arXiv subjects

Lei Sun

Publications and source records attributed to Lei Sun.

At least 199 records · Page 11Linked to original sources

arXiv2019

Spatially Resolved X-ray Spectroscopy of Kepler's Supernova Remnant: Distinct Properties of the Circumstellar Medium and the Ejecta

Kepler's supernova remnant (SNR) is believed to result from a Type Ia supernova, but be interacting with dense circumstellar material (CSM), which makes its progenitor system a mystery. Using the Chandra ACIS-S 741 ks effective exposure data and an advanced adaptive binning algorithm, we analyse the spectra from tessellated regions in Kepler's SNR. For the first time, we map out the detailed spatial distributions of emission measure (EM), electron temperature, ionization parameter, and metal abundances for both the low- and high-temperature plasma components. The low-temperature plasma diverges into two branches in the temperature v.s. ionization parameter diagram, which appear to be spatially associated with the warm CSM dusts and the Si- and S-rich ejecta, respectively. We construct the probability distribution functions of abundance ratios of O and Mg to L-shell emitting Fe ([O]/[Fe]$_{\rm L}$ and [Mg]/[Fe]$_{\rm L}$), and fit them with double Gaussians. Thereby, we distinguish the CSM from the ejecta: the CSM is characterized by [O]/[Fe]$_{\rm L}$ $= 0.77^{+0.30}_{-0.23}$ and [Mg]/[Fe]$_{\rm L}$ $= 1.11^{+0.46}_{-0.32}$, while the ejecta by [O]/[Fe]$_{\rm L}$ $= 0.31^{+0.17}_{-0.10}$ and [Mg]/[Fe]$_{\rm L}$ $= 0.38^{+0.36}_{-0.19}$. We estimate the total hydrogen mass of the shocked CSM as $\sim 1.4 M_\odot$ and the EM-weighted mean [Mg]/[O] $= 1.14 \pm 0.49$, which can be reproduced with an asymptotic giant branch donor star with initial mass of $\sim 4 M_\odot$. The abundance ratios from the shocked ejecta are well compatible with the predicted results from spherical delayed-detonation models for Type Ia supernovae. We also find that the two `ears' of the remnant are dominated by Si- and S-rich ejecta, thus favoring a pre-explosion jets scenario.

astro-ph.HE↗

arXiv2018

Solving the Empirical Bayes Normal Means Problem with Correlated Noise

The Normal Means problem plays a fundamental role in many areas of modern high-dimensional statistics, both in theory and practice. And the Empirical Bayes (EB) approach to solving this problem has been shown to be highly effective, again both in theory and practice. However, almost all EB treatments of the Normal Means problem assume that the observations are independent. In practice correlations are ubiquitous in real-world applications, and these correlations can grossly distort EB estimates. Here, exploiting theory from Schwartzman (2010), we develop new EB methods for solving the Normal Means problem that take account of unknown correlations among observations. We provide practical software implementations of these methods, and illustrate them in the context of large-scale multiple testing problems and False Discovery Rate (FDR) control. In realistic numerical experiments our methods compare favorably with other commonly-used multiple testing methods.

stat.ME↗

arXiv2018

Bayesian $l_0$-regularized Least Squares

Bayesian $l_0$-regularized least squares is a variable selection technique for high dimensional predictors. The challenge is optimizing a non-convex objective function via search over model space consisting of all possible predictor combinations. Spike-and-slab (a.k.a. Bernoulli-Gaussian) priors are the gold standard for Bayesian variable selection, with a caveat of computational speed and scalability. Single Best Replacement (SBR) provides a fast scalable alternative. We provide a link between Bayesian regularization and proximal updating, which provides an equivalence between finding a posterior mode and a posterior mean with a different regularization prior. This allows us to use SBR to find the spike-and-slab estimator. To illustrate our methodology, we provide simulation evidence and a real data example on the statistical properties and computational efficiency of SBR versus direct posterior sampling using spike-and-slab priors. Finally, we conclude with directions for future research.

stat.ML↗

arXiv2018

Mask R-CNN with Pyramid Attention Network for Scene Text Detection

In this paper, we present a new Mask R-CNN based text detection approach which can robustly detect multi-oriented and curved text from natural scene images in a unified manner. To enhance the feature representation ability of Mask R-CNN for text detection tasks, we propose to use the Pyramid Attention Network (PAN) as a new backbone network of Mask R-CNN. Experiments demonstrate that PAN can suppress false alarms caused by text-like backgrounds more effectively. Our proposed approach has achieved superior performance on both multi-oriented (ICDAR-2015, ICDAR-2017 MLT) and curved (SCUT-CTW1500) text detection benchmark tasks by only using single-scale and single-model testing.

