TY - JOUR A1 - Meinel, Christoph A1 - Wang, Long T1 - Building content clusters based on modelling page pairs N2 - We give a new view on building content clusters from page pair models. We measure the heuristic importance within every two pages by computing the distance of their accessed positions in usage sessions. We also compare our page pair models with the classical pair models used in information theories and natural language processing, and give different evaluation methods to build the reasonable content communities. And we finally interpret the advantages and disadvantages of our models from detailed experiment results Y1 - 2006 UR - http://www.springerlink.com/content/105633/ U6 - https://doi.org/10.1007/11610113_85 ER - TY - JOUR A1 - Menzel, Michael T1 - Modelling security in service-oriented architectures Y1 - 2010 SN - 978-3-86956-036-6 ER - TY - JOUR A1 - Metref, Sammy A1 - Cosme, Emmanuel A1 - Le Sommer, Julien A1 - Poel, Nora A1 - Brankart, Jean-Michel A1 - Verron, Jacques A1 - Gomez Navarro, Laura T1 - Reduction of spatially structured errors in Wide-Swath altimetric satellite data using data assimilation JF - Remote sensing N2 - The Surface Water and Ocean Topography (SWOT) mission is a next generation satellite mission expected to provide a 2 km-resolution observation of the sea surface height (SSH) on a two-dimensional swath. Processing SWOT data will be challenging because of the large amount of data, the mismatch between a high spatial resolution and a low temporal resolution, and the observation errors. The present paper focuses on the reduction of the spatially structured errors of SWOT SSH data. It investigates a new error reduction method and assesses its performance in an observing system simulation experiment. The proposed error-reduction method first projects the SWOT SSH onto a subspace spanned by the SWOT spatially structured errors. This projection is removed from the SWOT SSH to obtain a detrended SSH. The detrended SSH is then processed within an ensemble data assimilation analysis to retrieve a full SSH field. In the latter step, the detrending is applied to both the SWOT data and an ensemble of model-simulated SSH fields. Numerical experiments are performed with synthetic SWOT observations and an ensemble from a North Atlantic, 1/60 degrees simulation of the ocean circulation (NATL60). The data assimilation analysis is carried out with an ensemble Kalman filter. The results are assessed with root mean square errors, power spectrum density, and spatial coherence. They show that a significant part of the large scale SWOT errors is reduced. The filter analysis also reduces the small scale errors and allows for an accurate recovery of the energy of the signal down to 25 km scales. In addition, using the SWOT nadir data to adjust the SSH detrending further reduces the errors. KW - SWOT KW - correlated errors KW - OSSE KW - projection KW - detrending KW - ensemble kalman filter Y1 - 2019 U6 - https://doi.org/10.3390/rs11111336 SN - 2072-4292 VL - 11 IS - 11 PB - MDPI CY - Basel ER - TY - JOUR A1 - Michallek, Florian A1 - Genske, Ulrich A1 - Niehues, Stefan Markus A1 - Hamm, Bernd A1 - Jahnke, Paul T1 - Deep learning reconstruction improves radiomics feature stability and discriminative power in abdominal CT imaging BT - a phantom study JF - European Radiology N2 - Objectives To compare image quality of deep learning reconstruction (AiCE) for radiomics feature extraction with filtered back projection (FBP), hybrid iterative reconstruction (AIDR 3D), and model-based iterative reconstruction (FIRST). Methods Effects of image reconstruction on radiomics features were investigated using a phantom that realistically mimicked a 65-year-old patient's abdomen with hepatic metastases. The phantom was scanned at 18 doses from 0.2 to 4 mGy, with 20 repeated scans per dose. Images were reconstructed with FBP, AIDR 3D, FIRST, and AiCE. Ninety-three radiomics features were extracted from 24 regions of interest, which were evenly distributed across three tissue classes: normal liver, metastatic core, and metastatic rim. Features were analyzed in terms of their consistent characterization of tissues within the same image (intraclass correlation coefficient >= 0.75), discriminative power (Kruskal-Wallis test p value < 0.05), and repeatability (overall concordance correlation coefficient >= 0.75). Results The median fraction of consistent features across all doses was 6%, 8%, 6%, and 22% with FBP, AIDR 3D, FIRST, and AiCE, respectively. Adequate discriminative power was achieved by 48%, 82%, 84%, and 92% of features, and 52%, 20%, 17%, and 39% of features were repeatable, respectively. Only 5% of features combined consistency, discriminative power, and repeatability with FBP, AIDR 3D, and FIRST versus 13% with AiCE at doses above 1 mGy and 17% at doses >= 3 mGy. AiCE was the only reconstruction technique that enabled extraction of higher-order