Institution
Vaughn College of Aeronautics and Technology
Education•New York, New York, United States•
About: Vaughn College of Aeronautics and Technology is a education organization based out in New York, New York, United States. It is known for research contribution in the topics: Gravitational microlensing & Planetary system. The organization has 727 authors who have published 708 publications receiving 14082 citations. The organization is also known as: College of Aeronautics.
Papers published on a yearly basis
Papers
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TL;DR: In this article, the authors assessed the dispersion of odour from a waste transfer station in the North London area, UK and compared the performance of UK-ADMS (version 1.5) and MPTER (A Multiple Point Gaussian Dispersion Algorithm with Optional Terrain Adjustment).
9 citations
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9 citations
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Chungbuk National University1, Ohio State University2, University of Warsaw3, University of Cambridge4, Nagoya University5, University of Notre Dame6, Massey University7, University of Auckland8, Osaka University9, Vaughn College of Aeronautics and Technology10, Victoria University of Wellington11, Kyoto Sangyo University12, University of Canterbury13, Institut d'Astrophysique de Paris14, University of St Andrews15, University of Rijeka16, University of Vienna17, University of Toulouse18, University of Tasmania19, University of the Free State20, University of Hawaii21, Niels Bohr Institute22, San Francisco State University23, Space Telescope Science Institute24, Heidelberg University25, Texas A&M University26, Korea Astronomy and Space Science Institute27, Tel Aviv University28, Auckland University of Technology29, Weizmann Institute of Science30, Harvard University31, European Southern Observatory32, University of Hamburg33, Max Planck Society34, Liverpool John Moores University35, Las Cumbres Observatory Global Telescope Network36, Queen Mary University of London37
TL;DR: In this article, the authors reanalyze microlensing events in the published list of anomalous events that were observed from the Optical Gravitational Lensing Experiment (OGLE) lensing survey conducted during the 2004-2008 period.
Abstract: We reanalyze microlensing events in the published list of anomalous events that were observed from the Optical Gravitational Lensing Experiment (OGLE) lensing survey conducted during the 2004–2008 period. In order to check the existence of possible degenerate solutions and extract extra information, we conduct analyses based on combined data from other survey and follow-up observation and consider higher-order effects. Among the analyzed events, we present analyses of eight events for which either new solutions are identified or additional information is obtained. We find that the previous binary-source interpretations of five events are better interpreted by binary-lens models. These events include OGLE-2006-BLG-238, OGLE-2007-BLG-159, OGLE-2007-BLG-491, OGLE-2008-BLG-143, and OGLE-2008-BLG-210. With additional data covering caustic crossings, we detect finite-source effects for six events including OGLE-2006-BLG-215, OGLE-2006-BLG-238, OGLE-2006-BLG-450, OGLE-2008-BLG-143, OGLE-2008-BLG-210, and OGLE-2008-BLG-513. Among them, we are able to measure the Einstein radii of three events for which multi-band data are available. These events are OGLE-2006-BLG-238, OGLE-2008-BLG-210, and OGLE-2008-BLG-513. For OGLE-2008-BLG-143, we detect higher-order effects induced by the changes of the observer's position caused by the orbital motion of the Earth around the Sun. In addition, we present degenerate solutions resulting from the known close/wide or ecliptic degeneracy. Finally, we note that the masses of the binary companions of the lenses of OGLE-2006-BLG-450 and OGLE-2008-BLG-210 are in the brown-dwarf regime.
9 citations
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TL;DR: Inspired by reinforcement learning that learns from its experience, this article proposes a novel efficient deep $Q$ -network (DQN)-based feature selection method for multisourced data cleaning and develops a space searching algorithm called SS to speed up the training process of the DQN agent.
Abstract: The Internet of Things (IoT) integrates information collected from multisources and is able to support various intelligent smart city applications, such as industrial manufacturing, power systems, and mobile healthcare. In the big data era, multisourced data are collected on a daily basis, whereas a large part of the data may be irrelevant, redundant, noisy, or even malicious from a machine learning perspective. Feature selection has been a powerful data cleaning technique to reduce data redundancy and improve system performance in machine learning. Inspired by reinforcement learning that learns from its experience, in this article, we propose a novel efficient deep $Q$ -network (DQN)-based feature selection method for multisourced data cleaning. In particular, we model the feature selection problem as a competition between an agent and the environment in dynamic states, which is solved by a DQN. Traditional DQN suffers from high computational complexity and requires a significant amount of time in order to converge in the training process. To tackle these challenges, we develop a space searching algorithm called SS to speed up the training process of the DQN agent. To validate the efficacy and efficiency of the proposed method, we conduct extensive experiments on various types of IoT data. Simulation results show that the proposed DQN-based feature selection algorithms achieve much better performance compared with state-of-the-art methods, and are robust under data poisoning attacks.
9 citations
Authors
Showing all 732 results
Name | H-index | Papers | Citations |
---|---|---|---|
Xiang Zhang | 154 | 1733 | 117576 |
Denis J. Sullivan | 61 | 332 | 14092 |
To. Saito | 51 | 183 | 9392 |
Arthur H. Lefebvre | 41 | 123 | 4896 |
Michele Meo | 40 | 223 | 5557 |
Robin S. Langley | 40 | 263 | 5601 |
Ning Qin | 37 | 283 | 5011 |
Holger Babinsky | 33 | 242 | 4068 |
B. S. Gaudi | 31 | 64 | 2560 |
Philip J. Longhurst | 29 | 80 | 2578 |
Michael Gaster | 27 | 66 | 3998 |
Don Harris | 26 | 129 | 2537 |
To. Saito | 25 | 56 | 2362 |
John F. O'Connell | 22 | 89 | 1763 |
Rade Vignjevic | 21 | 84 | 1563 |