Analyzing Transatlantic Network Traffic over Scientific Data Caches

Large scientific collaborations often share huge volumes of data around the world. Consequently a significant amount of network bandwidth is needed for data replication and data access. Users in the same region may possibly share resources as well as data, especially when they are working on related topics with similar datasets. In this work, we study the network traffic patterns and resource utilization for scientific data caches connecting European networks to the US. We explore the efficiency of resource utilization, especially for network traffic which consists mostly of transatlantic data transfers, and the potential for having more caching node deployments. Our study shows that these data caches reduced network traffic volume by 97% during the study period. This demonstrates that such caching nodes are effective in reducing wide-area network traffic

A Comparative Study Among Pattern Classifiers In Interactive Image Segmentation

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Edition of natural images usually asks for considerable user involvement, being segmentation one of the main challenges. This paper describes an unified graph-based framework for fast, precise and accurate interactive image segmentation. The method divides segmentation into object recognition, enhancement and extraction. Recognition is done by the user when markers are selected inside and outside the object. Enhancement increases the dissimilarities between object and background and Extraction separates them. Enhancement is done by a fuzzy pixel classifier and it has a great impact in the number of markers required for extraction. In view of minimizing user involvement, we focus this paper on a comparative study among popular classifiers for enhancement, conducting experiments with several natural images and seven users. © 2009 IEEE.268275Petrobras,CNPq,CAPES,INCTMat,FAPERJConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Spina, T.V., Montoya-Zegarra, J.A., Falcão, A.X., Miranda, P.A.V., Fast interactive segmentation of natural images using the image foresting transform (2009) DSP (International Conference on Digital Signal Processing), , Santorini, Greece: IEEE, JulFalcão, A.X., Udupa, J.K., Miyazawa, F.K., An ultra-fast user-steered image segmentation paradigm: Live-wire-on-the-fly (2000) IEEE Trans. on Medical Imaging, 19 (1), pp. 55-62Falcão, A.X., Stolfi, J., Lotufo, R.A., The image foresting transform: Theory, algorithms, and applications (2004) IEEE Trans. on Pattern Analysis and Machine Intelligence, 26 (1), pp. 19-29Falcão, A.X., Bergo, F.P.G., Interactive volume segmentation with differential image foresting transforms (2004) IEEE Trans. on Medical Imaging, 23 (9), pp. 1100-1108J. P. Papa, A. X. Falcão, C. T. N. Suzuki, and N. D. A. Mascarenhas, A discrete approach for supervised pattern recognition, in 12th International Workshop on Combinatorial Image Analysis, 4958. LNCS Springer Berlin/Heidelberg, 2008, pp. 136-147Portilla, J., Simoncelli, E.P., A parametric texture model based on joint statistics of complex wavelet coefficients (2000) Intl. Journal of Computer Vision, 40 (1), pp. 49-70Meyer, F., Levelings, image simplification filters for segmentation (2004) Journal of Mathematical Imaging and Vision, 20 (1-2), pp. 59-72J. P. Papa, A. X. Falcão, C. T. N. Suzuki, and N. D. A. Mascarenhas, A discrete approach for supervised pattern recognition, in Proc. of the 12th Intl. Workshop on Combinatorial Image Analysis, LNCS 4958. Buffalo, NY, USA: Springer, Apr 7th-9th 2008, pp. 136-147C.-W. Hsu, C.-C. Chang, and C.-J. Lin, A practical guide to support vector classification, Department of Computer Science, National Taiwan University, Tech. Rep., 2009. [Online]. Available: http://www.csie.ntu.edu.tw/ ~cjlin/papers/guide/guide.pdfChang, C.C., Lin, C.J., (2001) LIBSVM: A library for support vector machines, , http://www.csie.ntu.edu.tw/~cjlin/libsvm, Online, AvailableHaykin, S., (1999) Neural Networks, A Comprehensive foundation, , Prenticer Hall, IncHecht-Nielsen, R., (1989) Neurocomputing, , Boston, MA, USA: Addison-Wesley Longman Publishing Co, IncMiranda, P.A.V., Falcão, A.X., Rocha, A., Bergo, F.P.G., Object delineation by K-connected components (2008) EURASIP Journal on Advances in Signal Processing, pp. 1-14. , doi: 10.1155/2008/467928Bradski, G., The OpenCV Library (2000) Dr. Dobbs Journal of Software ToolsMartin, D., Fowlkes, C., Tal, D., Malik, J., Berkeley segmentation dataset and benchmark, , http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping, Online, AvailableRother, C., Kolmogorov, V., Blake, A., Brown, M., Image and video editing: Grabcut, , http://research.microsoft.com/enus/um/cambridge/projects/visionimagevideoediting/segmentation/grabcut.htm, Online, Availablevan Rijsbergen, C., (1979) Information retrieval, , 2nd ed. London: Wiley Inter-scienc