cs.CV↗

arXiv2018

Gradient-Free Learning Based on the Kernel and the Range Space

In this article, we show that solving the system of linear equations by manipulating the kernel and the range space is equivalent to solving the problem of least squares error approximation. This establishes the ground for a gradient-free learning search when the system can be expressed in the form of a linear matrix equation. When the nonlinear activation function is invertible, the learning problem of a fully-connected multilayer feedforward neural network can be easily adapted for this novel learning framework. By a series of kernel and range space manipulations, it turns out that such a network learning boils down to solving a set of cross-coupling equations. By having the weights randomly initialized, the equations can be decoupled and the network solution shows relatively good learning capability for real world data sets of small to moderate dimensions. Based on the structural information of the matrix equation, the network representation is found to be dependent on the number of data samples and the output dimension.

cs.LG↗

arXiv2018

Deterministic Stretchy Regression

An extension of the regularized least-squares in which the estimation parameters are stretchable is introduced and studied in this paper. The solution of this ridge regression with stretchable parameters is given in primal and dual spaces and in closed-form. Essentially, the proposed solution stretches the covariance computation by a power term, thereby compressing or amplifying the estimation parameters. To maintain the computation of power root terms within the real space, an input transformation is proposed. The results of an empirical evaluation in both synthetic and real-world data illustrate that the proposed method is effective for compressive learning with high-dimensional data.

cs.LG↗

arXiv2018

NuSTAR Detection of a Hard X-ray Source in the Supernova Remnant - Molecular Cloud Interaction Site of IC 443

We report on a broadband study of a complex X-ray source (1SAX J0618.0+2227) associated with the interaction site of the supernova remnant (SNR) IC 443 and ambient molecular cloud (MC) using NuSTAR, XMM_Newton, and Chandra observations. Its X-ray spectrum is composed of both thermal and non-thermal components. The thermal component can be equally well represented by either a thin plasma model with kT=0.19 keV or a blackbody model with kT=0.11 keV. The non-thermal component can be fit with either a power-law with Gamma~1.7 or a cutoff power-law with Gamma~1.5 and a cutoff energy at E_cut~18 keV. Using the newly obtained NuSTAR dataset, we test three possible scenarios for isolated X-ray sources in the SNR-MC interaction site: 1) pulsar wind nebula (PWN); 2) SNR ejecta fragment; 3) shocked molecular clump. We conclude that this source is most likely composed of a SNR ejecta (or a PWN) and surrounding shocked molecular clumps. The nature of this hard X-ray source in the SNR-MC interaction site of IC 443 may shed light on unidentified X-ray sources with hard X-ray spectra in rich environments for star forming regions, such as the Galactic center.

astro-ph.HE↗

arXiv2018

An Anchor-Free Region Proposal Network for Faster R-CNN based Text Detection Approaches

The anchor mechanism of Faster R-CNN and SSD framework is considered not effective enough to scene text detection, which can be attributed to its IoU based matching criterion between anchors and ground-truth boxes. In order to better enclose scene text instances of various shapes, it requires to design anchors of various scales, aspect ratios and even orientations manually, which makes anchor-based methods sophisticated and inefficient. In this paper, we propose a novel anchor-free region proposal network (AF-RPN) to replace the original anchor-based RPN in the Faster R-CNN framework to address the above problem. Compared with a vanilla RPN and FPN-RPN, AF-RPN can get rid of complicated anchor design and achieve higher recall rate on large-scale COCO-Text dataset. Owing to the high-quality text proposals, our Faster R-CNN based two-stage text detection approach achieves state-of-the-art results on ICDAR-2017 MLT, ICDAR-2015 and ICDAR-2013 text detection benchmarks when using single-scale and single-model (ResNet50) testing only.

cs.CV↗

arXiv2017

Observation of spin-orbit magnetoresistance in metallic thin films on magnetic insulators

A magnetoresistance effect induced by the Rashba spin-orbit interaction was predicted, but not yet observed, in bilayers consisting of normal metal and ferromagnetic insulator. Here, we present an experimental observation of this new type of spin-orbit magnetoresistance (SOMR) effect in a bilayer structure Cu[Pt]/Y3Fe5O12 (YIG), where the Cu/YIG interface is decorated with nanosize Pt islands. This new MR is apparently not caused by the bulk spin-orbit interaction because of the negligible spin-orbit interaction in Cu and the discontinuity of the Pt islands. This SOMR disappears when the Pt islands are absent or located away from the Cu/YIG interface, therefore we can unambiguously ascribe it to the Rashba spin-orbit interaction at the interface enhanced by the Pt decoration. The numerical Boltzmann simulations are consistent with the experimental SOMR results in the angular dependence of magnetic field and the Cu thickness dependence. Our finding demonstrates the realization of the spin manipulation by interface engineering.

