The results of anomaly detection are sensitive to the choice of detection algorithms as they are specialized for different properties of data, especially for multidimensional data. Thus, it is vital to select the algorithm appropriately. To systematically select the algorithms, ensemble analysis techniques have been developed to support the assembly and comparison of heterogeneous algorithms. However, challenges remain due to the absence of the ground truth, interpretation, or evaluation of these anomaly detectors. In this paper, we present a visual analytics system named EnsembleLens that evaluates anomaly detection algorithms based on the ensemble analysis process. The system visualizes the ensemble processes and results by a set of novel visual designs and multiple coordinated contextual views to meet the requirements of correlation analysis, assessment and reasoning of anomaly detection algorithms. We also introduce an interactive analysis workflow that dynamically produces contextualized and interpretable data summaries that allow further refinements of exploration results based on user feedback. We demonstrate the effectiveness of EnsembleLens through a quantitative evaluation, three case studies with real-world data and interviews with two domain experts.

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In this paper, a full realization of the higher order method of moments (HMoM) with a parallel out-of-core LU solver on GPU/CPU platform is presented in detail, mainly including three parts: In the first part, both global-auxiliary table and local-auxiliary table are introduced for reducing a lot of tedious and repetitive calculations, and then a realization for GPU-oriented programming is proposed and optimized. In the second part, an overlapped grouping of all the curved quadrilaterals is proposed. With this scheme, all the submatrices can be efficiently generated one by one without wasting any calculations with the help of both the video memory and the host memory. In the third part, a GPU-based out-of-core algorithm for LU decomposition is proposed and further developed into a hybrid GPU/CPU algorithm. Numerical examples are provided to test the robustness of the proposed algorithm by comparison with the measurement and/or the traditional MoM with RWG basis functions, and to demonstrate the overall performance of the proposed algorithm by comparison with the existing algorithm for dealing with similar problems. The speedup ratio of the proposed algorithm for generating the HMoM matrix can achieve about from 7 to 12 compared with the GPU-based algorithm in literatures. Also compared with the 8-threaded CPU-based algorithm, the speedup ratio of the proposed algorithm for LU decomposition can exceed 13 for the single precision case and 7 for the double precision case. Index Terms-CUDA, GPU, high-order basis function, method of moments (MoM), OpenMP, out-of-core LU solver, parallel algorithm, speedup ratio. I. INTRODUCTION T HE method of moments (MoM) has gained wide applications in electromagnetic (EM) computations since Harrington published his monograph [1], especially after Rao-Wilton-Glisson (RWG) basis functions [2] (a type of local basis functions) were constructed. The early MoM mainly adopted such lower order basis functions as RWG basis functions, and hence, the number of unknowns is naturally larger, especially for electrically large problems. The MoM matrix is a dense matrix, leading to higher computational complexity and storage complexity. For this problem, many fast algorithms based on the MoM have emerged, such as the FMM [3], [4], the MLFMA [5], [6], the AIM [7], the P-FFT [8], the IE-FFT [9], the FG-FFT [10], the FGG-FG-FFT [11], and so on. These

Edge detection is a powerful tool used in geological features identification, such as faults and channels. In this paper, we present a new method for fault detection based on surface fitting algorithm, which is a popular method used in image edge detection. For each point in seismic volume, we will find a small neighborhood in a plane parallel to the local reflector with the help of dip estimation. The data in the neighborhood are then approximated by a bivariate cubic function, called facet model. The local gradient of the function is then being calculated latterly, named facet model attribute. To enhance the robustness of the output attribute and suppress noise, the gradient value are summed over a vertical window and normalized by energy. The dataset used in this paper is part of Netherlands offshore F3 block downloaded on the Opendtect website. To evaluate the performance of our method, we also calculate the dip guided Sobel attribute and variance attribute. By comparing with the two attributes, we found the result of our method is more accurate and shows more details in faults detection.