arXiv subjects

Dan Yang

Publications and source records attributed to Dan Yang.

72 records · Page 4Linked to original sources

arXiv2019

Possible $S$-wave $ND^{()}$ and $N\bar B^{()}$ bound states in a chiral quark model

$S$-wave bound-states composed of a nucleon($N$) and a heavy meson ($D$, $D^*$, $\bar{B}$ or $\bar{B}^*$) are investigated in both the chiral SU(3) quark model and the extended chiral SU(3) quark model by solving the resonating group method equation. The results reveal that the $ND$ and $ND^*$ interactions in the corresponding relative $S$-wave states are attractive, arising mainly from one boson exchange processes between light quarks. It is shown that these attractions are strong enough to form six $ND$ and $ND^*$ $S$-wave bound states in the extended chiral SU(3) quark model with the binding energies in the range of $3-45$ MeV, and three $S$-wave bound states within the chiral SU(3) quark model with binding energies of $2-8$ MeV. In particular, the experimentally observed $Σ_c(2800)$ is interpreted to be most likely an $S-$wave $ND$ state with the total isopsin $I=0$ and spin-parity $J^P=1/2^-$, while $Λ_c(2940)^+$ as an $S-$wave $ND^*$ state with $I=0$ and $J^P=3/2^-$. Further information on the $ND$ and $ND^*$ interactions in the (unbound) scattering kinematics are obtained from the corresponding $S$-wave phase shifts. The $N\bar{B}$ and $N\bar{B}^*$ systems are also investigated within the present two models and some $S-$wave bound states with binding energies in the range of $1-60$ MeV are predicted in these systems: six (in total) within the extended chiral SU(3) quark model and, four, within the chiral SU(3) quark model.

nucl-th↗

arXiv2019

Regional economic status inference from information flow and talent mobility

Novel data has been leveraged to estimate socioeconomic status in a timely manner, however, direct comparison on the use of social relations and talent movements remains rare. In this letter, we estimate the regional economic status based on the structural features of the two networks. One is the online information flow network built on the following relations on social media, and the other is the offline talent mobility network built on the anonymized resume data of job seekers with higher education. We find that while the structural features of both networks are relevant to economic status, the talent mobility network in a relatively smaller size exhibits a stronger predictive power for the gross domestic product (GDP). In particular, a composite index of structural features can explain up to about 84% of the variance in GDP. The result suggests future socioeconomic studies to pay more attention to the cost-effective talent mobility data.

physics.soc-ph↗

arXiv2018

Signal and noise in helioseismic holography

Helioseismic holography is an imaging technique used to study heterogeneities and flows in the solar interior from observations of solar oscillations at the surface. Holograms contain noise due to the stochastic nature of solar oscillations. We provide a theoretical framework for modeling signal and noise in Porter-Bojarski helioseismic holography. The wave equation may be recast into a Helmholtz-like equation, so as to connect with the acoustics literature and define the holography Green's function in a meaningful way. Sources of wave excitation are assumed to be stationary, horizontally homogeneous, and spatially uncorrelated. Using the first Born approximation we calculate holograms in the presence of perturbations in sound-speed, density, flows, and source covariance, as well as the noise level as a function of position. This work is a direct extension of the methods used in time-distance helioseismology to model signal and noise. To illustrate the theory, we compute the hologram intensity numerically for a buried sound-speed perturbation at different depths in the solar interior. The reference Green's function is obtained for a spherically-symmetric solar model using a finite-element solver in the frequency domain. Below the pupil area on the surface, we find that the spatial resolution of the hologram intensity is very close to half the local wavelength. For a sound-speed perturbation of size comparable to the local spatial resolution, the signal-to-noise ratio is approximately constant with depth. Averaging the hologram intensity over a number $N$ of frequencies above 3 mHz increases the signal-to-noise ratio by a factor nearly equal to the square root of $N$. This may not be the case at lower frequencies, where large variations in the holographic signal are due to the individual contributions of the long-lived modes of oscillation.

