arXiv subjects

Xin Zhang

Publications and source records attributed to Xin Zhang.

At least 1,009 records · Page 56Linked to original sources

arXiv2019

Integration of the Noncollinear Antiferromagnetic Metal Mn3Sn onto Ferroelectric Oxides for Electric-Field Control

Non-collinear antiferromagnetic materials have received dramatically increasing attention in the field of spintronics as their exotic topological features such as the Berry-curvature-induced anomalous Hall effect and possible magnetic Weyl states could be utilized in future topological antiferromagnetic spintronic devices. In this work, we report the successful integration of the antiferromagnetic metal Mn3Sn thin films onto ferroelectric oxide PMN-PT. By optimizing growth, we realized the large anomalous Hall effect with small switching magnetic fields of several tens mT fully comparable to those of bulk Mn3Sn single crystals, anisotropic magnetoresistance and negative parallel magnetoresistance in Mn3Sn thin films with antiferromagnetic order, which are similar to the signatures of the Weyl state in bulk Mn3Sn single crystals. More importantly, we found that the anomalous Hall effect in antiferromagnetic Mn3Sn thin films can be manipulated by electric fields applied onto the ferroelectric materials, thus demonstrating the feasibility of Mn3Sn-based topological spintronic devices operated in an ultralow power manner.

cond-mat.mtrl-sci↗

arXiv2019

Prospect for dark matter signatures from dwarf galaxies by LHAASO

The Large High Altitude Air Shower Observatory (LHAASO) is a next-generation observatory for high energy gamma rays and cosmic rays with wide field of view. It will detect gamma rays with high sensitivity in the energy range from 300 GeV to 1 PeV. Therefore, it is promising for LHAASO to search for the high-energy gamma rays induced by dark matter (DM) self-annihilation in dwarf spheroidal satellite galaxies (dSphs), which are ideal objects for the DM indirect detection. In this work, we investigate the LHAASO sensitivity to DM self-annihilation signatures for 19 dSphs and take the uncertainties on the spatial DM distribution of dSphs into account. We perform a joint likelihood analysis for the 19 dSphs and find that the LHAASO sensitivity to the DM annihilation cross section will reach $\mathcal{O}(10^{-24})\sim \mathcal{O}(10^{-25})$ cm$^3$ s$^{-1}$ at the mass scale above TeV for several annihilation modes, which is larger than the canonical thermal relic cross section by a factor of 10 to 100.

astro-ph.HE↗

arXiv2019

Graph Clustering in All Parameter Regimes

Resolution parameters in graph clustering represent a size and quality trade-off. We address the task of efficiently solving a parameterized graph clustering objective for all values of a resolution parameter. Specifically, we consider an objective we call LambdaPrime, involving a parameter $λ\in (0,1)$. This objective is related to other parameterized clustering problems, such as parametric generalizations of modularity, and captures a number of specific clustering problems as special cases, including sparsest cut and cluster deletion. While previous work provides approximation results for a single resolution parameter, we seek a set of approximately optimal clusterings for all values of $λ$ in polynomial time. In particular, we ask the question, how small a family of clusterings suffices to optimize -- or to approximately optimize -- the LambdaPrime objective over the full possible spectrum of $λ$? We obtain a family of logarithmically many clusterings by solving the parametric linear programming relaxation of LambdaPrime at a logarithmic number of parameter values, and round their solutions using existing approximation algorithms. We prove that this number is tight up to a constant factor. Specifically, for a certain class of ring graphs, a logarithmic number of feasible solutions is required to provide a constant-factor approximation for the LambdaPrime LP relaxation in all parameter regimes. We additionally show that for any graph with $n$ nodes and $m$ edges, there exists a set of $m$ or fewer clusterings such that for every $λ\in (0,1)$, the family contains an exact solution to the LambdaPrime objective. There also exists a set of $O(\log n)$ clusterings that provide a $(1+\varepsilon)$-approximate solution in all parameter regimes; we demonstrate simple graph classes for which these bounds are tight.

cs.CC↗

arXiv2019

Light edges in 1-planar graphs of minimum degree 3

A graph is 1-planar if it can be drawn in the plane so that each edge is crossed by at most one another edge. In this work we prove that each 1-planar graph of minimum degree at least $3$ contains an edge with degrees of its endvertices of type $(3,\leq23)$ or $(4,\leq11)$ or $(5,\leq9)$ or $(6,\leq8)$ or $(7,7)$. Moreover, the upper bounds $9,8$ and $7$ here are sharp and the upper bounds $23$ and $11$ are very close to the possible sharp ones, which may be 20 and 10, respectively. This generalizes a result of Fabrici and Madaras [Discrete Math., 307 (2007) 854--865] which says that each 3-connected 1-planar graph contains a light edge, and improves a result of Hudák and Šugerek [Discuss. Math. Graph Theory, 32(3) (2012) 545--556], which states that each 1-planar graph of minimum degree at least $4$ contains an edge with degrees of its endvertices of type $(4,\leq 13)$ or $(5,\leq 9)$ or $(6,\leq 8)$ or $(7, 7)$.

