but has seldom been applied to the study of imbibition. Previous methods used to simulate imbibition, such as moving mesh method, need to specify capillary pressure as a boundary condition a priori, whereas phase field method can calculate capillary pressure automatically for various geometries. Therefore, phase field method would be a versatile tool for the study of imbibition in various geometries. In this paper, phase field method is employed to solve dynamical imbibition problem in various geometries, including straight tube, conical tube and structures in which the topology changes. The variation of the imbibition height with respect to time from phase field simulation is verified with theoretical predictions from Lucas-Washburn law in a straight capillary tube with three gravitational scenarios. In addition, the capillary pressure and velocity field are found to be consistent with Laplace-Young equation and Hagen-Poiseuille equation in various geometries. The applicability and accuracy of the phase field method for the study of imbibition in structures with changing topology are also discussed.Cited as: Xiao, J., Luo, Y., Niu, M., Wang, Q., Wu, J., Liu, X., Xu, J. Study of imbibition in various geometries using phase field method. Capillarity, 2019, 2(4): 57-65, doi: 10.26804/capi.2019.04.0

Privacy Computing Meets Metaverse: Necessity, Taxonomy and Challenges

Metaverse, the core of the next-generation Internet, is a computer-generated holographic digital environment that simultaneously combines spatio-temporal, immersive, real-time, sustainable, interoperable, and data-sensitive characteristics. It cleverly blends the virtual and real worlds, allowing users to create, communicate, and transact in virtual form. With the rapid development of emerging technologies including augmented reality, virtual reality and blockchain, the metaverse system is becoming more and more sophisticated and widely used in various fields such as social, tourism, industry and economy. However, the high level of interaction with the real world also means a huge risk of privacy leakage both for individuals and enterprises, which has hindered the wide deployment of metaverse. Then, it is inevitable to apply privacy computing techniques in the framework of metaverse, which is a current research hotspot. In this paper, we conduct comprehensive research on the necessity, taxonomy and challenges when privacy computing meets metaverse. Specifically, we first introduce the underlying technologies and various applications of metaverse, on which we analyze the challenges of data usage in metaverse, especially data privacy. Next, we review and summarize state-of-the-art solutions based on federated learning, differential privacy, homomorphic encryption, and zero-knowledge proofs for different privacy problems in metaverse. Finally, we show the current security and privacy challenges in the development of metaverse and provide open directions for building a well-established privacy-preserving metaverse system. For easy access and reference, we integrate the related publications and their codes into a GitHub repository: https://github.com/6lyc/Awesome-Privacy-Computing-in-Metaverse.git.Comment: In Ad Hoc Networks (2024