Institution
University of Luxembourg
Education•Luxembourg, Luxembourg•
About: University of Luxembourg is a education organization based out in Luxembourg, Luxembourg. It is known for research contribution in the topics: Context (language use) & Computer science. The organization has 4744 authors who have published 22175 publications receiving 381824 citations.
Papers published on a yearly basis
Papers
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01 Jan 2015
TL;DR: The process used to build the Luxembourg SUMO Traffic (LuST) Scenario is shown, and a summary of its characteristics together with an overview of its possible use cases is presented.
Abstract: Different research communities varying from telecommunication to traffic engineering are working on problems related to vehicular traffic congestion, intelligent transportation systems, and mobility patterns using information collected from a variety of sensors. To test the solutions, the first step is to use a vehicular traffic simulator with an appropriate scenario in order to reproduce realistic mobility patterns. Many mobility simulators are available, and the choice is usually done based on the size and type of simulation required, but a common problem is to find a realistic traffic scenario. In order to evaluate and compare new communication protocols for vehicular networks, it is necessary to use a wireless network simulator in combination with a vehicular traffic simulator. This additional step introduces further requirements for the scenario. The aim of this work is to provide a scenario able to meet all the common requirements in terms of size, realism and duration, in order to have a common basis for the evaluations. In the interest of building a realistic scenario, we decided to start from a real city with a standard topology common in mid-size European cities, and real information concerning traffic demands and mobility patterns. In this paper we show the process used to build the Luxembourg SUMO Traffic (LuST) Scenario, and present a summary of its characteristics together with an overview of its possible use cases.
244 citations
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TL;DR: In this paper, the authors summarize the progress made recently in understanding the electronic structure of chalcopyrite solar cells and summarize the results of optoelectronic defect spectroscopy.
Abstract: We summarize the progress made recently in understanding the electronic structure of chalcopyrites. New insights into the dispersion of valence and conduction band allow conclusions on the effective masses of charge carriers and their orientation dependence, which influences the transport in solar cell absorbers of different orientation. Native point defects are responsible for the doping and thus the band bending in solar cells. Results of optoelectronic defect spectroscopy are reviewed. Native defects are also the source for a number of metastabilities, which strongly affect the efficiency of solar cells. Recent theoretical findings relate these effects to the Se vacancy and the InCu antisite defect. Experimentally determined activation energies support these models. Absorbers in chalcopyrite solar cells are polycrystalline, which is only possible because of the benign character of the grain boundaries. This can be related to an unusual electronic structure of the GB. Copyright © 2010 John Wiley & Sons, Ltd.
241 citations
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TL;DR: This paper proposes to create a new set of features based on analyzing the periodic behavior of the time of a transaction using the von Mises distribution, and examines how the different sets of features have an impact on the results.
Abstract: Credit card fraud detection evaluation measure.Each example is assumed to have different financial cost.Transaction aggregation strategy for predicting fraud.Periodic features using the von Mises distribution.Code is open source and available at albahnsen.com/CostSensitiveClassification. Every year billions of Euros are lost worldwide due to credit card fraud. Thus, forcing financial institutions to continuously improve their fraud detection systems. In recent years, several studies have proposed the use of machine learning and data mining techniques to address this problem. However, most studies used some sort of misclassification measure to evaluate the different solutions, and do not take into account the actual financial costs associated with the fraud detection process. Moreover, when constructing a credit card fraud detection model, it is very important how to extract the right features from the transactional data. This is usually done by aggregating the transactions in order to observe the spending behavioral patterns of the customers. In this paper we expand the transaction aggregation strategy, and propose to create a new set of features based on analyzing the periodic behavior of the time of a transaction using the von Mises distribution. Then, using a real credit card fraud dataset provided by a large European card processing company, we compare state-of-the-art credit card fraud detection models, and evaluate how the different sets of features have an impact on the results. By including the proposed periodic features into the methods, the results show an average increase in savings of 13%.
240 citations
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TL;DR: This second release of i-PI not only includes several new advanced path integral methods, but also offers other classes of algorithms that are moving towards becoming a universal force engine that is both modular and tightly coupled to the driver codes that evaluate the potential energy surface and its derivatives.
238 citations
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TL;DR: A dramatic drop in the neural stem cells (NSCs) number in the aging murine brain is reported and this smaller stem cell reservoir is protected from full depletion by an increase in quiescence that makes old NSCs more resistant to regenerate the injured brain.
238 citations
Authors
Showing all 4893 results
Name | H-index | Papers | Citations |
---|---|---|---|
Jun Wang | 166 | 1093 | 141621 |
Leroy Hood | 158 | 853 | 128452 |
Andreas Heinz | 108 | 1078 | 45002 |
Philippe Dubois | 101 | 1098 | 48086 |
John W. Berry | 97 | 351 | 52470 |
Michael Müller | 91 | 333 | 26237 |
Bart Preneel | 82 | 844 | 25572 |
Bjorn Ottersten | 81 | 1058 | 28359 |
Sander Kersten | 79 | 246 | 23985 |
Alexandre Tkatchenko | 77 | 271 | 26863 |
Rudi Balling | 75 | 238 | 19529 |
Lionel C. Briand | 75 | 380 | 24519 |
Min Wang | 72 | 716 | 19197 |
Stephen H. Friend | 70 | 184 | 53422 |
Ekhard K. H. Salje | 70 | 581 | 19938 |