Determining factors of the development of a national financial center: The case of China

This study explores, theoretically and empirically, one of the important issues of the geography of finance, namely the location of high-level financial services. Specifically, we will try to explain why foreign financial services are spatially concentrated in a particular city so as to form a national financial center in China. By reviewing various forces behind the formation of a financial center, we argue that information problems have created the need for geographic agglomeration of financial activities based on the source of information. This is true even in an era when financial markets work through sophisticated telecommunication networks. Based on a survey of the actual location of multinational corporation (MNC) regional headquarters, and through investigation of reasons for the agglomeration of these headquarters, we anticipate that Beijing, as the prime source of policy information, is more likely than other Chinese cities to be the national pre-eminent financial center when the Chinese financial markets become more open to foreign firms in the near future. This study illustrates, using China as a case study, that geography still provides strong justification of why major financial services continue to have a high degree of spatial agglomeration in particular locations, despite the fact that the electronic transmission of information has substantially reduced the friction of distance. © 2004 Elsevier Ltd. All rights reserved.postprin

Optimal Data Scheduling and Admission Control for Backscatter Sensor Networks

© 2017 IEEE. This paper studies the data scheduling and admission control problem for a backscatter sensor network (BSN). In the network, instead of initiating their own transmissions, the sensors can send their data to the gateway just by switching their antenna impedance and reflecting the received RF signals. As such, we can reduce remarkably the complexity, the power consumption, and the implementation cost of sensor nodes. Different sensors may have different functions, and data collected from each sensor may also have a different status, e.g., urgent or normal, and thus we need to take these factors into account. Therefore, in this paper, we first introduce a system model together with a mechanism in order to address the data collection and scheduling problem in the BSN. We then propose an optimization solution using the Markov decision process framework and a reinforcement learning algorithm based on the linear function approximation method, with the aim of finding the optimal data collection policy for the gateway. Through simulation results, we not only show the efficiency of the proposed solution compared with other baseline policies, but also present the analysis for data admission control policy under different classes of sensors as well as different types of data

Optimal Cross Slice Orchestration for 5G Mobile Services

© 2018 IEEE. 5G mobile networks encompass the capabilities of hosting a variety of services such as mobile social networks, multimedia delivery, healthcare, transportation, and public safety. Therefore, the major challenge in designing the 5G networks is how to support different types of users and applications with different quality-of-service requirements under a single physical network infrastructure. Recently, network slicing has been introduced as a promising solution to address this challenge. Network slicing allows programmable network instances which match the service requirements by using network virtualization technologies. However, how to efficiently allocate resources across network slices has not been well studied in the literature. Therefore, in this paper, we first introduce a model for orchestrating network slices based on the service requirements and available resources. Then, we propose a Markov decision process framework to formulate and determine the optimal policy that manages cross-slice admission control and resource allocation for the 5G networks. Through simulation results, we show that the proposed solution is efficient not only in providing slice-as-a-service based on service requirements, but also in maximizing the provider's revenue

Centralizers in semisimple algebras, and descent spectrum in Banach algebras

AbstractWe prove that semisimple algebras containing some algebraic element whose centralizer is semiperfect are artinian. As a consequence, semisimple complex Banach algebras containing some element whose centralizer is algebraic are finite-dimensional. This answers affirmatively a question raised in Burgos et al. (2006) [4], and is applied to show that an element a in a semisimple complex Banach algebra A does not perturb the descent spectrum of every element commuting with a if and only if some of power of a lies in the socle of A. This becomes a Banach algebra version of a theorem in Burgos et al. (2006) [4], Kaashoek and Lay (1972) [9] for bounded linear operators on complex Banach spaces

