Evolving joint ventures:: A study of Dalian-based joint ventures in the transitional Chinese economy

This thesis attempts to explain the evolution and survival of joint ventures in the transitional Chinese economy. Since China 'opened its doors' to foreign direct investment in 1979, the inflows of capital, technology, and management skills via joint ventures have contributed significantly to its economic development. Joint ventures were unquestionably regarded as the most feasible and also the only possible strategy for legitimate foreign direct investment. However, evidence is presented showing that in the 1990s, in line with China's ongoing economic reform, significant changes took place in the country's institutional and economic environment. Hence, the changes in its business environment had a strong impact on China's foreign direct investment. The findings of this study show that China's transitional economy has had a significant impact on the relaxation of state control over foreign direct investment and has seriously affected joint venture partners' initial power relationships and interdependency. The new power relationships between venture partners have driven dramatic changes in joint venture partners' strategies, ownership structure, and operational management. As a result, this study further indicate that the changes in China's business environment, although not the only dominant factor, have exacerbated joint ventures' internal difficulties and further affected joint venture partners' strategic choices, which in turn has led to a rapid evolution of China-based joint ventures. This study contributes to both the theoretical and practical understanding of the evolution of China-based joint ventures as they have adapted under China's economic transition and indicates an additional exit way out to a joint venture's eventual destination, rather than termination or dissolution. This study also offers areas for future research

Bi-Directional Generation for Unsupervised Domain Adaptation

Unsupervised domain adaptation facilitates the unlabeled target domain relying on well-established source domain information. The conventional methods forcefully reducing the domain discrepancy in the latent space will result in the destruction of intrinsic data structure. To balance the mitigation of domain gap and the preservation of the inherent structure, we propose a Bi-Directional Generation domain adaptation model with consistent classifiers interpolating two intermediate domains to bridge source and target domains. Specifically, two cross-domain generators are employed to synthesize one domain conditioned on the other. The performance of our proposed method can be further enhanced by the consistent classifiers and the cross-domain alignment constraints. We also design two classifiers which are jointly optimized to maximize the consistency on target sample prediction. Extensive experiments verify that our proposed model outperforms the state-of-the-art on standard cross domain visual benchmarks.Comment: 9 pages, 4 figure

An overview on nonlinear porous flow in low permeability porous media

AbstractThis paper gives an overview on nonlinear porous flow in low permeability porous media, reveals the microscopic mechanisms of flows, and clarifies properties of porous flow fluids. It shows that, deviating from Darcy's linear law, the porous flow characteristics obey a nonlinear law in a low-permeability porous medium, and the viscosity of the porous flow fluid and the permeability values of water and oil are not constants. Based on these characters, a new porous flow model, which can better describe low permeability reservoir, is established. This model can describe various patterns of porous flow, as Darcy's linear law does. All the parameters involved in the model, having definite physical meanings, can be obtained directly from the experiments

Create Your World: Lifelong Text-to-Image Diffusion

Text-to-image generative models can produce diverse high-quality images of concepts with a text prompt, which have demonstrated excellent ability in image generation, image translation, etc. We in this work study the problem of synthesizing instantiations of a use's own concepts in a never-ending manner, i.e., create your world, where the new concepts from user are quickly learned with a few examples. To achieve this goal, we propose a Lifelong text-to-image Diffusion Model (L2DM), which intends to overcome knowledge "catastrophic forgetting" for the past encountered concepts, and semantic "catastrophic neglecting" for one or more concepts in the text prompt. In respect of knowledge "catastrophic forgetting", our L2DM framework devises a task-aware memory enhancement module and a elastic-concept distillation module, which could respectively safeguard the knowledge of both prior concepts and each past personalized concept. When generating images with a user text prompt, the solution to semantic "catastrophic neglecting" is that a concept attention artist module can alleviate the semantic neglecting from concept aspect, and an orthogonal attention module can reduce the semantic binding from attribute aspect. To the end, our model can generate more faithful image across a range of continual text prompts in terms of both qualitative and quantitative metrics, when comparing with the related state-of-the-art models. The code will be released at https://wenqiliang.github.io/.Comment: 15 pages,10 figure

Digital Twin-Enhanced Deep Reinforcement Learning for Resource Management in Networks Slicing

Network slicing-based communication systems can dynamically and efficiently allocate resources for diversified services. However, due to the limitation of the network interface on channel access and the complexity of the resource allocation, it is challenging to achieve an acceptable solution in the practical system without precise prior knowledge of the dynamics probability model of the service requests. Existing work attempts to solve this problem using deep reinforcement learning (DRL), however, such methods usually require a lot of interaction with the real environment in order to achieve good results. In this paper, a framework consisting of a digital twin and reinforcement learning agents is present to handle the issue. Specifically, we propose to use the historical data and the neural networks to build a digital twin model to simulate the state variation law of the real environment. Then, we use the data generated by the network slicing environment to calibrate the digital twin so that it is in sync with the real environment. Finally, DRL for slice optimization optimizes its own performance in this virtual pre-verification environment. We conducted an exhaustive verification of the proposed digital twin framework to confirm its scalability. Specifically, we propose to use loss landscapes to visualize the generalization of DRL solutions. We explore a distillation-based optimization scheme for lightweight slicing strategies. In addition, we also extend the framework to offline reinforcement learning, where solutions can be used to obtain intelligent decisions based solely on historical data. Numerical simulation experiments show that the proposed digital twin can significantly improve the performance of the slice optimization strategy

Experimental study on spontaneous imbibition characteristics of tight rocks

 In the exploitation of tight oil and gas reservoirs, multi-stage hydraulic fracturing technology is mainly used and a complex system of fractures and matrix is formed after fracturing. In the process of field production, it is reported that longer shut-in time results in good oil and gas production rate. The reason of this phenomenon is considered as the spontaneous imbibition of oil and gas driven by capillary force in reservoirs. Spontaneous imbibition is an important recovery mechanism in low permeability and tight reservoirs. The pore structure of tight rocks is very complex and the pore connectivity is poor. It is of great significance to study the imbibition mechanism of tight porous rocks. Through the combination of spontaneous imbibition experiments, this work studies the influencing factors and reveals the mechanism of the gas/oil recovery from tight reservoirs. The spontaneous imbibition experiments were carried on the gas/water system and the oil/water system. The swelling clay minerals in shales will enhance the imbibition. Cores with high permeability have small recovery, which may be due to the low capillary force in tight cores. Fractures can promote the imbibition volume of tight cores.Cited as: Gao, L., Yang, Z., Shi, Y. Experimental study on spontaneous imbibition characteristics of tight rocks. Advances in Geo-Energy Research, 2018, 2(3): 292-304, doi: 10.26804/ager.2018.03.0