astro-ph.SR↗

arXiv2018

Ghost Images in Helioseismic Holography? Toy Models in a Uniform Medium

Helioseismic holography is a powerful technique used to probe the solar interior based on estimations of the 3D wavefield. Porter--Bojarski holography, which is a well-established method used in acoustics to recover sources and scatterers in 3D, is also an estimation of the wavefield, and hence it has the potential to be applied to helioseismology. Here we present a proof of concept study, where we compare helioseismic holography and Porter--Bojarski holography under the assumption that the waves propagate in a homogeneous medium. We consider the problem of locating a point source of wave excitation inside a sphere. Under these assumptions, we find that the two imaging methods have the same capability of locating the source, with the exception that helioseismic holography suffers from "ghost images" (i.e., artificial peaks away from the source location). We conclude that Porter--Bojarski holography may improve the current method used in helioseismology.

astro-ph.SR↗

arXiv2017

Minimal surfaces in a unit sphere pinched by intrinsic curvature and normal curvature

We establish a nice orthonormal frame field on a closed surface minimally immersed in a unit sphere $S^{n}$, under which the shape operators take very simple forms. Using this frame field, we obtain an interesting property $K+K^{N}=1$ for the Gauss curvature $K$ and the normal curvature $K^{N}$ if the Gauss curvature is positive. Moreover, using this property we obtain the pinching on the intrinsic curvature and normal curvature, the pinching on the normal curvature, respectively.

math.DG↗

arXiv2017

Bounds of memory strength for power-law series

Many time series produced by complex systems are empirically found to follow power-law distributions with different exponents $α$. By permuting the independently drawn samples from a power-law distribution, we present non-trivial bounds on the memory strength (1st-order autocorrelation) as a function of $α$, which are markedly different from the ordinary $\pm 1$ bounds for Gaussian or uniform distributions. When $1 < α\leq 3$, as $α$ grows bigger, the upper bound increases from 0 to +1 while the lower bound remains 0; when $α> 3$, the upper bound remains +1 while the lower bound descends below 0. Theoretical bounds agree well with numerical simulations. Based on the posts on Twitter, ratings of MovieLens, calling records of the mobile operator Orange, and browsing behavior of Taobao, we find that empirical power-law distributed data produced by human activities obey such constraints. The present findings explain some observed constraints in bursty time series and scale-free networks, and challenge the validity of measures like autocorrelation and assortativity coefficient in heterogeneous systems.

physics.data-an↗

arXiv2016

Regression-based Hypergraph Learning for Image Clustering and Classification

Inspired by the recently remarkable successes of Sparse Representation (SR), Collaborative Representation (CR) and sparse graph, we present a novel hypergraph model named Regression-based Hypergraph (RH) which utilizes the regression models to construct the high quality hypergraphs. Moreover, we plug RH into two conventional hypergraph learning frameworks, namely hypergraph spectral clustering and hypergraph transduction, to present Regression-based Hypergraph Spectral Clustering (RHSC) and Regression-based Hypergraph Transduction (RHT) models for addressing the image clustering and classification issues. Sparse Representation and Collaborative Representation are employed to instantiate two RH instances and their RHSC and RHT algorithms. The experimental results on six popular image databases demonstrate that the proposed RH learning algorithms achieve promising image clustering and classification performances, and also validate that RH can inherit the desirable properties from both hypergraph models and regression models.

cs.CV↗

arXiv2016

Lower bound of assortativity coefficient in scale-free networks

The degree-degree correlation is important in understanding the structural organization of a network and the dynamics upon a network. Such correlation is usually measured by the assortativity coefficient $r$, with natural bounds $r \in [-1,1]$. For scale-free networks with power-law degree distribution $p(k) \sim k^{-γ}$, we analytically obtain the lower bound of assortativity coefficient in the limit of large network size, which is not -1 but dependent on the power-law exponent $γ$. This work challenges the validation of assortativity coefficient in heterogeneous networks, suggesting that one cannot judge whether a network is positively or negatively correlated just by looking at its assortativity coefficient.