math.CO↗

arXiv2019

Cosmological parameter estimation with future gravitational wave standard siren observation from the Einstein Telescope

In this work, we use the simulated gravitational wave (GW) standard siren data from the future observation of the Einstein Telescope (ET) to constrain various dark energy cosmological models, including the $Λ$CDM, $w$CDM, CPL, $α$DE, GCG, and NGCG models. We also use the current mainstream cosmological electromagnetic observations, i.e., the cosmic microwave background anisotropies data, the baryon acoustic oscillations data, and the type Ia supernovae data, to constrain these models. We find that the GW standard siren data could tremendously improve the constraints on the cosmological parameters for all these dark energy models. For all the cases, the GW standard siren data can be used to break the parameter degeneracies generated by the current cosmological electromagnetic observational data. Therefore, it is expected that the future GW standard siren observation from the ET would play a crucial role in the cosmological parameter estimation in the future. The conclusion of this work is quite solid because it is based on the analysis for various dark energy models.

astro-ph.CO↗

arXiv2019

Byzantine-Resilient Stochastic Gradient Descent for Distributed Learning: A Lipschitz-Inspired Coordinate-wise Median Approach

In this work, we consider the resilience of distributed algorithms based on stochastic gradient descent (SGD) in distributed learning with potentially Byzantine attackers, who could send arbitrary information to the parameter server to disrupt the training process. Toward this end, we propose a new Lipschitz-inspired coordinate-wise median approach (LICM-SGD) to mitigate Byzantine attacks. We show that our LICM-SGD algorithm can resist up to half of the workers being Byzantine attackers, while still converging almost surely to a stationary region in non-convex settings. Also, our LICM-SGD method does not require any information about the number of attackers and the Lipschitz constant, which makes it attractive for practical implementations. Moreover, our LICM-SGD method enjoys the optimal $O(md)$ computational time-complexity in the sense that the time-complexity is the same as that of the standard SGD under no attacks. We conduct extensive experiments to show that our LICM-SGD algorithm consistently outperforms existing methods in training multi-class logistic regression and convolutional neural networks with MNIST and CIFAR-10 datasets. In our experiments, LICM-SGD also achieves a much faster running time thanks to its low computational time-complexity.

cs.LG↗

arXiv2019

Compressed Distributed Gradient Descent: Communication-Efficient Consensus over Networks

Network consensus optimization has received increasing attention in recent years and has found important applications in many scientific and engineering fields. To solve network consensus optimization problems, one of the most well-known approaches is the distributed gradient descent method (DGD). However, in networks with slow communication rates, DGD's performance is unsatisfactory for solving high-dimensional network consensus problems due to the communication bottleneck. This motivates us to design a communication-efficient DGD-type algorithm based on compressed information exchanges. Our contributions in this paper are three-fold: i) We develop a communication-efficient algorithm called amplified-differential compression DGD (ADC-DGD) and show that it converges under {\em any} unbiased compression operator; ii) We rigorously prove the convergence performances of ADC-DGD and show that they match with those of DGD without compression; iii) We reveal an interesting phase transition phenomenon in the convergence speed of ADC-DGD. Collectively, our findings advance the state-of-the-art of network consensus optimization theory.

cs.DC↗

arXiv2019

Seismic tomography using variational inference methods

Seismic tomography is a methodology to image the interior of solid or fluid media, and is often used to map properties in the subsurface of the Earth. In order to better interpret the resulting images it is important to assess imaging uncertainties. Since tomography is significantly nonlinear, Monte Carlo sampling methods are often used for this purpose, but they are generally computationally intractable for large datasets and high-dimensional parameter spaces. To extend uncertainty analysis to larger systems we use variational inference methods to conduct seismic tomography. In contrast to Monte Carlo sampling, variational methods solve the Bayesian inference problem as an optimization problem, yet still provide probabilistic results. In this study, we applied two variational methods, automatic differential variational inference (ADVI) and Stein variational gradient descent (SVGD), to 2D seismic tomography problems using both synthetic and real data and we compare the results to those from two different Monte Carlo sampling methods. The results show that variational inference methods can produce accurate approximations to the results of Monte Carlo sampling methods at significantly lower computational cost, provided that gradients of parameters with respect to data can be calculated efficiently. We expect that the methods can be applied fruitfully to many other types of geophysical inverse problems.

