6,155 research outputs found
Vorticity, Gyroscopic precession, and Spin-Curvature Force
In investigating the relation between vorticity and gyroscopic precession, we
calculate the vorticity vector in Godel, Kerr, Lewis, Schwarzschild, Minkowski
metric and find out the vorticity vector of the specific observers is the
angular velocity of gyroscopic precession. Furthermore, considering space-time
torsion will flip the vorticity and spin-curvature force to opposite sign. This
result is very similar to the behavior of positive and negative helicity of
quantum spin in Stern-Gerlach force. It implies that the inclusion of torsion
will lead to analogous property of quantum spin even in classical treatment
A New Parameter Estimation Algorithm Based on Sub-band Dual Frequency Conjugate LVT
A new parameter estimation algorithm, known as Sub-band Dual Frequency
Conjugate LVT (SDFC-LVT), is proposed for the ground moving targets. This
algorithm first constructs two sub-band signals with different central
frequencies. After that, the two signals are shifted by different values in
frequency domain and a new signal is constructed by multiplying one with the
conjugate of the other. Finally, Keystone transform and LVT operation are
performed on the constructed signal to attain the estimates. The cross-term and
the performance of the proposed method are analyzed in detail. Since the
equivalent carrier frequency is reduced greatly, the proposed method is capable
of obtaining the accurate parameter estimates and resolving the problem of
ambiguity which invalidates Keystone transform. It is search-free and can
compensate the range walk of multiple targets simultaneously, thereby reducing
the computational burden. The effectiveness of the proposed method is
demonstrated by both simulated and real data.Comment: 27 pages, 7 figure
Parameter Estimation of Ground Moving Targets Based on SKT-DLVT Processing
It is well known that the motion of a ground moving target may induce the
range cell migration, spectrum spread and velocity ambiguity during the imaging
time, which makes the image smeared. To eliminate the influence of these
factors on image focusing, a novel method for parameter estimation of ground
moving targets, known as SKT-DLVT, is proposed in this paper. In this method,
the segmental keystone transform (SKT) is used to correct the range walk of
targets simultaneously, and a new transform, namely, Doppler Lv's transform
(LVT) is applied on the azimuth signal to estimate the parameters. Theoretical
analysis confirms that no interpolation is needed for the proposed method and
the targets can be well focused within limited searching range of the ambiguity
number. The proposed method is capable of obtaining the accurate parameter
estimates efficiently in the low signal-to-noise ratio (SNR) scenario with low
computational burden and memory cost, making it suitable to be applied in
memory-limited and real-time processing systems. The effectiveness of the
proposed method is demonstrated by both simulated and real data.Comment: 39 pages, 9 figure
An SU(3) Unified Model of Electroweak Interaction Using Generalized Yang-Mills Theory
Generalized Yang-Mills theory has a covariant derivative which contains both
vector and scalar gauge bosons. Based on this theory, we construct an SU(3)
unified model of weak and electromagnetic interactions. By using the NJL
mechanism, the symmetry breaking can be realized dynamically. The masses of W,Z
are obtained and interactions between various particles are the same as that of
Weinberg-Salam (WS) model. At the same time,the Weinberg angle can be given
Characteristics of Long Gamma-Ray bursts in the Comoving Frame
We compile a sample of 93 long gamma-ray bursts (GRBs) from Fermi satellite
and 131 from Konus-Wind, which have measured redshifts and well determined
spectra, and estimate their pseudo Lorentz factors () using the
tight -- correlation. The
statistical properties and pair correlations of temporal and spectral
parameters are studied in the observer frame, rest frame and comoving frame,
respectively. We find that the distributions of the duration, peak energy,
isotropic energy and luminosity in the different frames are basically
lognormal, and their distributions in the comoving frame are narrow, clustering
around s, keV, erg and erg
s, where the redshift evolution effect has been taken into account. We
also find that the values of are broadly distributed between few
tens and several hundreds with median values . We further analyze the
pair correlations of all the quantities, and well confirm
-, -,
- and - relations, and
find that the corresponding relations in the comoving frame do still exist, but
have large dispersions. This suggests not only the well-known spectrum-energy
relations are intrinsic correlations, but also the observed correlations are
governed by the Doppler effect. In addition, the peak energies of long GRBs are
independent of durations both in the rest frame and in the comoving frame. And
there is a weak anticorrelation between the peak energy and Lorentz factor.Comment: 42 pages, 14 figures, 7 tables, accepted for publication in Ap
A route to room temperature ferromagnetic ultrathin SrRuO films
Experimental efforts to stabilize ferromagnetism in ultrathin films of
transition metal oxides have so far failed, despite expectations based on
density functional theory (DFT) and DFT+U. Here, we investigate one of the most
promising materials, SrRuO, and include correlation effects beyond DFT by
means of dynamical mean field theory. In agreement with experiment we find an
intrinsic thickness limitation for metallic ferromagnetism in SrRuO thin
films. Indeed, we demonstrate that the realization of ultrathin ferromagnetic
films is out of reach of standard thin-film techniques. Proposing charge
carrier doping as a new route to manipulate thin films, we predict room
temperature ferromagnetism in electron-doped SrRuO ultra thin films.Comment: 5 pages, 3 figure
An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition
Skeleton-based action recognition is an important task that requires the
adequate understanding of movement characteristics of a human action from the
given skeleton sequence. Recent studies have shown that exploring spatial and
temporal features of the skeleton sequence is vital for this task.
