17,790 research outputs found

    Advantages of the multinucleon transfer reactions based on 238U target for producing neutron-rich isotopes around N = 126

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    The mechanism of multinucleon transfer (MNT) reactions for producing neutron-rich heavy nuclei around N = 126 is investigated within two different theoretical frameworks: dinuclear system (DNS) model and isospin-dependent quantum molecular dynamics (IQMD) model. The effects of mass asymmetry relaxation, N=Z equilibration, and shell closures on production cross sections of neutron-rich heavy nuclei are investigated. For the first time, the advantages for producing neutron-rich heavy nuclei around N = 126 is found in MNT reactions based on 238U target. We propose the reactions with 238U target for producing unknown neutron-rich heavy nuclei around N = 126 in the future.Comment: 6 pages, 6 figure

    Efficient Processing of k Nearest Neighbor Joins using MapReduce

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    k nearest neighbor join (kNN join), designed to find k nearest neighbors from a dataset S for every object in another dataset R, is a primitive operation widely adopted by many data mining applications. As a combination of the k nearest neighbor query and the join operation, kNN join is an expensive operation. Given the increasing volume of data, it is difficult to perform a kNN join on a centralized machine efficiently. In this paper, we investigate how to perform kNN join using MapReduce which is a well-accepted framework for data-intensive applications over clusters of computers. In brief, the mappers cluster objects into groups; the reducers perform the kNN join on each group of objects separately. We design an effective mapping mechanism that exploits pruning rules for distance filtering, and hence reduces both the shuffling and computational costs. To reduce the shuffling cost, we propose two approximate algorithms to minimize the number of replicas. Extensive experiments on our in-house cluster demonstrate that our proposed methods are efficient, robust and scalable.Comment: VLDB201

    New Constructions of Zero-Correlation Zone Sequences

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    In this paper, we propose three classes of systematic approaches for constructing zero correlation zone (ZCZ) sequence families. In most cases, these approaches are capable of generating sequence families that achieve the upper bounds on the family size (KK) and the ZCZ width (TT) for a given sequence period (NN). Our approaches can produce various binary and polyphase ZCZ families with desired parameters (N,K,T)(N,K,T) and alphabet size. They also provide additional tradeoffs amongst the above four system parameters and are less constrained by the alphabet size. Furthermore, the constructed families have nested-like property that can be either decomposed or combined to constitute smaller or larger ZCZ sequence sets. We make detailed comparisons with related works and present some extended properties. For each approach, we provide examples to numerically illustrate the proposed construction procedure.Comment: 37 pages, submitted to IEEE Transactions on Information Theor

    The UL(3)×UR(3)U_L(3) \times U_R(3) Extended Nambu--Jona-Lasinio Model in Differential Regularization

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    We employ the method of differential regularization to calculate explicitly the one-loop effective action of a bosonized UL(3)×UR(3)U_L(3)\times U_R(3) extended Nambu--Jona-Lasinio model consisting of scalar, pseudoscalar, vector and axial vector fields.Comment: LaTeX, 17 page
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