cond-mat.mtrl-sci↗

arXiv2017

Rapid and Precise Determination of Zero-Field Splittings by Terahertz Time-Domain Electron Paramagnetic Resonance Spectroscopy

Zero-field splitting (ZFS) parameters are fundamentally tied to the geometries of metal ion complexes. Despite their critical importance for understanding the magnetism and spectroscopy of metal complexes, they are not routinely available through general laboratory-based techniques, and are often inferred from magnetism data. Here we demonstrate a simple tabletop experimental approach that enables direct and reliable determination of ZFS parameters in the terahertz (THz) regime. We report time-domain measurements of electron paramagnetic resonance (EPR) signals associated with THz-frequency ZFSs in molecular complexes containing high-spin transition-metal ions. We measure the temporal profiles of the free-induction decays of spin resonances in the complexes at zero and nonzero external magnetic fields, and we derive the EPR spectra via numerical Fourier transformation of the time-domain signals. In most cases, absolute values of the ZFS parameters are extracted from the measured zero-field EPR frequencies, and the signs can be determined by zero-field measurements at two different temperatures. Field-dependent EPR measurements further allow refined determination of the ZFS parameters and access to the g-factor. The results show good agreement with those obtained by other methods. The simplicity of the method portends wide applicability in chemistry, biology and material science.

physics.chem-ph↗

arXiv2017

Bayesian Model Averaging for the X-Chromosome Inactivation Dilemma in Genetic Association Study

X-chromosome is often excluded from the so called `whole-genome' association studies due to its intrinsic difference between males and females. One particular analytical challenge is the unknown status of X-inactivation, where one of the two X-chromosome variants in females may be randomly selected to be silenced. In the absence of biological evidence in favour of one specific model, we consider a Bayesian model averaging framework that offers a principled way to account for the inherent model uncertainty, providing model averaging-based posterior density intervals and Bayes factors. We examine the inferential properties of the proposed methods via extensive simulation studies, and we apply the methods to a genetic association study of an intestinal disease occurring in about twenty percent of Cystic Fibrosis patients. Compared with the results previously reported assuming the presence of inactivation, we show that the proposed Bayesian methods provide more feature-rich quantities that are useful in practice.

stat.AP↗

arXiv2016

Constrained Maximum Correntropy Adaptive Filtering

Constrained adaptive filtering algorithms inculding constrained least mean square (CLMS), constrained affine projection (CAP) and constrained recursive least squares (CRLS) have been extensively studied in many applications. Most existing constrained adaptive filtering algorithms are developed under mean square error (MSE) criterion, which is an ideal optimality criterion under Gaussian noises. This assumption however fails to model the behavior of non-Gaussian noises found in practice. Motivated by the robustness and simplicity of maximum correntropy criterion (MCC) in non-Gaussian impulsive noises, this paper proposes a new adaptive filtering algorithm called constrained maximum correntropy criterion (CMCC). Specifically, CMCC incorporates a linear constraint into a MCC filter to solve a constrained optimization problem explicitly. The proposed adaptive filtering algorithm is easy to implement and has low computational complexity, and in terms of convergence accuracy (say lower mean square deviation) and stability, can significantly outperform those MSE based constrained adaptive algorithms in presence of heavy-tailed impulsive noises. Additionally, the mean square convergence behaviors are studied under energy conservation relation, and a sufficient condition to ensure the mean square convergence and the steady-state mean square deviation (MSD) of the proposed algorithm are obtained. Simulation results confirm the theoretical predictions under both Gaussian and non- Gaussian noises, and demonstrate the excellent performance of the novel algorithm by comparing it with other conventional methods.

stat.ML↗

arXiv2016

On "Exponential Lower Bounds for Polytopes in Combinatorial Optimization" by Fiorini et al. (2015): A Refutation For Models With Disjoint Sets of Descriptive Variables

We provide a numerical refutation of the developments of Fiorini et al. (2015)* for models with disjoint sets of descriptive variables. We also provide an insight into the meaning of the existence of a one-to-one linear map between solutions of such models. *: Fiorini, S., S. Massar, S. Pokutta, H.R. Tiwary, and R. de Wolf (2015). Exponential Lower Bounds for Polytopes in Combinatorial Optimization. Journal of the ACM 62:2, Article No. 17.

cs.CC↗

arXiv2016

Conetronics in 2D Metal-Organic Frameworks: Double Dirac Cones, Magnetic Half Dirac Cones and Quantum Anomalous Hall Effect

Based on recently synthesized Ni3C12S12 class 2D metal-organic frameworks, we predict electronic properties of M3C12S12 and M3C12O12, where M is Zn, Cd, Hg, Be, or Mg with no M orbital contributions to bands near Fermi level. For M3C12S12, their band structures exhibit double Dirac cones with different Fermi velocities that are n and p type, respectively, which are switchable by few-percent strain. The crossing of two cones are symmetry-protected to be non-hybridizing, leading to two independent channels in 2D node-line semimetals at the same k-point akin to spin-channels in spintronics, rendering conetronics device possible. The node line rings right at their crossing, which are both electron and hole pockets at the Fermi level, can give rise to magnetoresistance that will not saturate when the magnetic field is infinitely large, due to perfect n-p compensation. For M3C12O12, together with conjugated metal-tricatecholate polymers M3(HHTP)2, the spin-polarized slow Dirac cone center is pinned precisely at the Fermi level, making the systems conducting in only one spin or cone channel. Quantum anomalous Hall effect can arise in MOFs with non-negligible spin-orbit coupling like Cu3C12O12. Compounds of M3C12S12 and M3C12O12 with different M, can be used to build spintronic and cone-selecting heterostructure devices, tunable by strain or electrostatic gating.

cond-mat.mtrl-sci↗

arXiv2016

A Generalized Levene's Scale Test for Variance Heterogeneity in the Presence of Sample Correlation and Group Uncertainty

We generalize Levene's test for variance (scale) heterogeneity between $k$ groups for more complex data, which includes sample correlation and group membership uncertainty. Following a two-stage regression framework, we show that least absolute deviation regression must be used in the stage 1 analysis to ensure a correct asymptotic $χ^2_{k-1}/(k-1)$ distribution of the generalized scale ($gS$) test statistic. We then show that the proposed $gS$ test is independent of the generalized location test, under the joint null hypothesis of no mean and no variance heterogeneity. Consequently, we generalize the recently proposed joint location-scale ($gJLS$) test valuable in settings where there is an interaction effect, but one interacting variable is not available. We evaluate the proposed method via an extensive simulation study, and two genetic association application studies.

stat.ME↗

arXiv2015

Nonlocal effective medium analysis in symmetric metal-dielectric multilayer metamaterials

The optical nonlocality in symmetric metal-dielectric multilayer metamaterials is theoretically and experimentally investigated with respect to transverse-magnetic-polarized incident light. A nonlocal effective medium theory is derived from the transfer-matrix method to determine the nonlocal effective permittivity depending on both the frequency and wave vector in a symmetric metal-dielectric multilayer stack. In contrast to the local effective medium theory, our proposed nonlocal effective medium theory can accurately predict measured incident angle-dependent reflection spectra from a fabricated multilayer stack and provide nonlocal dispersion relations. Moreover, the bulk plasmon polaritons with large wave vectors supported in the multilayer stack are also investigated with the nonlocal effective medium theory through the analysis of the dispersion relation and eigenmode.

physics.optics↗

arXiv2015

Diffraction-free optical beam propagation with near-zero phase variation in extremely anisotropic metamaterials

Extremely anisotropic metal-dielectric multilayer metamaterials are designed to have the effective permittivity tensor of a transverse component (parallel to the interfaces of the multilayer) with zero real part and a longitudinal component (normal to the interfaces of the multilayer) with ultra-large imaginary part at the same wavelength, including the optical nonlocality analysis based on the transfer-matrix method. The diffraction-free deep-subwavelength optical beam propagation with near-zero phase variation in the designed multilayer stack due to the near-flat iso-frequency contour is demonstrated and analyzed, including the effects of the multilayer period and the material loss.

physics.optics↗

arXiv2014

A novel joint location-scale testing framework for improved detection of variants with main or interaction effects

We propose a novel and easy-to-implement joint location-scale association testing procedure that can account for complex genetic architecture without explicitly modeling interaction effects, and is suitable for large-scale whole-genome scans and meta-analyses. We focus on Fisher's method and use it to combine evidence from the standard location test and the more recent scale test, and we describe its use for single-variant, gene-set and pathway association analyses.

stat.ME↗