physics.geo-ph↗

arXiv2019

Probabilistic Neural Network Tomography across Grane field (North Sea) from Surface Wave Dispersion Data

Surface wave tomography uses measured dispersion properties of surface waves to infer the spatial distribution of subsurface properties such as shear-wave velocities. These properties can be estimated vertically below any geographical location at which surface wave dispersion data are available. As the inversion is significantly non-linear, Monte Carlo methods are often used to invert dispersion curves for shear-wave velocity profiles with depth to give a probabilistic solution. Such methods provide uncertainty information but are computationally expensive. Neural network based inversion provides a more efficient way to obtain probabilistic solutions when those solutions are required beneath many geographical locations. Unlike Monte Carlo methods, once a network has been trained it can be applied rapidly to perform any number of inversions. We train a class of neural networks called mixture density networks, to invert dispersion curves for shear-wave velocity models and their non-linearised uncertainty. Mixture density networks are able to produce fully probabilistic solutions in the form of weighted sums of multivariate analytic kernels such as Gaussians, and we show that including data uncertainties in the mixture density network gives more reliable mean velocity estimates when data contains significant noise. The networks were applied to data from the Grane field in the Norwegian North sea to produce shear-wave velocity maps at several depth levels. Post-training we obtained probabilistic velocity profiles with depth beneath 26,772 locations to produce a 3D velocity model in 21 seconds on a standard desktop computer. This method is therefore ideally suited for rapid, repeated 3D subsurface imaging and monitoring.

physics.geo-ph↗

arXiv2019

Equitable tree-$O(d)$-coloring of $d$-degenerate graphs

An equitable tree-$k$-coloring of a graph is a vertex coloring on $k$ colors so that every color class incudes a forest and the sizes of any two color classes differ by at most one.This kind of coloring was first introduced in 2013 and can be used to formulate the structure decomposition problem on the communication network with some security considerations. In 2015, Esperet, Lemoine and Maffray showed that every $d$-degenerate graph admits an equitable tree-$k$-coloring for every $k\geq 3^{d-1}$. Motivated by this result, we attempt to lower their exponential bound to a linear bound. Precisely, we prove that every $d$-degenerate graph $G$ admits an equitable tree-$k$-coloring for every $k\geq αd$ provided that $|G|\geq βΔ(G)$, where $(α,β)\in \{(8,56), (9,26), (10,18), (11,15), (12,13), (13,12), (14,11), (15,10), (17,9), (20,8), (27,7), (52,6)\}$.

math.CO↗

arXiv2019

Equitable partition of graphs into induced linear forests

It is proved that the vertex set of any simple graph $G$ can be equitably partitioned into $k$ subsets for any integer $k\geq\max\{\big\lceil\frac{Δ(G)+1}{2}\big\rceil,\big\lceil\frac{|G|}{4}\big\rceil\}$ so that each of them induces a linear forest.

math.CO↗

arXiv2019

Cosmology from the Chinese Space Station Optical Survey (CSS-OS)

The Chinese Space Station Optical Survey (CSS-OS) is a planned full sky survey operated by the Chinese Space Station Telescope (CSST). It can simultaneously perform the photometric imaging and spectroscopic slitless surveys, and will probe weak and strong gravitational lensing, galaxy clustering, individual galaxies and galaxy clusters, active galactic nucleus (AGNs), and so on. It aims to explore the properties of dark matter and dark energy and other important cosmological problems. In this work, we focus on two main CSS-OS scientific goals, i.e. the weak gravitational lensing (WL) and galaxy clustering surveys. We generate the mock CSS-OS data based on the observational COSMOS and zCOSMOS catalogs. We investigate the constraints on the cosmological parameters from the CSS-OS using the Markov Chain Monte Carlo (MCMC) method. The intrinsic alignments, galaxy bias, velocity dispersion, and systematics from instrumental effects in the CSST WL and galaxy clustering surveys are also included, and their impacts on the constraint results are discussed. We find that the CSS-OS can improve the constraints on the cosmological parameters by a factor of a few (even one order of magnitude in the optimistic case), compared to the current WL and galaxy clustering surveys. The constraints can be further enhanced when performing joint analysis with the WL, galaxy clustering, and galaxy-galaxy lensing data. Therefore, the CSS-OS is expected to be a powerful survey for exploring the Universe. Since some assumptions may be still optimistic and simple, it is possible that the results from the real survey could be worse. We will study these issues in details with the help of simulations in the future.

