84 research outputs found

    Intellectual Property Regimes, Innovative Capabilities, and Patenting in Korea

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    In this paper, we empirically investigate whether and to what extent major changes in IPR contributed to subsequent upgrading of innovative capabilities and patenting in Korea. We found that major IPR changes in Korea in the 1980s led to the big increase in patenting, thereby supporting the friendly court hypothesis. Especially, the trend of substance patent applications by local residents seems to suggest that the IPR change in Korea encouraged local firms to focus more on developing innovative capabilities and patenting more actively. Based on the Korean experience, we offer an insight into the recent debate on the relationship between IPR and economic development in developing countries

    Search Behavior and Catch-up of Firms in Emerging Markets

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    This study investigates catch-up in the form of knowledge creation of firms in emerging markets by stressing two distinct types of search behaviors of an organization โ€“ horizontal search and vertical search. Based on an empirical analysis of 204 Chinese firms, this study provides new theoretical insights into and practical implications by emphasizing that in order to catch-up, firms in emerging markets should adopt idiosyncratic search strategies different from those of firms in more advanced countries. The regression results show that due to their under-developed absorptive capacity, firms in emerging markets should avoid searching in diverse knowledge fields, as established large firms in advanced countries are encouraged to do, in order to innovate successfully. Our findings also suggest that searching for recent and emerging knowledge helps firms in emerging markets overcome their learning curve disadvantage in the process of catch-up

    GraNNDis: Efficient Unified Distributed Training Framework for Deep GNNs on Large Clusters

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    Graph neural networks (GNNs) are one of the most rapidly growing fields within deep learning. According to the growth in the dataset and the model size used for GNNs, an important problem is that it becomes nearly impossible to keep the whole network on GPU memory. Among numerous attempts, distributed training is one popular approach to address the problem. However, due to the nature of GNNs, existing distributed approaches suffer from poor scalability, mainly due to the slow external server communications. In this paper, we propose GraNNDis, an efficient distributed GNN training framework for training GNNs on large graphs and deep layers. GraNNDis introduces three new techniques. First, shared preloading provides a training structure for a cluster of multi-GPU servers. We suggest server-wise preloading of essential vertex dependencies to reduce the low-bandwidth external server communications. Second, we present expansion-aware sampling. Because shared preloading alone has limitations because of the neighbor explosion, expansion-aware sampling reduces vertex dependencies that span across server boundaries. Third, we propose cooperative batching to create a unified framework for full-graph and minibatch training. It significantly reduces redundant memory usage in mini-batch training. From this, GraNNDis enables a reasonable trade-off between full-graph and mini-batch training through unification especially when the entire graph does not fit into the GPU memory. With experiments conducted on a multi-server/multi-GPU cluster, we show that GraNNDis provides superior speedup over the state-of-the-art distributed GNN training frameworks

    Learning and innovation: Exploitation and exploration trade-offs

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    a b s t r a c t a r t i c l e i n f o This paper examines the relationship between learning and innovation outcomes, focusing on the trade-off between exploitation and exploration in learning and innovation. The study identifies two types of learning and two outcomes of innovation. Exploitation and exploration in learning are inversely associated with innovation rates and impact. While exploitative, localized learning is positively associated with innovation rates, but negatively associated with impact, exploratory learning-by-experimentation shows the opposite relationship. The study examines panel data of 103 companies in the global pharmaceutical industry over a 7-year period in an empirical test of our hypotheses. Results support the existence of the exploitation and exploration trade-off

    Explaining variations in semiconductor catch-up strategies in China, Korea, Malaysia and Taiwan

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    Presented at the GLOBELICS 6th International Conference 2008 22-24 September, Mexico City, Mexico

    Chiral self-sorted multifunctional supramolecular biocoordination polymers and their applications in sensors

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    Chiral supramolecules have great potential for use in chiral recognition, sensing, and catalysis. Particularly, chiral supramolecular biocoordination polymers (SBCPs) provide a versatile platform for characterizing biorelated processes such as chirality transcription. Here, we selectively synthesize homochiral and heterochiral SBCPs, composed of chiral naphthalene diimide ligands and Zn ions, from enantiomeric and mixed R-ligands and S-ligands, respectively. Notably, we find that the chiral self-sorted SBCPs exhibit multifunctional properties, including photochromic, photoluminescent, photoconductive, and chemiresistive characteristics, thus can be used for various sensors. Specifically, these materials can be used for detecting hazardous amine materials due to the electron transfer from the amine to the SBCP surface and for enantioselectively sensing a chiral species naproxen due to the different binding energies with regard to their chirality. These results provide guidelines for the synthesis of chiral SBCPs and demonstrate their versatility and feasibility for use in various sensors covering photoactive, chemiresistive, and chiral sensors

    OF@TEIN: An OpenFlow-enabled SDN Testbed over International SmartX Rack Sites

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    In this paper, we will discuss our on-going effort for OF@TEIN SDN(Software-Defined Networking) testbed, which currently spans over Korea and fiveSouth-East Asian (SEA) collaborators with internationally deployed OpenFlowenabledSmartX Racks
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