Institution
Hong Kong University of Science and Technology
Education•Hong Kong, Hong Kong, China•
About: Hong Kong University of Science and Technology is a education organization based out in Hong Kong, Hong Kong, China. It is known for research contribution in the topics: Computer science & Catalysis. The organization has 20126 authors who have published 52428 publications receiving 1965915 citations. The organization is also known as: HKUST & The Hong Kong University of Science and Technology.
Topics: Computer science, Catalysis, Communication channel, CMOS, MIMO
Papers published on a yearly basis
Papers
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TL;DR: This work proposes a novel dimensionality reduction framework for reducing the distance between domains in a latent space for domain adaptation and proposes both unsupervised and semisupervised feature extraction approaches, which can dramatically reduce thedistance between domain distributions by projecting data onto the learned transfer components.
Abstract: Domain adaptation allows knowledge from a source domain to be transferred to a different but related target domain. Intuitively, discovering a good feature representation across domains is crucial. In this paper, we first propose to find such a representation through a new learning method, transfer component analysis (TCA), for domain adaptation. TCA tries to learn some transfer components across domains in a reproducing kernel Hilbert space using maximum mean miscrepancy. In the subspace spanned by these transfer components, data properties are preserved and data distributions in different domains are close to each other. As a result, with the new representations in this subspace, we can apply standard machine learning methods to train classifiers or regression models in the source domain for use in the target domain. Furthermore, in order to uncover the knowledge hidden in the relations between the data labels from the source and target domains, we extend TCA in a semisupervised learning setting, which encodes label information into transfer components learning. We call this extension semisupervised TCA. The main contribution of our work is that we propose a novel dimensionality reduction framework for reducing the distance between domains in a latent space for domain adaptation. We propose both unsupervised and semisupervised feature extraction approaches, which can dramatically reduce the distance between domain distributions by projecting data onto the learned transfer components. Finally, our approach can handle large datasets and naturally lead to out-of-sample generalization. The effectiveness and efficiency of our approach are verified by experiments on five toy datasets and two real-world applications: cross-domain indoor WiFi localization and cross-domain text classification.
3,195 citations
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TL;DR: A comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management is provided in this paper, where a set of issues, challenges, and future research directions for MEC are discussed.
Abstract: Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized mobile cloud computing toward mobile edge computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also discuss a set of issues, challenges, and future research directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.
2,992 citations
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TL;DR: This comprehensive Review focuses on the low- and non-platinum electrocatalysts including advanced platinum alloys, core-shell structures, palladium-based catalysts, metal oxides and chalcogenides, carbon-based non-noble metal catalysts and metal-free catalysts.
Abstract: The recent advances in electrocatalysis for oxygen reduction reaction (ORR) for proton exchange membrane fuel cells (PEMFCs) are thoroughly reviewed. This comprehensive Review focuses on the low- and non-platinum electrocatalysts including advanced platinum alloys, core–shell structures, palladium-based catalysts, metal oxides and chalcogenides, carbon-based non-noble metal catalysts, and metal-free catalysts. The recent development of ORR electrocatalysts with novel structures and compositions is highlighted. The understandings of the correlation between the activity and the shape, size, composition, and synthesis method are summarized. For the carbon-based materials, their performance and stability in fuel cells and comparisons with those of platinum are documented. The research directions as well as perspectives on the further development of more active and less expensive electrocatalysts are provided.
2,964 citations
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20 Aug 19962,938 citations
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TL;DR: The results show that the proposed algorithm outperforms multiuser OFDM systems with static time-division multiple access (TDMA) or frequency-divisionmultiple access (FDMA) techniques which employ fixed and predetermined time-slot or subcarrier allocation schemes.
Abstract: Multiuser orthogonal frequency division multiplexing (OFDM) with adaptive multiuser subcarrier allocation and adaptive modulation is considered. Assuming knowledge of the instantaneous channel gains for all users, we propose a multiuser OFDM subcarrier, bit, and power allocation algorithm to minimize the total transmit power. This is done by assigning each user a set of subcarriers and by determining the number of bits and the transmit power level for each subcarrier. We obtain the performance of our proposed algorithm in a multiuser frequency selective fading environment for various time delay spread values and various numbers of users. The results show that our proposed algorithm outperforms multiuser OFDM systems with static time-division multiple access (TDMA) or frequency-division multiple access (FDMA) techniques which employ fixed and predetermined time-slot or subcarrier allocation schemes. We have also quantified the improvement in terms of the overall required transmit power, the bit-error rate (BER), or the area of coverage for a given outage probability.
2,925 citations
Authors
Showing all 20461 results
Name | H-index | Papers | Citations |
---|---|---|---|
Ruedi Aebersold | 182 | 879 | 141881 |
John R. Yates | 177 | 1036 | 129029 |
John Hardy | 177 | 1178 | 171694 |
Lei Jiang | 170 | 2244 | 135205 |
Gang Chen | 167 | 3372 | 149819 |
Roger Y. Tsien | 163 | 441 | 138267 |
Xiang Zhang | 154 | 1733 | 117576 |
Rui Zhang | 151 | 2625 | 107917 |
Ben Zhong Tang | 149 | 2007 | 116294 |
Michael E. Greenberg | 148 | 316 | 114317 |
Yi Yang | 143 | 2456 | 92268 |
Shi-Zhang Qiao | 142 | 523 | 80888 |
Shuit-Tong Lee | 138 | 1121 | 77112 |
David H. Pashley | 137 | 740 | 63657 |
Steven G. Louie | 137 | 777 | 88794 |