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 (${\Gamma_0}$) using the tight ${L_{{\rm{iso}}}}$-${E_{\rm p}}$-${\Gamma_0}$ 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 $T'_{\rm 90}\sim 4000$ s, $E'_{\rm p,c}\sim 0.7$ keV, $E'_{\rm iso,c} \sim 8\times10^{49}$ erg and $L'_{\rm iso,c}\sim 2.5\times10^{46}$ erg s$^{-1}$, where the redshift evolution effect has been taken into account. We also find that the values of ${\Gamma_0}$ are broadly distributed between few tens and several hundreds with median values $\sim 270$. We further analyze the pair correlations of all the quantities, and well confirm ${E_{{\rm{iso}}}}$-${E_{\rm p}}$, ${L_{{\rm{iso}}}}$-${E_{\rm p}}$, ${L_{{\rm{iso}}}}$-${\Gamma_0}$ and $E_{\rm{iso}}$-${\Gamma_0}$ 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 SrRuO3_3 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$_3$, 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$_3$ 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$_3$ 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 $\gamma$-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 $\gamma-$ray source HESS J1713-381. The photon spectrum of CTB 37B is consistent with a power-law with an index of $1.89\pm0.08$ in the energy range of $0.5-500$ 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 $\gamma$-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 $64.38\%$ by our ATR framework, significantly higher than $44.76\%$ 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