The Asymptotic Method Developed from Weak Turbulent Theory and the Nonlinear Permeability and Damping Rate in QGP

With asymptotic method developed from weak turbulent theory, the kinetic equations for QGP are expanded in fluctuation field potential $A^T_\mu$. Considering the second-order and third-order currents, we derive the nonlinear permeability tensor function from Yang-Mills field equation, and find that the third-order current is more important in turbulent theory. The nonlinear permeability formulae for longitudinal color oscillations show that the non-Abelian effects are more important than the Abelian-like effects. To compare with other works, we give the numerical result of the damping rate for the modes with zero wave vector.Comment: 16page

Non-Abelian Kubo Formula and the Multiple Time-Scale Method

The non-Abelian Kubo formula is derived from the kinetic theory. That expression is compared with the one obtained using the eikonal for a Chern-Simons theory. The multiple time-scale method is used to solve the non-Abelian Kubo formula, and the damping rate for longitudinal color waves is computed.Comment: 18 pages, latex , to be pblished in Ann. Phys.(N,Y)(1996

Efficient Multi-way Theta-Join Processing Using MapReduce

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

In terms of a new QCD factorization formula for J/$\psi$ production, we calculate the J/$\psi$ suppression in nuclear collisions by including the multiple scattering between the pre-J/$\psi$ partonic states and the nuclear medium. We find agreement with all data on J/$\psi$ suppression in hadron-nucleus and nucleus-nucleus collisions, except a couple of points (the ``second drop'') at the highest $E_T$ 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

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