Institution

# Johns Hopkins University Applied Physics Laboratory

Facility•North Laurel, Maryland, United States•

About: Johns Hopkins University Applied Physics Laboratory is a facility organization based out in North Laurel, Maryland, United States. It is known for research contribution in the topics: Magnetosphere & Solar wind. The organization has 4836 authors who have published 9970 publications receiving 314161 citations.

Topics: Magnetosphere, Solar wind, Substorm, Ionosphere, Magnetopause

##### Papers published on a yearly basis

##### Papers

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TL;DR: The Duality Principle relating stochastic estimation and deterministic control problems plays an important role in the proof of theoretical results and properties of the variance equation are of great interest in the theory of adaptive systems.

Abstract: A nonlinear differential equation of the Riccati type is derived for the covariance matrix of the optimal filtering error. The solution of this \"variance equation\" completely specifies the optimal filter for either finite or infinite smoothing intervals and stationary or nonstationary statistics. The variance equation is closely related to the Hamiltonian (canonical) differential equations of the calculus of variations. Analytic solutions are available in some cases. The significance of the variance equation is illustrated by examples which duplicate, simplify, or extend earlier results in this field. The Duality Principle relating stochastic estimation and deterministic control problems plays an important role in the proof of theoretical results. In several examples, the estimation problem and its dual are discussed side-by-side. Properties of the variance equation are of great interest in the theory of adaptive systems. Some aspects of this are considered briefly.

6,152 citations

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TL;DR: This work presents a simple and efficient implementation of Lloyd's k-means clustering algorithm, which it calls the filtering algorithm, and establishes the practical efficiency of the algorithm's running time.

Abstract: In k-means clustering, we are given a set of n data points in d-dimensional space R/sup d/ and an integer k and the problem is to determine a set of k points in Rd, called centers, so as to minimize the mean squared distance from each data point to its nearest center. A popular heuristic for k-means clustering is Lloyd's (1982) algorithm. We present a simple and efficient implementation of Lloyd's k-means clustering algorithm, which we call the filtering algorithm. This algorithm is easy to implement, requiring a kd-tree as the only major data structure. We establish the practical efficiency of the filtering algorithm in two ways. First, we present a data-sensitive analysis of the algorithm's running time, which shows that the algorithm runs faster as the separation between clusters increases. Second, we present a number of empirical studies both on synthetically generated data and on real data sets from applications in color quantization, data compression, and image segmentation.

5,288 citations

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TL;DR: The complexity of ML/DM algorithms is addressed, discussion of challenges for using ML/ DM for cyber security is presented, and some recommendations on when to use a given method are provided.

Abstract: This survey paper describes a focused literature survey of machine learning (ML) and data mining (DM) methods for cyber analytics in support of intrusion detection. Short tutorial descriptions of each ML/DM method are provided. Based on the number of citations or the relevance of an emerging method, papers representing each method were identified, read, and summarized. Because data are so important in ML/DM approaches, some well-known cyber data sets used in ML/DM are described. The complexity of ML/DM algorithms is addressed, discussion of challenges for using ML/DM for cyber security is presented, and some recommendations on when to use a given method are provided.

1,704 citations

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University of Leeds

^{1}, Jet Propulsion Laboratory^{2}, University of Colorado Boulder^{3}, Technical University of Denmark^{4}, Durham University^{5}, University of Texas at Austin^{6}, Ohio State University^{7}, University College London^{8}, Technische Universität München^{9}, Lamont–Doherty Earth Observatory^{10}, Johns Hopkins University Applied Physics Laboratory^{11}, University of Tasmania^{12}, Newcastle University^{13}, Utrecht University^{14}, University of Kansas^{15}, Swansea University^{16}, Goddard Space Flight Center^{17}, University of Ottawa^{18}, University of California, Irvine^{19}, University of Bristol^{20}, British Antarctic Survey^{21}, University of Innsbruck^{22}, Delft University of Technology^{23}TL;DR: There is good agreement between different satellite methods—especially in Greenland and West Antarctica—and that combining satellite data sets leads to greater certainty, and the mass balance of Earth’s polar ice sheets is estimated by combining the results of existing independent techniques.

Abstract: We combined an ensemble of satellite altimetry, interferometry, and gravimetry data sets using common geographical regions, time intervals, and models of surface mass balance and glacial isostatic adjustment to estimate the mass balance of Earth’s polar ice sheets. We find that there is good agreement between different satellite methods—especially in Greenland and West Antarctica—and that combining satellite data sets leads to greater certainty. Between 1992 and 2011, the ice sheets of Greenland, East Antarctica, West Antarctica, and the Antarctic Peninsula changed in mass by –142 ± 49, +14 ± 43, –65 ± 26, and –20 ± 14 gigatonnes year−1, respectively. Since 1992, the polar ice sheets have contributed, on average, 0.59 ± 0.20 millimeter year−1 to the rate of global sea-level rise.

1,215 citations

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TL;DR: In this paper, the magnitude, spatial distribution, and seasonality of the surface warming in the Arctic is examined and compared among the models, and it is found that the mean sea-ice state in the control (or present) climate is found to influence both the magnitude and spatial distribution of the high-latitude warming in models.

Abstract: The Northern Hemisphere polar amplification of climate change is documented in models taking part in the Coupled Model Intercomparison Project and in the new version of the Community Climate System Model. In particular, the magnitude, spatial distribution, and seasonality of the surface warming in the Arctic is examined and compared among the models. The range of simulated polar warming in the Arctic is from 1.5 to 4.5 times the global mean warming. While ice-albedo feedback is likely to account for much of the polar amplification, the strength of the feedback depends on numerous physical processes and parametrizations which differ considerably among the models. Nonetheless, the mean sea-ice state in the control (or present) climate is found to influence both the magnitude and spatial distribution of the high-latitude warming in the models. In particular, the latitude of the maximum warming is correlated inversely and significantly with sea-ice extent in the control climate. Additionally, models with relatively thin Arctic ice cover in the control climate tend to have higher polar amplification. An intercomparison of model results also shows that increases in poleward ocean heat transport at high latitudes and increases in polar cloud cover are significantly correlated to amplified Arctic warming. This suggests that these changes in the climate state may modify polar amplification. No significant correlation is found between polar amplification and the control climate continental ice and snow cover.

1,125 citations

##### Authors

Showing all 4902 results

Name | H-index | Papers | Citations |
---|---|---|---|

David J. Smith | 125 | 2090 | 108066 |

Matthew Jones | 125 | 1161 | 96909 |

Peter J. Pronovost | 118 | 737 | 55076 |

Benjamin Smith | 103 | 631 | 46514 |

Kenneth S. Suslick | 101 | 475 | 41105 |

James A. Yorke | 101 | 445 | 44101 |

Edward Ott | 101 | 669 | 44649 |

Louis R. Kavoussi | 95 | 544 | 31830 |

Grover M. Hutchins | 92 | 462 | 26889 |

Wolfgang Baumjohann | 86 | 712 | 33376 |

Scott L. Murchie | 86 | 350 | 22380 |

YangQuan Chen | 84 | 1048 | 36543 |

Patrick S. Stayton | 83 | 320 | 22016 |

Vassilis Angelopoulos | 80 | 741 | 32314 |

Geoffrey D. Reeves | 80 | 547 | 22464 |