features. Conclusions AiCE more than doubled the yield of radiomics features at doses typically used clinically. Inconsistent tissue characterization within CT images contributes significantly to the poor stability of radiomics features. KW - Tomography KW - X-ray computed KW - Phantoms KW - imaging KW - Liver neoplasms KW - Algorithms KW - Reproducibility of results Y1 - 2022 U6 - https://doi.org/10.1007/s00330-022-08592-y SN - 1432-1084 VL - 32 IS - 7 SP - 4587 EP - 4595 PB - Springer CY - New York ER - TY - JOUR A1 - Middelanis, Robin A1 - Willner, Sven N. A1 - Otto, Christian A1 - Kuhla, Kilian A1 - Quante, Lennart A1 - Levermann, Anders T1 - Wave-like global economic ripple response to Hurricane Sandy JF - Environmental research letters : ERL / Institute of Physics N2 - Tropical cyclones range among the costliest disasters on Earth. Their economic repercussions along the supply and trade network also affect remote economies that are not directly affected. We here simulate possible global repercussions on consumption for the example case of Hurricane Sandy in the US (2012) using the shock-propagation model Acclimate. The modeled shock yields a global three-phase ripple: an initial production demand reduction and associated consumption price decrease, followed by a supply shortage with increasing prices, and finally a recovery phase. Regions with strong trade relations to the US experience strong magnitudes of the ripple. A dominating demand reduction or supply shortage leads to overall consumption gains or losses of a region, respectively. While finding these repercussions in historic data is challenging due to strong volatility of economic interactions, numerical models like ours can help to identify them by approaching the problem from an exploratory angle, isolating the effect of interest. For this, our model simulates the economic interactions of over 7000 regional economic sectors, interlinked through about 1.8 million trade relations. Under global warming, the wave-like structures of the economic response to major hurricanes like the one simulated here are likely to intensify and potentially overlap with other weather extremes. KW - supply chains KW - Hurricane Sandy KW - economic ripples KW - extreme weather KW - impacts KW - loss propagation KW - natural disasters Y1 - 2021 U6 - https://doi.org/10.1088/1748-9326/ac39c0 SN - 1748-9326 VL - 16 IS - 12 PB - IOP Publ. Ltd. CY - Bristol ER - TY - JOUR A1 - Mika, Sebastian A1 - Rätsch, Gunnar A1 - Weston, J. A1 - Schölkopf, B. A1 - Smola, Alexander J. A1 - Müller, Klaus-Robert T1 - Invariant feature extraction and classification in kernel spaces Y1 - 2000 ER - TY - JOUR A1 - Mileo, Alessandra A1 - Schaub, Torsten T1 - Qualitative constraint enforcement in advanced policy specification Y1 - 2007 ER - TY - JOUR A1 - Mileo, Alessandra A1 - Schaub, Torsten T1 - Extending ordered disjunctions for policy enforcement : preliminary report Y1 - 2006 UR - http://www.easychair.org/FLoC-06/PREFS-preproceedings.pdf ER - TY - JOUR A1 - Mileo, Alessandra A1 - Schaub, Torsten A1 - Merico, Davide A1 - Bisiani, Roberto T1 - Knowledge-based multi-criteria optimization to support indoor positioning JF - Annals of mathematics and artificial intelligence N2 - Indoor position estimation constitutes a central task in home-based assisted living environments. Such environments often rely on a heterogeneous collection of low-cost sensors whose diversity and lack of precision has to be compensated by advanced techniques for localization and tracking. Although there are well established quantitative methods in robotics and neighboring fields for addressing these problems, they lack advanced knowledge representation and reasoning capacities. Such capabilities are not only useful in dealing with heterogeneous and incomplete information but moreover they allow for a better inclusion of semantic information and more general homecare and patient-related knowledge. We address this problem and investigate how state-of-the-art localization and tracking methods can be combined with Answer Set Programming, as a popular knowledge representation and reasoning formalism. We report upon a case-study and provide a first experimental evaluation of knowledge-based position estimation both in a simulated as well as in a real setting. KW - Knowledge representation KW - Answer Set Programming KW - Wireless Sensor Networks KW - Localization KW - Tracking Y1 - 2011 U6 - https://doi.org/10.1007/s10472-011-9241-2 SN - 1012-2443 SN - 1573-7470 VL - 62 IS - 3-4 SP - 345 EP - 370 PB - Springer CY - Dordrecht ER - TY - JOUR A1 - Montavon, Gregoire A1 - Braun, Mikio L. A1 - Krüger, Tammo A1 - Müller, Klaus-Robert T1 - Analyzing local structure in Kernel-Based learning JF - IEEE signal processing magazine Y1 - 2013 U6 - https://doi.org/10.1109/MSP.2013.2249294 SN - 1053-5888 VL - 30 IS - 4 SP - 62 EP - 74 PB - Inst. of Electr. and Electronics Engineers CY - Piscataway ER -