2,459 research outputs found

    Efficient Multi-way Theta-Join Processing Using MapReduce

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    Multi-way Theta-join queries are powerful in describing complex relations and therefore widely employed in real practices. However, existing solutions from traditional distributed and parallel databases for multi-way Theta-join queries cannot be easily extended to fit a shared-nothing distributed computing paradigm, which is proven to be able to support OLAP applications over immense data volumes. In this work, we study the problem of efficient processing of multi-way Theta-join queries using MapReduce from a cost-effective perspective. Although there have been some works using the (key,value) pair-based programming model to support join operations, efficient processing of multi-way Theta-join queries has never been fully explored. The substantial challenge lies in, given a number of processing units (that can run Map or Reduce tasks), mapping a multi-way Theta-join query to a number of MapReduce jobs and having them executed in a well scheduled sequence, such that the total processing time span is minimized. Our solution mainly includes two parts: 1) cost metrics for both single MapReduce job and a number of MapReduce jobs executed in a certain order; 2) the efficient execution of a chain-typed Theta-join with only one MapReduce job. Comparing with the query evaluation strategy proposed in [23] and the widely adopted Pig Latin and Hive SQL solutions, our method achieves significant improvement of the join processing efficiency.Comment: VLDB201

    J/ψ\psi production and suppression in nuclear collisions

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    In terms of a new QCD factorization formula for J/ψ\psi production, we calculate the J/ψ\psi suppression in nuclear collisions by including the multiple scattering between the pre-J/ψ\psi partonic states and the nuclear medium. We find agreement with all data on J/ψ\psi suppression in hadron-nucleus and nucleus-nucleus collisions, except a couple of points (the ``second drop'') at the highest ETE_T bins of the new NA50 data.Comment: Latex, 4 pages, to appear in the proceedings of Quark Matter 200

    MAMMOGRAM AND TOMOSYNTHESIS CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORKS

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    Mammography is the most widely used method of screening for breast cancer. Traditional mammography produces two-dimensional X-ray images, while advanced tomosynthesis mammography produces reconstructed three-dimensional images. Due to high variability in tumor size and shape, and the low signal-to-noise ratio inherent to mammography, manual classification yields a significant number of false positives, thereby contributing to an unnecessarily large number of biopsies performed to reduce the risk of misdiagnosis. Achieving high diagnostic accuracy requires expertise acquired over many years of experience as a radiologist. The convolutional neural network (CNN) is a popular deep-learning construct used in image classification. The convolutional process involves simplifying an image containing millions of pixels to a set of small feature maps, thereby reducing the input dimension while retaining the features that distinguish different classes of images. This technique has achieved significant advancements in large-set image-classification challenges in recent years. In this study, high-quality original mammograms and tomosynthesis were obtained with approval from an institutional review board. Different classifiers based on convolutional neural networks were built to classify the 2-D mammograms and 3-D tomosynthesis, and each classifier was evaluated based on its performance relative to truth values generated by a board of expert radiologists. The results show that CNNs have great potential for automatic breast cancer detection using mammograms and tomosynthesis

    Synthesis, Characterization And Application Of Pt Complexes With Pincer Ccc-Bis(Nhc) Ligands

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    The unique metallation/transmetallation route for the synthesis of CCC-bis(NHC) pincer ligand supported transition metal complexes was extended to Pt. Several platinum complexes were synthesized. The identity of these complexes was confirmed through multiple technologies, including nuclear magnetic resonance spectroscopy, mass spectroscopy, elemental analysis, thermal analysis, and X-ray crystallography. The intriguing photophysical and thermal properties of these complexes are reported. These complexes were believed to be congeners of materials for organic light emitting diodes (OLEDs)
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