astro-ph.CO↗

arXiv2019

Bandgap closing at the screw dislocations of WS2 spirals

Van der Waals (vdW) layered transition metal dichalcogenides (TMDCs) materials are emerging as one class of quantum materials holding novel optical and electronic properties. In particular, the bandgap tunability attractive for nanoelectronics technology have been observed up to 1.1 eV when applying dielectric screening or grain boundary engineering. Here we present the experimental observation of bandgap closing at the center of the screw dislocation-driven WS2 spiral pyramid by means of scanning tunneling spectroscopy, which is validated by first-principle calculations. The observed giant bandgap modulation is attributed to the presence of dangling bonds induced by the W-S broken and the enhanced localized stress in the core of the dislocation. Achieving this metallic state and the consequent vertical conducting channel presents a pathway to 3D-interconnected vdW heterostructure devices based on emergent semiconducting TMDCs.

cond-mat.mes-hall↗

arXiv2019

Ergodic Mutual Information for Generalized Fadings

In this paper, novel expressions are derived to evaluate the ergodic mutual information (EMI) under BPSK modulation of single-input single-output (SISO) systems operating in generalized fading channels, including $η$-$μ$ fading and $κ$-$μ$ fading. To verify our derivation, we first investigate the specific fading types, namely Rayleigh, Nakagami-$m$ and Rician, and then turn to generalized fading scenarios. It is shown that all the expressions concluded from the generalized cases can be specialized into those derived from the specific ones. Different from the conventional results of the EMI, our developed expressions contain only the simplest numerical calculations, without any Meijer's G-functions which must be implemented in the particular computing software. Additionally, it should be noted that our work provides a complete analysis of the EMI in wireless channel under discrete inputs.

eess.SP↗

arXiv2019

Performance of Multiple Antennas Selection in SIMO Systems with BPSK/QPSK Modulations

This paper studies the performance of single-input multiple-output (SIMO) systems under receive antenna selection (RAS) and BPSK/QPSK modulations. At the receiver, a subset of branches are selected and combined using maximal-ratio combining (MRC) to maximize the instantaneous Signal to Noise Ratio (SNR). By assuming independent and identical distributed (i.i.d.) Rayleigh flat fading, a closed-form expression, with considerably high precision, is developed to approximate the average input-output mutual information, also termed as symmetric capacity, of the whole system. Later, this approximated expression is further utilized to investigate the efficient capacity and energy efficiency of the SIMO system under BPSK/QPSK modulations and RAS. Besides analytical derivations, simulations are provided to demonstrate the approximation precision, feasibility and validity of the derived results.

eess.SP↗

arXiv2019

Ergodic Secrecy Rate of Antenna-Selection-Aided MIMOME Channels with BPSK/QPSK Modulations

This paper analyzes transmit antenna selection (TAS) under Rayleigh flat fading for BPSK/QPSK modulations in multiple-input multiple-output wiretap channels, also termed as multiple-input multiple-output multiple-eavesdropper (MIMOME) channels. In our protocol, a single antenna is selected to transmit the secret message and selection combing (SC) or maximal-ratio combing (MRC) is utilized at the legitimate receiver or the eavesdropper. Novel closed-form expressions for the ergodic secrecy rates are derived to approximate the exact values, which hold high precision and compact forms. Besides theoretical derivations, simulations are provided to demonstrate the feasibility and validity of the proposed formulas.

eess.SP↗

arXiv2019

orthoDr: Semiparametric Dimension Reduction via Orthogonality Constrained Optimization

orthoDr is a package in R that solves dimension reduction problems using orthogonality constrained optimization approach. The package serves as a unified framework for many regression and survival analysis dimension reduction models that utilize semiparametric estimating equations. The main computational machinery of orthoDr is a first-order algorithm developed by \cite{wen2013feasible} for optimization within the Stiefel manifold. We implement the algorithm through Rcpp and OpenMP for fast computation. In addition, we developed a general-purpose solver for such constrained problems with user-specified objective functions, which works as a drop-in version of optim(). The package also serves as a platform for future methodology developments along this line of work.

stat.CO↗

arXiv2019

Gravitational wave standard sirens and cosmological parameter measurement

Gravitational wave standard sirens can be used to arbitrate the $H_0$ tension within the following several years. In the future, the observation of gravitational wave standard sirens would be developed into a powerful new cosmological probe because they can play an important role in breaking parameter degeneracies formed by other observations. Therefore, gravitational wave standard sirens are of great importance for the future accurate measurement of cosmological parameters.

astro-ph.CO↗