A dynamic edge caching framework for mobile 5G networks

© 2002-2012 IEEE. Mobile edge caching has emerged as a new paradigm to provide computing, networking resources, and storage for a variety of mobile applications. That helps achieve low latency, high reliability, and improve efficiency in handling a very large number of smart devices and emerging services (e.g., IoT, industry automation, virtual reality) in mobile 5G networks. Nonetheless, the development of mobile edge caching is challenged by the decentralized nature of edge nodes, their small coverage, limited computing, and storage resources. In this article, we first give an overview of mobile edge caching in 5G networks. After that, its key challenges and current approaches are discussed. We then propose a novel caching framework. Our framework allows an edge node to authorize the legitimate users and dynamically predicts and updates their content demands using the matrix factorization technique. Based on the prediction, the edge node can adopt advanced optimization methods to determine optimal content to store so as to maximize its revenue and minimize the average delay of its mobile users. Through numerical results, we demonstrate that our proposed framework provides not only an effective caching approach, but also an efficient economic solution for the mobile service provider

A Survey on Consensus Mechanisms and Mining Strategy Management in Blockchain Networks

© 2013 IEEE. The past decade has witnessed the rapid evolution in blockchain technologies, which has attracted tremendous interests from both the research communities and industries. The blockchain network was originated from the Internet financial sector as a decentralized, immutable ledger system for transactional data ordering. Nowadays, it is envisioned as a powerful backbone/framework for decentralized data processing and data-driven self-organization in flat, open-access networks. In particular, the plausible characteristics of decentralization, immutability, and self-organization are primarily owing to the unique decentralized consensus mechanisms introduced by blockchain networks. This survey is motivated by the lack of a comprehensive literature review on the development of decentralized consensus mechanisms in blockchain networks. In this paper, we provide a systematic vision of the organization of blockchain networks. By emphasizing the unique characteristics of decentralized consensus in blockchain networks, our in-depth review of the state-of-the-art consensus protocols is focused on both the perspective of distributed consensus system design and the perspective of incentive mechanism design. From a game-theoretic point of view, we also provide a thorough review of the strategy adopted for self-organization by the individual nodes in the blockchain backbone networks. Consequently, we provide a comprehensive survey of the emerging applications of blockchain networks in a broad area of telecommunication. We highlight our special interest in how the consensus mechanisms impact these applications. Finally, we discuss several open issues in the protocol design for blockchain consensus and the related potential research directions

Reinforcement Learning Approach for RF-Powered Cognitive Radio Network with Ambient Backscatter

© 2018 IEEE. For an RF-powered cognitive radio network with ambient backscattering capability, while the primary channel is busy, the RF-powered secondary user (RSU) can either backscatter the primary signal to transmit its own data or harvest energy from the primary signal (and store in its battery). The harvested energy then can be used to transmit data when the primary channel becomes idle. To maximize the throughput for the secondary system, it is critical for the RSU to decide when to backscatter and when to harvest energy. This optimal decision has to account for the dynamics of the primary channel, energy storage capability, and data to be sent. To tackle that problem, we propose a Markov decision process (MDP)-based framework to optimize RSU's decisions based on its current states, e.g., energy, data as well as the primary channel state. As the state information may not be readily available at the RSU, we then design a low-complexity online reinforcement learning algorithm that guides the RSU to find the optimal solution without requiring prior-and complete-information from the environment. The extensive simulation results then clearly show that the proposed solution achieves higher throughputs, i.e., up to 50%, than that of conventional methods

Cyberattack detection in mobile cloud computing: A deep learning approach

© 2018 IEEE. With the rapid growth of mobile applications and cloud computing, mobile cloud computing has attracted great interest from both academia and industry. However, mobile cloud applications are facing security issues such as data integrity, users' confidentiality, and service availability. A preventive approach to such problems is to detect and isolate cyber threats before they can cause serious impacts to the mobile cloud computing system. In this paper, we propose a novel framework that leverages a deep learning approach to detect cyberattacks in mobile cloud environment. Through experimental results, we show that our proposed framework not only recognizes diverse cyberattacks, but also achieves a high accuracy (up to 97.11%) in detecting the attacks. Furthermore, we present the comparisons with current machine learning-based approaches to demonstrate the effectiveness of our proposed solution

香港书面汉语句法变异 : 粤语的移用、文言的保留及其他

Author name used in this publication: 王灿龙Title in Traditional Chinese: 香港書面漢語句法變異: 粵語的移用、文言的保留及其他Journal title in Traditional Chinese: 語言文字應用2002-2003 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe