13,366 research outputs found
Simultaneous pi/2 rotation of two spin species of different gyromagnetic ratios
We examine the characteristics of the pi/2 pulse for simultaneously rotating
two spin species of different gyromagnetic ratios with the same sign. For a
pi/2 pulse using a rotating magnetic field, we derive the equation relating the
frequency and strength of the pulse to the gyromagnetic ratios of the two
particles and the strength of the constant holding field. For a pi/2 pulse
using a linear oscillatory magnetic field, we obtain the solutions numerically,
and compare them with the solutions for the rotating pi/2 pulse. Application of
this analysis to the specific case of rotating neutrons and 3He atoms
simultaneously with a pi/2 pulse, proposed for a neutron electric dipole moment
experiment, is also presented
Warm asymmetric quark matter and proto-quark stars within the confined-isospin-density-dependent mass model
We extend the confined-isospin-density-dependent mass (CIDDM) model to
include temperature dependence of the equivalent mass for quarks. Within the
CIDDM model, we study the equation of state (EOS) for -equilibrium quark
matter, quark symmetry energy, quark symmetry free energy, and the properties
of quark stars at finite temperature. We find that including the temperature
dependence of the equivalent mass can significantly influence the properties of
the strange quark matter (SQM) as well as the quark symmetry energy, the quark
symmetry free energy, and the maximum mass of quark stars at finite
temperature. The mass-radius relations for different stages of the proto-quark
stars (PQSs) along the star evolution are analyzed. Our results indicate that
the heating (cooling) process for PQSs will increase (decrease) the maximum
mass within CIDDM model by including temperature dependence of the equivalent
mass for quarks.Comment: 9 pages, 5 figures. Presentation improved and discussions added.
Accepted version to appear in PR
Isovector properties of quark matter and quark stars in an isospin-dependent confining model
The confining quark matter (CQM) model, in which the confinement and
asymptotic freedom are modeled via the Richardson potential for quark-quark
vector interaction and the chiral symmetry restoration at high density is
described by the density dependent quark mass, is extended to include isospin
dependence of the quark mass. Within this extended isospin-dependent confining
quark matter (ICQM) model, we study the properties of strange quark matter and
quark stars. We find that including isospin dependence of the quark mass can
significantly influence the quark matter symmetry energy, the stability of
strange quark matter and the mass-radius relation of quark stars. In
particular, we demonstrate although the recently discovered large mass pulsars
PSR J1614.2230 and PSR J0348+0432 with masses around two times solar mass
() cannot be quark stars within the original CQM model, they can be
well described by quark stars in the ICQM model if the isospin dependence of
quark mass is strong enough so that the quark matter symmetry energy is about
four times that of a free quark gas. We also discuss the effects of the density
dependence of quark mass on the properties of quark stars. Our results indicate
that the heavy quark stars with mass around (if exist) can put
strong constraints on isospin and density dependence of the quark mass as well
as the quark matter symmetry energy.Comment: 10 pages, 6 figures, 2 tables. Presentation improved, 2 tables and
discussions added. Accepted version to appear in PR
Quark matter symmetry energy and quark stars
We extend the confined-density-dependent-mass (CDDM) model to include isospin
dependence of the equivalent quark mass. Within the
confined-isospin-density-dependent-mass (CIDDM) model, we study the quark
matter symmetry energy, the stability of strange quark matter, and the
properties of quark stars. We find that including isospin dependence of the
equivalent quark mass can significantly influence the quark matter symmetry
energy as well as the properties of strange quark matter and quark stars. While
the recently discovered large mass pulsars PSR J1614-2230 and PSR J0348+0432
with masses around cannot be quark stars within the CDDM model,
they can be well described by quark stars in the CIDDM model. In particular,
our results indicate that the two-flavor - quark matter symmetry energy
should be at least about twice that of a free quark gas or normal quark matter
within conventional Nambu-Jona-Lasinio model in order to describe the PSR
J1614-2230 and PSR J0348+0432 as quark stars.Comment: 13 pages, 8 figures, 1 table. Results with varied quark mass scaling
parameter z and discussions added. Accepted version to appear in Ap
Implicit Regularization via Hadamard Product Over-Parametrization in High-Dimensional Linear Regression
We consider Hadamard product parametrization as a change-of-variable
(over-parametrization) technique for solving least square problems in the
context of linear regression. Despite the non-convexity and exponentially many
saddle points induced by the change-of-variable, we show that under certain
conditions, this over-parametrization leads to implicit regularization: if we
directly apply gradient descent to the residual sum of squares with
sufficiently small initial values, then under proper early stopping rule, the
iterates converge to a nearly sparse rate-optimal solution with relatively
better accuracy than explicit regularized approaches. In particular, the
resulting estimator does not suffer from extra bias due to explicit penalties,
and can achieve the parametric root- rate (independent of the dimension)
under proper conditions on the signal-to-noise ratio. We perform simulations to
compare our methods with high dimensional linear regression with explicit
regularizations. Our results illustrate advantages of using implicit
regularization via gradient descent after over-parametrization in sparse vector
estimation
Explicit calculation on two-loop correction to the chiral magnetic effect with NJL model
Chiral Magnetic Effect(CME) is usually believed not receiving higher order
corrections due to the non-renormalization of AVV triangle diagram in the
framework of quantum field theory. However, the CME-relevant triangle, which is
obtained by expanding the current-current correlation requires zero momentum on
the axial vertex, is not equivalent to the general AVV triangle when taking the
zero-momentum limit owing to the infrared problem on the axial vertex.
Therefore, it is still significant to check if there exists perturbative higher
order corrections to the current-current correlation. In this paper, we
explicitly calculate the two-loop corrections of CME within NJL model with
Chern-Simons term which ensures a consistent . The result shows the
two-loop corrections to the CME conductivity are zero, which confirms the
non-renomalization of CME conductivity.Comment: 7 pages, 3 figure
Quark stars under strong magnetic fields
Within the confined isospin- and density-dependent mass model, we study the
properties of strange quark matter (SQM) and quark stars (QSs) under strong
magnetic fields. The equation of state of SQM under a constant magnetic field
is obtained self-consistently and the pressure perpendicular to the magnetic
field is shown to be larger than that parallel to the magnetic field, implying
that the properties of magnetized QSs generally depend on both the strength and
the orientation of the magnetic fields distributed inside the stars. Using a
density-dependent magnetic field profile which is introduced to mimic the
magnetic field strength distribution in a star, we study the properties of
static spherical QSs by assuming two extreme cases for the magnetic field
orientation in the stars, i.e., the radial orientation in which the local
magnetic fields are along the radial direction and the transverse orientation
in which the local magnetic fields are randomly oriented but perpendicular to
the radial direction. Our results indicate that including the magnetic fields
with radial (transverse) orientation can significantly decrease (increase) the
maximum mass of QSs, demonstrating the importance of the magnetic field
orientation inside the magnetized compact stars.Comment: 9 pages, 4 figures. Discussions added. Accepted version to appear in
PR
Scene Parsing via Dense Recurrent Neural Networks with Attentional Selection
Recurrent neural networks (RNNs) have shown the ability to improve scene
parsing through capturing long-range dependencies among image units. In this
paper, we propose dense RNNs for scene labeling by exploring various long-range
semantic dependencies among image units. Different from existing RNN based
approaches, our dense RNNs are able to capture richer contextual dependencies
for each image unit by enabling immediate connections between each pair of
image units, which significantly enhances their discriminative power. Besides,
to select relevant dependencies and meanwhile to restrain irrelevant ones for
each unit from dense connections, we introduce an attention model into dense
RNNs. The attention model allows automatically assigning more importance to
helpful dependencies while less weight to unconcerned dependencies. Integrating
with convolutional neural networks (CNNs), we develop an end-to-end scene
labeling system. Extensive experiments on three large-scale benchmarks
demonstrate that the proposed approach can improve the baselines by large
margins and outperform other state-of-the-art algorithms.Comment: 10 pages. arXiv admin note: substantial text overlap with
arXiv:1801.0683
Clustered Object Detection in Aerial Images
Detecting objects in aerial images is challenging for at least two reasons:
(1) target objects like pedestrians are very small in pixels, making them
hardly distinguished from surrounding background; and (2) targets are in
general sparsely and non-uniformly distributed, making the detection very
inefficient. In this paper, we address both issues inspired by observing that
these targets are often clustered. In particular, we propose a Clustered
Detection (ClusDet) network that unifies object clustering and detection in an
end-to-end framework. The key components in ClusDet include a cluster proposal
sub-network (CPNet), a scale estimation sub-network (ScaleNet), and a dedicated
detection network (DetecNet). Given an input image, CPNet produces object
cluster regions and ScaleNet estimates object scales for these regions. Then,
each scale-normalized cluster region is fed into DetecNet for object detection.
ClusDet has several advantages over previous solutions: (1) it greatly reduces
the number of chips for final object detection and hence achieves high running
time efficiency, (2) the cluster-based scale estimation is more accurate than
previously used single-object based ones, hence effectively improves the
detection for small objects, and (3) the final DetecNet is dedicated for
clustered regions and implicitly models the prior context information so as to
boost detection accuracy. The proposed method is tested on three popular aerial
image datasets including VisDrone, UAVDT and DOTA. In all experiments, ClusDet
achieves promising performance in comparison with state-of-the-art detectors.
Code will be available in \url{https://github.com/fyangneil}
Motif-based Rule Discovery for Predicting Real-valued Time Series
Time series prediction is of great significance in many applications and has
attracted extensive attention from the data mining community. Existing work
suggests that for many problems, the shape in the current time series may
correlate an upcoming shape in the same or another series. Therefore, it is a
promising strategy to associate two recurring patterns as a rule's antecedent
and consequent: the occurrence of the antecedent can foretell the occurrence of
the consequent, and the learned shape of consequent will give accurate
predictions. Earlier work employs symbolization methods, but the symbolized
representation maintains too little information of the original series to mine
valid rules. The state-of-the-art work, though directly manipulating the
series, fails to segment the series precisely for seeking
antecedents/consequents, resulting in inaccurate rules in common scenarios. In
this paper, we propose a novel motif-based rule discovery method, which
utilizes motif discovery to accurately extract frequently occurring consecutive
subsequences, i.e. motifs, as antecedents/consequents. It then investigates the
underlying relationships between motifs by matching motifs as rule candidates
and ranking them based on the similarities. Experimental results on real open
datasets show that the proposed approach outperforms the baseline method by
23.9%. Furthermore, it extends the applicability from single time series to
multiple ones
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