A Behavioral Theory of Leviathan Inc:State Firms’ Responses to Performance Shortfalls

We examine the role of ownership in organizational responses to performance shortfalls. State owners prize performance stability over being competitive, and their firms thus frame performance shortfalls differently than private firms do. In particular, state firms’ performance-stability orientation makes them respond to small performance shortfalls more readily than private firms do. They largely disregard performance comparisons with industry competitors, which more readily trigger divestitures at private firms. Comparisons among state firms matter relatively more. We find empirical support for these propositions in the population of Chinese state and private firms publicly traded in Shanghai and Shenzhen Stock Exchanges from 2003 to 2019. The frame-contingent responsiveness we document extends theory of organizational behavior and adds important nuance to our understanding of state-firm inertia

The Co-creation and Circulation of Brands and Cultures: Historical Chinese Culture, Global Fashion Systems, and the Development of Chinese Global Brands

This dissertation is a study of the possibilities and processes of constructing strong Chinese brands in the global marketplace. It investigates conceptual and strategic relationships between brands and cultures, focusing specifically on the issue of the unprivileged position of Chinese brands vis-à-vis that of other famous global counterparts. Accordingly, it deploys three illustrative cases from the Chinese context – Jay Chou (a successful Chinese music artist), the 2008 Beijing Olympics opening ceremony, and Shanghai Tang (a global Chinese fashion brand). In so doing, it moves away from the general trend to study the managerial aspects of Western brand building in Chinese contexts, and instead examines how Chinese brands express cultural aspects of their own well-known brand development models in the global marketplace. In short, this study uses a Chinese vantage to examine the emergence of cultural branding (using historical culture and global fashion systems to develop global brands), and its capacity to function as a useful complement to existing models of brand globalisation and global brand culture. The function of the three cases is illustrative and analytic. Collectively, they serve as a lens through which to study Chinese brand development in the global marketplace and examine global brand culture. Each case was fleshed out through various multi-sited ethnographic studies, which consisted of interviewing and observing consumers and managerial workers, the results of which shed light on several important but under-studied aspects of global brand culture. These include Chinese cultural branding in the global context, the cultural approach to branding among various brand actors, and relationships between brands and cultures across branding cultures. Drawing on these examinations, this study not only demonstrates ways in which brands and cultures circulate and construct each other in global brand culture. It also uses these insights to argue for the development of Chinese culture or Chinese-ness into a global brand resource by Chinese brand builders

In-situ Model Downloading to Realize Versatile Edge AI in 6G Mobile Networks

The sixth-generation (6G) mobile networks are expected to feature the ubiquitous deployment of machine learning and AI algorithms at the network edge. With rapid advancements in edge AI, the time has come to realize intelligence downloading onto edge devices (e.g., smartphones and sensors). To materialize this version, we propose a novel technology in this article, called in-situ model downloading, that aims to achieve transparent and real-time replacement of on-device AI models by downloading from an AI library in the network. Its distinctive feature is the adaptation of downloading to time-varying situations (e.g., application, location, and time), devices' heterogeneous storage-and-computing capacities, and channel states. A key component of the presented framework is a set of techniques that dynamically compress a downloaded model at the depth-level, parameter-level, or bit-level to support adaptive model downloading. We further propose a virtualized 6G network architecture customized for deploying in-situ model downloading with the key feature of a three-tier (edge, local, and central) AI library. Furthermore, experiments are conducted to quantify 6G connectivity requirements and research opportunities pertaining to the proposed technology are discussed.Comment: The paper has been submitted to IEEE for possible publicatio

Intelligent Detection of Road Cracks Based on Improved YOLOv5

With the gradual increase of highway coverage, the frequency of road cracks also increases, which brings a series of security risks. It is necessary to detect road cracks, but the traditional detection method is inefficient and unsafe. In this paper, deep learning is used to detect road cracks, and an improved model BiTrans-YOLOv5 is proposed. We add Swin Transformer to YOLOv5s to replace the original C3 module, and explore the performance of Transformer in the field of road crack detection. We also change the original PANet of YOLOv5s into a bidirectional feature pyramid network (BIFPN), which can detect small targets more accurately. Experiments on the data set Road Damage show that BiTrans-YOLOv5 has improved in Precision, Recall, F1 score and [email protected] compared with YOLOv5s, among which [email protected] has improved by 5.4%. It is proved that BiTrans-YOLOv5 has better performance in road detection projects

VDIP-TGV: Blind Image Deconvolution via Variational Deep Image Prior Empowered by Total Generalized Variation

Recovering clear images from blurry ones with an unknown blur kernel is a challenging problem. Deep image prior (DIP) proposes to use the deep network as a regularizer for a single image rather than as a supervised model, which achieves encouraging results in the nonblind deblurring problem. However, since the relationship between images and the network architectures is unclear, it is hard to find a suitable architecture to provide sufficient constraints on the estimated blur kernels and clean images. Also, DIP uses the sparse maximum a posteriori (MAP), which is insufficient to enforce the selection of the recovery image. Recently, variational deep image prior (VDIP) was proposed to impose constraints on both blur kernels and recovery images and take the standard deviation of the image into account during the optimization process by the variational principle. However, we empirically find that VDIP struggles with processing image details and tends to generate suboptimal results when the blur kernel is large. Therefore, we combine total generalized variational (TGV) regularization with VDIP in this paper to overcome these shortcomings of VDIP. TGV is a flexible regularization that utilizes the characteristics of partial derivatives of varying orders to regularize images at different scales, reducing oil painting artifacts while maintaining sharp edges. The proposed VDIP-TGV effectively recovers image edges and details by supplementing extra gradient information through TGV. Additionally, this model is solved by the alternating direction method of multipliers (ADMM), which effectively combines traditional algorithms and deep learning methods. Experiments show that our proposed VDIP-TGV surpasses various state-of-the-art models quantitatively and qualitatively.Comment: 13 pages, 5 figure

Guilotes, a new genus of Coelotinae spiders from Guangxi Zhuang Autonomous Region, China (Araneae, Agelenidae)

A new genus of the subfamily Coelotinae F.O. Pickard-Cambridge, 1893, Guilotes Z. Zhao & S. Li, gen. n. from China is described, as well as four new species: G. ludiensis Z. Zhao & S. Li, sp. n. (♂♀, type species), G. qingshitanensis Z. Zhao & S. Li, sp. n. (♂♀), G. xingpingensis Z. Zhao & S. Li, sp. n. (♂♀) and G. yandongensis Z. Zhao & S. Li, sp. n. (♀). The DNA barcodes of all species are documented for future use