physics.soc-ph↗

arXiv2015

ND^() and NB^() interactions in a chiral quark model

ND and ND^* interactions become a hot topic after the observation of new charmed hadrons Σ_c(2800) and Λ_c(2940)^+. In this letter, we have preliminary investigated S-wave ND and ND^* interactions with possible quantum numbers in the chiral SU(3) quark model and the extended chiral SU(3) quark model by solving the resonating group method equation. The numerical results show that the interactions between N and D or N and D^* are both attractive, which are mainly from σexchanges between light quarks. Further bound-state studies indicate the attractions are strong enough to form ND or ND^* molecules, except for (ND)_{J=3/2} and (ND^*)_{J=3/2} in the chiral SU(3) quark model. In consequence ND system with J=1/2 and ND^* system with J=3/2 in the extended SU(3) quark model could correspond to the observed Σ_c(2800) and Λ_c(2940)^+, respectively. Naturally, the same method can be applied to research NB and NB^* interactions, and similar conclusions obtained, i.e. NB and NB^* attractive forces may achieve bound states, except for (NB^*)_{J=3/2} in the chiral SU(3) quark model. Meanwhile, S partial wave phase shifts of ND^{(*)} and NB^{(*)} elastic scattering are illustrated, which are qualitatively consistent with results from bound state problem.

nucl-th↗

arXiv2015

Learning Hypergraph-regularized Attribute Predictors

We present a novel attribute learning framework named Hypergraph-based Attribute Predictor (HAP). In HAP, a hypergraph is leveraged to depict the attribute relations in the data. Then the attribute prediction problem is casted as a regularized hypergraph cut problem in which HAP jointly learns a collection of attribute projections from the feature space to a hypergraph embedding space aligned with the attribute space. The learned projections directly act as attribute classifiers (linear and kernelized). This formulation leads to a very efficient approach. By considering our model as a multi-graph cut task, our framework can flexibly incorporate other available information, in particular class label. We apply our approach to attribute prediction, Zero-shot and $N$-shot learning tasks. The results on AWA, USAA and CUB databases demonstrate the value of our methods in comparison with the state-of-the-art approaches.

cs.CV↗

arXiv2014

Sparse Graph-based Transduction for Image Classification

Motivated by the remarkable successes of Graph-based Transduction (GT) and Sparse Representation (SR), we present a novel Classifier named Sparse Graph-based Classifier (SGC) for image classification. In SGC, SR is leveraged to measure the correlation (similarity) of each two samples and a graph is constructed for encoding these correlations. Then the Laplacian eigenmapping is adopted for deriving the graph Laplacian of the graph. Finally, SGC can be obtained by plugging the graph Laplacian into the conventional GT framework. In the image classification procedure, SGC utilizes the correlations, which are encoded in the learned graph Laplacian, to infer the labels of unlabeled images. SGC inherits the merits of both GT and SR. Compared to SR, SGC improves the robustness and the discriminating power of GT. Compared to GT, SGC sufficiently exploits the whole data. Therefore it alleviates the undercomplete dictionary issue suffered by SR. Four popular image databases are employed for evaluation. The results demonstrate that SGC can achieve a promising performance in comparison with the state-of-the-art classifiers, particularly in the small training sample size case and the noisy sample case.

cs.CV↗

arXiv2014

On The Effect of Hyperedge Weights On Hypergraph Learning

Hypergraph is a powerful representation in several computer vision, machine learning and pattern recognition problems. In the last decade, many researchers have been keen to develop different hypergraph models. In contrast, no much attention has been paid to the design of hyperedge weights. However, many studies on pairwise graphs show that the choice of edge weight can significantly influence the performances of such graph algorithms. We argue that this also applies to hypegraphs. In this paper, we empirically discuss the influence of hyperedge weight on hypegraph learning via proposing three novel hyperedge weights from the perspectives of geometry, multivariate statistical analysis and linear regression. Extensive experiments on ORL, COIL20, JAFFE, Sheffield, Scene15 and Caltech256 databases verify our hypothesis. Similar to graph learning, several representative hyperedge weighting schemes can be concluded by our experimental studies. Moreover, the experiments also demonstrate that the combinations of such weighting schemes and conventional hypergraph models can get very promising classification and clustering performances in comparison with some recent state-of-the-art algorithms.

cs.CV↗

arXiv2014

Shape Primitive Histogram: A Novel Low-Level Face Representation for Face Recognition

We further exploit the representational power of Haar wavelet and present a novel low-level face representation named Shape Primitives Histogram (SPH) for face recognition. Since human faces exist abundant shape features, we address the face representation issue from the perspective of the shape feature extraction. In our approach, we divide faces into a number of tiny shape fragments and reduce these shape fragments to several uniform atomic shape patterns called Shape Primitives. A convolution with Haar Wavelet templates is applied to each shape fragment to identify its belonging shape primitive. After that, we do a histogram statistic of shape primitives in each spatial local image patch for incorporating the spatial information. Finally, each face is represented as a feature vector via concatenating all the local histograms of shape primitives. Four popular face databases, namely ORL, AR, Yale-B and LFW-a databases, are employed to evaluate SPH and experimentally study the choices of the parameters. Extensive experimental results demonstrate that the proposed approach outperform the state-of-the-arts.

