Policies And International Integration: Influences On Trade And Foreign Direct Investment

This paper assesses the importance of border and non-border policies for global economic integration. The focus is on four widely-advocated policies: removing explicit restrictions to trade and FDI; promoting domestic competition; improving the adaptability of labour markets; and ensuring adequate levels of infrastructure capital. The analysis covers FDI and trade in both goods and services, thus aiming to account for the most important channels of globalisation and dealing with most modes of cross-border services supply. It first describes trends in trade, FDI and the four sets of policies using a large set of structural policy indicators recently constructed by the OECD, including the new summary indicators for FDI-specific regulations described in Golub (2003). It then estimates the impact of policies on bilateral trade and bilateral and multilateral FDI. The results highlight that, despite extensive liberalisation over the past two decades, there is scope for further reducing policy barriers to integration of OECD markets. Remaining barriers have a significant impact on trade and FDI, with anticompetitive domestic regulations and restrictive labour market arrangements estimated to curb integration as much as explicit trade and FDI restrictions. Simulating the removal of such barriers suggests that the quantitative effects of further liberalisation of trade, FDI and domestic product and labour markets on global integration could be substantial

Multi-Layered Clustering for Power Consumption Profiling in Smart Grids

Open access publicationSmart Grids (SGs) have many advantages over traditional power grids as they enhance the way electricity is generated, distributed, and consumed by adopting advanced sensing, communication and control functionalities that depend on power consumption profiles of consumers. Clustering algorithms (e.g., centralized clustering) are used for profiling individual’s power consumption. Due to the distributed nature and ever growing size of SGs, it is predicted that massive amounts of data will be created. However, conventional clustering algorithms neither efficient enough nor scalable enough to deal with such amount of data. In addition, the cost for transferring and analyzing large amounts of data is expensive high both computationally and communicationally. This paper thus proposes a power consumption profiling model based on two levels of clustering. At the first level, local power consumption profiles are derived, which are then used by the second level in order to create a global power consumption profile. The followed approach reduces the communication and computation complexity of the proposed two level model and improves the privacy of consumers. We point out that having good knowledge of the local power profiles leads to more effective prediction model and cost-effective power pricing scheme, especially in a heterogeneous grid topology. In addition, the correlations between the local and global profiles can be used to localize/identify power consumption outliers. Simulation results illustrate that the proposed model is effective in reducing the computational complexity without much affecting its accuracy. The reduction in computational complexity is about 52% and the reduction in the communicational complexity is about 95% when compared to the centralized clustering approach

Unsupervised Holistic Image Generation from Key Local Patches

We introduce a new problem of generating an image based on a small number of key local patches without any geometric prior. In this work, key local patches are defined as informative regions of the target object or scene. This is a challenging problem since it requires generating realistic images and predicting locations of parts at the same time. We construct adversarial networks to tackle this problem. A generator network generates a fake image as well as a mask based on the encoder-decoder framework. On the other hand, a discriminator network aims to detect fake images. The network is trained with three losses to consider spatial, appearance, and adversarial information. The spatial loss determines whether the locations of predicted parts are correct. Input patches are restored in the output image without much modification due to the appearance loss. The adversarial loss ensures output images are realistic. The proposed network is trained without supervisory signals since no labels of key parts are required. Experimental results on six datasets demonstrate that the proposed algorithm performs favorably on challenging objects and scenes.Comment: 16 page

An intent-based network virtualization platform for SDN

© 2016 IFIP. Currently, the Software Defined Networking (SDN) paradigm has attracted significant interests from industry and academia as a future network architecture. SDN brings many benefits to network operations and management including programmability, agility, elasticity, and flexibility. With SDN and OpenFlow, one of the promising SDN protocols, software defined Network Virtualization (NV) techniques can be designed and implemented via flow table segmentation to provision independent virtual networks (VNs). In this paper, we propose an intent based virtual network management platform based on software defined NV. The objective of the proposed NV platform is to automate the management and configuration of virtual networks based on high level tenant requirement specifications, called intents. The design and implementation of the platform is based on ONOS, an open-source SDN controller, and OpenVirteX, a network hypervisor. The platform is designed to provide multiple VNs over the same physical infrastructure to multiple tenants

Electroweak phase transition in a nonminimal supersymmetric model

The Higgs potential of the minimal nonminimal supersymmetric standard model (MNMSSM) is investigated within the context of electroweak phase transition. We investigate the allowed parameter space yielding correct electroweak phase transitoin employing a high temperature approximation. We devote to phenomenological consequences for the Higgs sector of the MNMSSM for electron-positron colliders. It is observed that a future $e^+ e^-$ linear collider with $\sqrt{s} = 1000$ GeV will be able to test the model with regard to electroweak baryogenesis.Comment: 28 pages, 5 tables, 12 figure

Social media use and impact during the holiday travel planning process

Through an empirical study among holiday travellers, residing in the Former Soviet Union Republics, this paper presents a comprehensive view of role and impact of social media on the whole holiday travel planning process: Before, during and after the trip, providing insights on usage levels, scope of use, level of influence and trust. Findings suggest that social media are predominantly used after holidays for experience sharing. It is also shown that there is a strong correlation between perceived level of influence from social media and changes made in holiday plans prior to final decisions. Moreover, it is revealed that user-generated content is perceived as more trustworthy when compared to official tourism websites, travel agents and mass media advertising

Observation of incoherently coupled dark-bright vector solitons in single-mode fibers

We report experimental observation of incoherently coupled dark-bright vector solitons in single-mode fibers. Properties of the vector solitons agree well with those predicted by the respective systems of incoherently coupled nonlinear Schroedinger equations. To the best of our knowledge, this is the first experimental observation of temporal incoherently coupled dark-bright solitons in single-mode fibers.Comment: to be published in Optics Expres