Nevertheless, how to effectively extract discriminative spatial and temporal
features is still a challenging problem. In this paper, we propose a novel
Attention Enhanced Graph Convolutional LSTM Network (AGC-LSTM) for human action
recognition from skeleton data. The proposed AGC-LSTM can not only capture
discriminative features in spatial configuration and temporal dynamics but also
explore the co-occurrence relationship between spatial and temporal domains. We
also present a temporal hierarchical architecture to increases temporal
receptive fields of the top AGC-LSTM layer, which boosts the ability to learn
the high-level semantic representation and significantly reduces the
computation cost. Furthermore, to select discriminative spatial information,
the attention mechanism is employed to enhance information of key joints in
each AGC-LSTM layer. Experimental results on two datasets are provided: NTU
RGB+D dataset and Northwestern-UCLA dataset. The comparison results demonstrate
the effectiveness of our approach and show that our approach outperforms the
state-of-the-art methods on both datasets.Comment: Accepted by CVPR201
A GeV source in the direction of Supernova Remnant CTB 37B
Supernova remnants (SNRs) are the most attractive candidates for the
acceleration sites of Galactic cosmic rays. We report the detection of GeV
-ray emission with the Pass 8 events recorded by Fermi Large Area
Telescope (Fermi-LAT) in the vicinity of the shell type SNR CTB 37B that is
likely associated with the TeV ray source HESS J1713-381. The photon
spectrum of CTB 37B is consistent with a power-law with an index of
in the energy range of GeV, and the measured flux
connects smoothly with that of HESS J1713-381 at a few hundred GeV. No
significant spatial extension and time variation are detected. The
multi-wavelength data can be well fitted with either a leptonic model or a
hadronic one. However, parameters of both models suggest more efficient
particle acceleration than typical SNRs. Meanwhile, the X-ray and -ray
spectral properties of CTB 37B show that it is an interesting source bridging
young SNRs dominated by non-thermal emission and old SNRs interacting with
molecular clouds.Comment: 6 pages, 5 figures, 2 tables, published in ApJ, 817, 6
Deep Human Parsing with Active Template Regression
In this work, the human parsing task, namely decomposing a human image into
semantic fashion/body regions, is formulated as an Active Template Regression
(ATR) problem, where the normalized mask of each fashion/body item is expressed
as the linear combination of the learned mask templates, and then morphed to a
more precise mask with the active shape parameters, including position, scale
and visibility of each semantic region. The mask template coefficients and the
active shape parameters together can generate the human parsing results, and
are thus called the structure outputs for human parsing. The deep Convolutional
Neural Network (CNN) is utilized to build the end-to-end relation between the
input human image and the structure outputs for human parsing. More
specifically, the structure outputs are predicted by two separate networks. The
first CNN network is with max-pooling, and designed to predict the template
coefficients for each label mask, while the second CNN network is without
max-pooling to preserve sensitivity to label mask position and accurately
predict the active shape parameters. For a new image, the structure outputs of
the two networks are fused to generate the probability of each label for each
pixel, and super-pixel smoothing is finally used to refine the human parsing
result. Comprehensive evaluations on a large dataset well demonstrate the
significant superiority of the ATR framework over other state-of-the-arts for
human parsing. In particular, the F1-score reaches by our ATR
framework, significantly higher than based on the state-of-the-art
algorithm.Comment: This manuscript is the accepted version for IEEE Transactions on
Pattern Analysis and Machine Intelligence (TPAMI) 201
Protein token: a dynamic unit in protein interactions
In this study, we introduced a new unit, named "protein token", as a dynamic
protein structural unit for protein-protein interactions. Unlike the
conventional structural units, protein token is not based on the sequential or
spatial arrangement of residues, but comprises remote residues involved in
cooperative conformational changes during protein interactions. Application of
protein token on Ras GTPases revealed various tokens present in the
superfamily. Distinct token combinations were found in H-Ras interacting with
its various regulators and effectors, directing to a possible clue for the
multiplexer property of Ras superfamily. Thus, this protein token theory may
provide a new approach to study protein-protein interactions in broad
applications
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