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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VU University Amsterdam1, University of Pennsylvania2, University of Maryland, Baltimore3, Cornell University4, New Mexico State University5, Qatar Airways6, Louisiana Tech University7, Université du Québec8, Stockholm School of Economics9, University of Buenos Aires10, University of Alberta11, University of Indonesia12, University of Queensland13, Bellevue University14, London Business School15, Western Illinois University16, University of Memphis17, Fudan University18, Boğaziçi University19, University of Reading20, University of South Africa21, Athens University of Economics and Business22, Ludwig Maximilian University of Munich23, University of Calgary24, University of Burgundy25, National Sun Yat-sen University26, Hong Kong Polytechnic University27, Indian Institute of Management Ahmedabad28, City University of Hong Kong29, Lincoln University (New Zealand)30, University of Lethbridge31, Wayne State University32, University College Dublin33, Indiana University34, Kuwait University35, Technion – Israel Institute of Technology36, University of Giessen37, The Chinese University of Hong Kong38, University of Zurich39, Fordham University40, Complutense University of Madrid41, University of Nebraska–Lincoln42, INCAE Business School43, National University of Malaysia44, Opole University45, Hong Kong Baptist University46, Tbilisi State University47, Ohio State University48, University of Wrocław49, Alexandria University50, University of San Francisco51, Melbourne Business School52, Bentley University53, University of Los Andes54, I-Shou University55, Johannes Kepler University of Linz56, International Labour Organization57, Smith College58, Copenhagen Business School59, Chungnam National University60, National University of Singapore61, Tilburg University62, Hong Kong University of Science and Technology63, Thammasat University64, Sewanee: The University of the South65, FernUniversität Hagen66, Soochow University (Suzhou)67, University of St. Gallen68, Kumamoto University69
TL;DR: In this paper, the authors focus on culturally endorsed implicit theories of leadership (CLTs) and show that attributes associated with charismatic/transformational leadership will be universally endorsed as contributing to outstanding leadership.
Abstract: This study focuses on culturally endorsed implicit theories of leadership (CLTs). Although cross-cultural research emphasizes that different cultural groups likely have different conceptions of what leadership should entail, a controversial position is argued here: namely that attributes associated with charismatic/transformational leadership will be universally endorsed as contributing to outstanding leadership. This hypothesis was tested in 62 cultures as part of the Global Leadership and Organizational Behavior Effectiveness (GLOBE) Research Program. Universally endorsed leader attributes, as well as attributes that are universally seen as impediments to outstanding leadership and culturally contingent attributes are presented here. The results support the hypothesis that specific aspects of charismatic/transformational leadership are strongly and universally endorsed across cultures.
1,227 citations
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TL;DR: Euclid as mentioned in this paper is a space-based survey mission from the European Space Agency designed to understand the origin of the universe's accelerating expansion, using cosmological probes to investigate the nature of dark energy, dark matter and gravity by tracking their observational signatures.
Abstract: Euclid is a space-based survey mission from the European Space Agency designed to understand the origin of the Universe's accelerating expansion. It will use cosmological probes to investigate the nature of dark energy, dark matter and gravity by tracking their observational signatures on the geometry of the universe and on the cosmic history of structure formation. The mission is optimised for two independent primary cosmological probes: Weak gravitational Lensing (WL) and Baryonic Acoustic Oscillations (BAO). The Euclid payload consists of a 1.2 m Korsch telescope designed to provide a large field of view. It carries two instruments with a common field-of-view of ~0.54 deg2: the visual imager (VIS) and the near infrared instrument (NISP) which contains a slitless spectrometer and a three bands photometer. The Euclid wide survey will cover 15,000 deg2 of the extragalactic sky and is complemented by two 20 deg2 deep fields. For WL, Euclid measures the shapes of 30-40 resolved galaxies per arcmin2 in one broad visible R+I+Z band (550-920 nm). The photometric redshifts for these galaxies reach a precision of dz/(1+z) \lt 0.05. They are derived from three additional Euclid NIR bands (Y, J, H in the range 0.92-2.0 micron), complemented by ground based photometry in visible bands derived from public data or through engaged collaborations. The BAO are determined from a spectroscopic survey with a redshift accuracy dz/(1+z) =0.001. The slitless spectrometer, with spectral resolution ~250, predominantly detects Ha emission line galaxies. Euclid is a Medium Class mission of the ESA Cosmic Vision 2015-2025 programme, with a foreseen launch date in 2019. This report (also known as the Euclid Red Book) describes the outcome of the Phase A study.
1,213 citations
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TL;DR: A review of the mechanisms of solute sorption onto various biosorbents has been performed in this article, where the mechanisms have been subdivided into reaction based systems and diffusion based systems, and the literature has been reviewed in accordance with these two groups.
Abstract: A review of the mechanisms of solute sorption onto various biosorbents has been performed. The mechanisms have been subdivided into reaction based systems and diffusion based systems and the literature has been reviewed in accordance with these two groups. The range of solute-sorbent systems reviewed include metal ions, dyestuffs and several organic substances in aqueous systems onto a wide range of biosorbents and mineral earths. Extensive tables are presented summarising isotherm types, sorption capacities, kinetic models which have been applied particularly to biosorbent systems but also to many other adsorbent materials.
1,209 citations
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25 Apr 2019TL;DR: A simplified network based on a query-independent formulation, which maintains the accuracy of NLNet but with significantly less computation is created, and this simplified design shares similar structure with Squeeze-Excitation Network (SENet), which generally outperforms both simplified NLNet and SENet on major benchmarks for various recognition tasks.
Abstract: The Non-Local Network (NLNet) presents a pioneering approach for capturing long-range dependencies, via aggregating query-specific global context to each query position. However, through a rigorous empirical analysis, we have found that the global contexts modeled by non-local network are almost the same for different query positions within an image. In this paper, we take advantage of this finding to create a simplified network based on a query-independent formulation, which maintains the accuracy of NLNet but with significantly less computation. We further observe that this simplified design shares similar structure with Squeeze-Excitation Network (SENet). Hence we unify them into a three-step general framework for global context modeling. Within the general framework, we design a better instantiation, called the global context (GC) block, which is lightweight and can effectively model the global context. The lightweight property allows us to apply it for multiple layers in a backbone network to construct a global context network (GCNet), which generally outperforms both simplified NLNet and SENet on major benchmarks for various recognition tasks.
1,202 citations
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TL;DR: Multi-task learning (MTL) as mentioned in this paper is a learning paradigm in machine learning and its aim is to leverage useful information contained in multiple related tasks to help improve the generalization performance of all the tasks.
Abstract: Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to leverage useful information contained in multiple related tasks to help improve the generalization performance of all the tasks. In this paper, we give a survey for MTL from the perspective of algorithmic modeling, applications and theoretical analyses. For algorithmic modeling, we give a definition of MTL and then classify different MTL algorithms into five categories, including feature learning approach, low-rank approach, task clustering approach, task relation learning approach and decomposition approach as well as discussing the characteristics of each approach. In order to improve the performance of learning tasks further, MTL can be combined with other learning paradigms including semi-supervised learning, active learning, unsupervised learning, reinforcement learning, multi-view learning and graphical models. When the number of tasks is large or the data dimensionality is high, we review online, parallel and distributed MTL models as well as dimensionality reduction and feature hashing to reveal their computational and storage advantages. Many real-world applications use MTL to boost their performance and we review representative works in this paper. Finally, we present theoretical analyses and discuss several future directions for MTL.
1,202 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 |