6,638 research outputs found
Spin-polarized currents generated by magnetic Fe atomic chains
Fe-based devices are widely used in spintronics because of high
spin-polarization and magnetism. In this work, free-standing Fe atomic chains
were proposed to be used as the thinnest wires to generate spin-polarized
currents due to the spin-polarized energy bands. By ab initio calculations, the
zigzag structure was found more stable than the wide-angle zigzag structure and
has higher ratio of spin-up and spin-down currents. By our theoretical
prediction, Fe atomic chains have sufficiently long thermal lifetime only at
T<=150 K, while C atomic chains are very stable even at T=1000 K. This result
means that the spintronic devices based on Fe chains could only work at low
temperature. A system constructed by a short Fe chain sandwiched between two
graphene electrodes was proposed as a spin-polarized current generator, while a
C chain does not have such property. The present work may be instructive and
meaningful to further practical applications based on recent technical
development on the preparation of metal atomic chains [Proc. Natl. Acad. Sci.
U.S.A. 107, 9055 (2010)].Comment: Nanotechnology (2014
On Degrees of Freedom of Projection Estimators with Applications to Multivariate Nonparametric Regression
In this paper, we consider the nonparametric regression problem with
multivariate predictors. We provide a characterization of the degrees of
freedom and divergence for estimators of the unknown regression function, which
are obtained as outputs of linearly constrained quadratic optimization
procedures, namely, minimizers of the least squares criterion with linear
constraints and/or quadratic penalties. As special cases of our results, we
derive explicit expressions for the degrees of freedom in many nonparametric
regression problems, e.g., bounded isotonic regression, multivariate
(penalized) convex regression, and additive total variation regularization. Our
theory also yields, as special cases, known results on the degrees of freedom
of many well-studied estimators in the statistics literature, such as ridge
regression, Lasso and generalized Lasso. Our results can be readily used to
choose the tuning parameter(s) involved in the estimation procedure by
minimizing the Stein's unbiased risk estimate. As a by-product of our analysis
we derive an interesting connection between bounded isotonic regression and
isotonic regression on a general partially ordered set, which is of independent
interest.Comment: 72 pages, 7 figures, Journal of the American Statistical Association
(Theory and Methods), 201
Community Detection by -penalized Graph Laplacian
Community detection in network analysis aims at partitioning nodes in a
network into disjoint communities. Most currently available algorithms
assume that is known, but choosing a correct is generally very
difficult for real networks. In addition, many real networks contain outlier
nodes not belonging to any community, but currently very few algorithm can
handle networks with outliers. In this paper, we propose a novel model free
tightness criterion and an efficient algorithm to maximize this criterion for
community detection. This tightness criterion is closely related with the graph
Laplacian with penalty. Unlike most community detection methods, our
method does not require a known and can properly detect communities in
networks with outliers.
Both theoretical and numerical properties of the method are analyzed. The
theoretical result guarantees that, under the degree corrected stochastic block
model, even for networks with outliers, the maximizer of the tightness
criterion can extract communities with small misclassification rates even when
the number of communities grows to infinity as the network size grows.
Simulation study shows that the proposed method can recover true communities
more accurately than other methods. Applications to a college football data and
a yeast protein-protein interaction data also reveal that the proposed method
performs significantly better.Comment: 40 pages, 15 Postscript figure
Galaxy formation with cold gas accretion and evolving stellar initial mass function
The evolution of the galaxy stellar mass function is especially useful to
test the current model of galaxy formation. Observational data have revealed a
few inconsistencies with predictions from the model. For
example, most massive galaxies have already been observed at very high
redshifts, and they have experienced only mild evolution since then. In
conflict with this, semi-analytical models of galaxy formation predict an
insufficient number of massive galaxies at high redshift and a rapid evolution
between redshift 1 and 0 . In addition, there is a strong correlation between
star formation rate and stellar mass for star-forming galaxies, which can be
roughly reproduced with the model, but with a normalization that is too low at
high redshift. Furthermore, the stellar mass density obtained from the integral
of the cosmic star formation history is higher than the measured one by a
factor of 2. In this paper, we study these issues using a semi-analytical model
that includes: 1) cold gas accretion in massive halos at high redshift; 2)
tidal stripping of stellar mass from satellite galaxies; and 3) an evolving
stellar initial mass function (bottom-light) with a higher gas recycle
fraction. Our results show that the combined effects from 1) and 2) can predict
sufficiently massive galaxies at high redshifts and reproduce their mild
evolution at low redshift, While the combined effects of 1) and 3) can
reproduce the correlation between star formation rate and stellar mass for
star-forming galaxies across wide range of redshifts. A bottom-light/top-heavy
stellar IMF could partly resolve the conflict between the stellar mass density
and cosmic star formation history.Comment: 9 pages, 7 figures. Accepted for publication in Ap
Predicting the chemical stability of monatomic chains
A simple model for evaluating the thermal atomic transfer rates in
nanosystems [EPL 94, 40002 (2011)] was developed to predict the chemical
reaction rates of nanosystems with small gas molecules. The accuracy of the
model was verified by MD simulations for molecular adsorption and desorption on
a monatomic chain. By the prediction, a monatomic carbon chain should survive
for 120 years in the ambient of 1 atm O2 at room temperature, and it is very
invulnerable to N2, H2O, NO2, CO and CO2, while a monatomic gold chain quickly
ruptures in vacuum. It is worth noting that since the model can be easily
applied via common ab initio calculations, it could be widely used in the
prediction of chemical stability of nanosystems
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