cs.CV↗

arXiv2014

Rate Optimal Denoising of Simultaneously Sparse and Low Rank Matrices

We study minimax rates for denoising simultaneously sparse and low rank matrices in high dimensions. We show that an iterative thresholding algorithm achieves (near) optimal rates adaptively under mild conditions for a large class of loss functions. Numerical experiments on synthetic datasets also demonstrate the competitive performance of the proposed method.

math.ST↗

arXiv2014

Collaborative Discriminant Locality Preserving Projections With its Application to Face Recognition

We present a novel Discriminant Locality Preserving Projections (DLPP) algorithm named Collaborative Discriminant Locality Preserving Projection (CDLPP). In our algorithm, the discriminating power of DLPP are further exploited from two aspects. On the one hand, the global optimum of class scattering is guaranteed via using the between-class scatter matrix to replace the original denominator of DLPP. On the other hand, motivated by collaborative representation, an $L_2$-norm constraint is imposed to the projections to discover the collaborations of dimensions in the sample space. We apply our algorithm to face recognition. Three popular face databases, namely AR, ORL and LFW-A, are employed for evaluating the performance of CDLPP. Extensive experimental results demonstrate that CDLPP significantly improves the discriminating power of DLPP and outperforms the state-of-the-arts.

cs.CV↗

arXiv2013

Face Recognition via Globality-Locality Preserving Projections

We present an improved Locality Preserving Projections (LPP) method, named Gloablity-Locality Preserving Projections (GLPP), to preserve both the global and local geometric structures of data. In our approach, an additional constraint of the geometry of classes is imposed to the objective function of conventional LPP for respecting some more global manifold structures. Moreover, we formulate a two-dimensional extension of GLPP (2D-GLPP) as an example to show how to extend GLPP with some other statistical techniques. We apply our works to face recognition on four popular face databases, namely ORL, Yale, FERET and LFW-A databases, and extensive experimental results demonstrate that the considered global manifold information can significantly improve the performance of LPP and the proposed face recognition methods outperform the state-of-the-arts.

cs.CV↗

arXiv2011

A Sparse SVD Method for High-dimensional Data

We present a new computational approach to approximating a large, noisy data table by a low-rank matrix with sparse singular vectors. The approximation is obtained from thresholded subspace iterations that produce the singular vectors simultaneously, rather than successively as in competing proposals. We introduce novel ways to estimate thresholding parameters which obviate the need for computationally expensive cross-validation. We also introduce a way to sparsely initialize the algorithm for computational savings that allow our algorithm to outperform the vanilla SVD on the full data table when the signal is sparse. A comparison with two existing sparse SVD methods suggests that our algorithm is computationally always faster and statistically always at least comparable to the better of the two competing algorithms.

stat.ME↗

arXiv2011

The surge-like eruption of a miniature filament

We report on the rare eruption of a miniature H_alpha filament that took a surge form. The filament first underwent a full development within 46 minutes and then began to erupt 9 minutes later, followed by a compact, impulsive X-ray class M2.2 flare with a two ribbon nature only at the early eruption phase. During the eruption, its top rose, whereas the two legs remained rooted in the chromosphere and swelled little perpendicular to the rising direction. This led to a surge-like eruption with a narrow angular extent. Similar to the recent observations for standard and blowout X-ray jets by Moore et al., we thus define it as a "blowout H_alpha surge". Furthermore, our observations showed that the eruption was associated with (1) a coronal mass ejection guided by a preexisting streamer, (2) abrupt, significant, and persistent changes in the photospheric magnetic field around the filament, and (3) sudden disappearance of a small pore. These observations thus provide evidence that blowout surge is a small-scale version of large-scale filament eruption in many aspects. Our observations further suggest that at least part of H_alpha surges belong to blowout-type ones, and exact distinction between standard and blowout H_alpha surges is important in understanding their different origins and associated eruptive phenomena.

astro-ph.SR↗