• CMS workflow execution using intelligent job scheduling and data access strategies.

      Hasham, Khawar; Delgado Peris, Antonio; Anjum, Ashiq; Evans, Dave; Gowdy, Stephen; Hernandez, José M.; Huedo, Eduardo; Hufnagel, Dirk; van Lingen, Frank; McClatchey, Richard; et al. (IEEE, 2011-06)
      Complex scientific workflows can process large amounts of data using thousands of tasks. The turnaround times of these workflows are often affected by various latencies such as the resource discovery, scheduling and data access latencies for the individual workflow processes or actors. Minimizing these latencies will improve the overall execution time of a workflow and thus lead to a more efficient and robust processing environment. In this paper, we propose a pilot job concept that has intelligent data reuse and job execution strategies to minimize the scheduling, queuing, execution and data access latencies. The results have shown that significant improvements in the overall turnaround time of a workflow can be achieved with this approach. The proposed approach has been evaluated, first using the CMS Tier0 data processing workflow, and then simulating the workflows to evaluate its effectiveness in a controlled environment.
    • Polarimetric SAR image semantic segmentation with 3D discrete wavelet transform and Markov random field

      Bi, Haixia; Xu, Lin; Cao, Xiangyong; Xue, Yong; Xu, Zongben; University of Derby; University of Bristol; Shanghai Em-Data Technology Co., Ltd.; Xi’an Jiaotong University, Xi’an, China; University of Derby (IEEE, 2020-06-02)
      Polarimetric synthetic aperture radar (PolSAR) image segmentation is currently of great importance in image processing for remote sensing applications. However, it is a challenging task due to two main reasons. Firstly, the label information is difficult to acquire due to high annotation costs. Secondly, the speckle effect embedded in the PolSAR imaging process remarkably degrades the segmentation performance. To address these two issues, we present a contextual PolSAR image semantic segmentation method in this paper.With a newly defined channelwise consistent feature set as input, the three-dimensional discrete wavelet transform (3D-DWT) technique is employed to extract discriminative multi-scale features that are robust to speckle noise. Then Markov random field (MRF) is further applied to enforce label smoothness spatially during segmentation. By simultaneously utilizing 3D-DWT features and MRF priors for the first time, contextual information is fully integrated during the segmentation to ensure accurate and smooth segmentation. To demonstrate the effectiveness of the proposed method, we conduct extensive experiments on three real benchmark PolSAR image data sets. Experimental results indicate that the proposed method achieves promising segmentation accuracy and preferable spatial consistency using a minimal number of labeled pixels.