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Florian Gerber
Brigham Young University
20Publications
8H-index
177Citations
Publications 21
Newest
#1Matthew J. Heaton (BYU: Brigham Young University)H-Index: 11
#2Abhirup Datta (BYU: Brigham Young University)H-Index: 14
Last.Andrew Zammit-Mangion (BYU: Brigham Young University)H-Index: 1
view all 14 authors...
The Gaussian process is an indispensable tool for spatial data analysts. The onset of the “big data” era, however, has lead to the traditional Gaussian process being computationally infeasible for modern spatial data. As such, various alternatives to the full Gaussian process that are more amenable to handling big spatial data have been proposed. These modern methods often exploit low-rank structures and/or multi-core and multi-threaded computing environments to facilitate computation. This stud...
44 CitationsSource
#1Mostafa Karimzadeh (University of Bern)H-Index: 2
#2Florian Gerber (University of Bern)H-Index: 8
Last.Torsten Braun (University of Bern)H-Index: 31
view all 4 authors...
Increasing adoption of cellular phones equipped with global positioning system (GPS) chips enables the exploration of pedestrians' mobility patterns. Tasks such as discovering hot-spots in large cities can be addressed through the usage of accumulated GPS coordinates. In this work we utilize spatiotemporal analysis on collected geo-location points to discover Zone of Interests (ZOIs) of pedestrians in large cities to understand people's dynamics. We design an adaptive Markov model to forecast lo...
Source
The R package optimParallel provides a parallel version of the L-BFGS-B optimization method of optim(). The main function of the package is optimParallel(), which has the same usage and output as optim(). Using optimParallel() can significantly reduce the optimization time, especially when the evaluation time of the objective function is large and no analytical gradient is available. We introduce the R package and illustrate its implementation, which takes advantage of the lexical scoping mechan...
Source
#1Mostafa Karimzadeh (University of Bern)H-Index: 2
#2Zhongliang Zhao (University of Bern)H-Index: 11
Last.Torsten Braun (University of Bern)H-Index: 31
view all 4 authors...
The growing ubiquity of smart-phones equipped with built-in sensors and global positioning system (GPS) has resulted in the collection of large volumes of mobility data without the need of any additional devices. The large size of heterogeneous mobility data gives rise to rapid development of location-based services (LBSs). The predictability of mobile users’ behavior is essential to enhance LBSs. To predict human mobility, many techniques have been proposed. However, existing techniques require...
1 CitationsSource
#1Ben H. Warren (UZH: University of Zurich)H-Index: 4
#2Oskar Hagen (ETH Zurich)H-Index: 6
Last.Elena Conti (UZH: University of Zurich)H-Index: 34
view all 6 authors...
3 CitationsSource
#1Mostafa Karimzadeh (University of Bern)H-Index: 2
#2Zhongliang Zhao (University of Bern)H-Index: 11
Last.Torsten Braun (University of Bern)H-Index: 31
view all 4 authors...
The prevalence of smartphones equipped with global positioning system has enabled researchers to excavate users mobility patterns in the cities. The knowledge of users’ behavior, such as their locations, plays a significant role in location-based services, resource management, logistic administration and urban planning. To understand complex behavior of humans we utilize spatio-temporal analysis on collected geo-location points to exploit Individual Zone of Interests in urban areas. In addition,...
1 CitationsSource
#1Zhongliang Zhao (University of Bern)H-Index: 11
#2Mostafa Karimzadeh (University of Bern)H-Index: 2
Last.Torsten Braun (University of Bern)H-Index: 31
view all 4 authors...
With the explosive growth of location-based service on mobile devices, predicting users’ future locations and trajectories is of increasing importance to support proactive information services. In this paper, we model this problem as a supervised learning task and propose to use ensemble learning methods with hybrid features to solve it. We characterize the properties of users’ visited locations and movement patterns and then extract feature types (temporal, spatial, and system) to quantify the ...
2 CitationsSource
#1Florian Gerber (UZH: University of Zurich)H-Index: 8
#2Rogier de Jong (UZH: University of Zurich)H-Index: 11
Last.Reinhard Furrer (UZH: University of Zurich)H-Index: 22
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Continuous, consistent, and long time-series from remote sensing are essential to monitoring changes on Earth’s surface. However, analyzing such data sets is often challenging due to missing values introduced by cloud cover, missing orbits, sensor geometry artifacts, and so on. We propose a new and accurate spatio-temporal prediction method to replace missing values in remote sensing data sets. The method exploits the spatial coherence and temporal seasonal regularity that are inherent in many d...
10 CitationsSource
The R package optimParallel provides a parallel version of the gradient-based optimization methods of optim(). The main function of the package is optimParallel(), which has the same usage and output as optim(). Using optimParallel() can significantly reduce optimization times. We introduce the R package and illustrate its implementation, which takes advantage of the lexical scoping mechanism of R.
#1Florian Gerber (UZH: University of Zurich)H-Index: 8
#2K. Mösinger (UZH: University of Zurich)H-Index: 1
Last.Reinhard Furrer (UZH: University of Zurich)H-Index: 22
view all 3 authors...
Abstract The R package dotCall64 provides an enhanced version of the foreign function interface (FFI) to call compiled C, C++, and Fortran code from the software environment R. It allows users to integrate compiled code without using complex application programming interfaces (APIs), such as the C API of R. Moreover, dotCall64 supports long vectors having more than 2 31 − 1 elements and implements a mechanism to avoid unnecessary copies of R objects. Therefore, dotCall64 facilitates making exist...
2 CitationsSource
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