17,790 research outputs found
Advantages of the multinucleon transfer reactions based on 238U target for producing neutron-rich isotopes around N = 126
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
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
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 () and the ZCZ width () for a given
sequence period ().
Our approaches can produce various binary and polyphase ZCZ families with
desired parameters 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 Extended Nambu--Jona-Lasinio Model in Differential Regularization
We employ the method of differential regularization to calculate explicitly
the one-loop effective action of a bosonized extended
Nambu--Jona-Lasinio model consisting of scalar, pseudoscalar, vector and axial
vector fields.Comment: LaTeX, 17 page
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