arXiv subjects

Xue Chen

Publications and source records attributed to Xue Chen.

At least 37 records · Page 2Linked to original sources

arXiv2022

Ultrafast coherent interlayer phonon dynamics in atomically thin layers of MnBi2Te4

The atomically thin MnBi2Te4 crystal is a novel magnetic topological insulator, exhibiting exotic quantum physics. Here we report a systematic investigation of ultrafast carrier dynamics and coherent interlayer phonons in few-layer MnBi2Te4 as a function of layer number using time-resolved pump-probe reflectivity spectroscopy. Pronounced coherent phonon oscillations from the interlayer breathing mode are directly observed in the time domain. We find that the coherent oscillation frequency, the photocarrier and coherent phonon decay rates all depend sensitively on the sample thickness. The time-resolved measurements are complemented by ultralow-frequency Raman spectroscopy measurements, which both confirm the interlayer breathing mode and additionally enable observation of the interlayer shear mode. The layer dependence of these modes allows us to extract both the out-of-plane and in-plane interlayer force constants. Our studies not only reveal the interlayer van der Waals coupling strengths, but also shed light on the ultrafast optical properties of this novel two-dimensional material.

cond-mat.mes-hall↗

arXiv2022

Physical realization of topological Roman surface by spin-induced ferroelectric polarization in cubic lattice

Topology, a mathematical concept in geometry, has become an ideal theoretical tool for describing topological states and phase transitions. Many topological concepts have found their physical entities in real or reciprocal spaces identified by topological/geometrical invariants, which are usually defined on orientable surfaces such as torus and sphere. It is natural to quest whether it is possible to find the physical realization of more intriguing non-orientable surfaces. Herein, we show that the set of spin-induced ferroelectric polarizations in cubic perovskite oxides AMn3Cr4O12 (A = La and Tb) resides on the topological Roman surface, a non-orientable two-dimensional manifold formed by sewing a Mobius strip edge to that of a disc. The induced polarization may travel in a loop along the non-orientable Mobius strip or orientable disc depending on how the spin evolves as controlled by external magnetic field. Experimentally, the periodicity of polarization can be the same or the twice of the rotating magnetic field, being well consistent with the orientability of disc and Mobius strip, respectively. This path dependent topological magnetoelectric effect presents a way to detect the global geometry of the surface and deepens our understanding of topology in both mathematics and physics

cond-mat.str-el↗

arXiv2022

Improved Decoding of Expander Codes

We study the classical expander codes, introduced by Sipser and Spielman \cite{SS96}. Given any constants $0< α, \varepsilon < 1/2$, and an arbitrary bipartite graph with $N$ vertices on the left, $M < N$ vertices on the right, and left degree $D$ such that any left subset $S$ of size at most $αN$ has at least $(1-\varepsilon)|S|D$ neighbors, we show that the corresponding linear code given by parity checks on the right has distance at least roughly $\frac{αN}{2 \varepsilon }$. This is strictly better than the best known previous result of $2(1-\varepsilon ) αN$ \cite{Sudan2000note, Viderman13b} whenever $\varepsilon < 1/2$, and improves the previous result significantly when $\varepsilon $ is small. Furthermore, we show that this distance is tight in general, thus providing a complete characterization of the distance of general expander codes. Next, we provide several efficient decoding algorithms, which vastly improve previous results in terms of the fraction of errors corrected, whenever $\varepsilon < \frac{1}{4}$. Finally, we also give a bound on the list-decoding radius of general expander codes, which beats the classical Johnson bound in certain situations (e.g., when the graph is almost regular and the code has a high rate). Our techniques exploit novel combinatorial properties of bipartite expander graphs. In particular, we establish a new size-expansion tradeoff, which may be of independent interests.

cs.IT↗

arXiv2022

Structure and relaxor ferroelectric behavior of novel tungsten bronze type ceramic, Sr5BiTi3Nb7O30

A novel lead-free tungsten bronze type ceramic Sr5BiTi3Nb7O30, was prepared by a conventional solid-state reaction route. The room-temperature crystal structure shows an average structure with centro-symmetric space group P4/mbm identified by synchrotron XRD. Temperature dependence of dielectric permittivity indicates that Sr5BiTi3Nb7O30 is a ferroelectric relaxor with Tm near 260 K. The ceramic displays stronger frequency dispersion and lower phase-transition temperature compared with Sr6Ti2Nb8O30. A macroscopic and phenomenological statistical model was employed to describe the temperature dependence of their dielectric responses. The calculated size of polar nanoregions (PNRs) of Sr5BiTi3Nb7O30 compared with Sr6Ti2Nb8O30 implies that the stronger diffusion phase transition for the former is related to the disorder emerged in both A and B sites. The smaller PNRs can be activated at lower temperature but have smaller electrical dipole moment. This is the origin of relaxor behavior of Sr5BiTi3Nb7O30 with lower Tm and dielectric permittivity. The PNRs is related to a local structure with a polar space group P4bm, which contributes to the dielectric frequency dispersion of relaxor behavior. This work opens up a promising feasible route to the development of relaxor ferroelectrics in tungsten bronze type oxides.

cond-mat.mtrl-sci↗

arXiv2021

Nanoparticle Radiosensitization: from extended local effect modeling to a survival modificationframework of compound Poisson additive killing and its carbon dots validation

Objective: To construct an analytical model instead of local effect modeling for the prediction of the biological effectiveness of nanoparticle radiosensitization. Approach: An extended local effects model is first proposed with a more comprehensive description of the nanoparticles mediated local killing enhancements, but meanwhile puts forward challenging issues that remain difficult and need to be further studied. As a novel method instead of local effect modeling, a survival modification framework of compound Poisson additive killing is proposed, as the consequence of an independent additive killing by the assumed equivalent uniform doses of individual nanoparticles per cell under the LQ model. A compound Poisson killing (CPK)model based on the framework is thus derived, giving a general expression of nanoparticle mediated LQ parameter modification. For practical use, a simplified form of the model is also derived, as a concentration dependent correction only to the α parameter, with the relative correction (alpha"/alpha)) dominated by the mean number, and affected by the agglomeration of nanoparticles per cell. For different agglomeration state, a monodispersion model of the dispersity factor η=1, and an agglomeration model of 2/3<η<1,are provided for practical prediction of (alpha"/alpha) value respectively. Main results: Initial validation by the radiosensitization ofHepG2 cells by carbon dots showed a high accuracy of the CPK model. In a safe range of concentration (0.003-0.03 μg/μL)of the carbon dots, the prediction errors of the monodispersion and agglomeration models were both within 2%, relative to the clonogenic survival data of the sensitized HepG2 cells.

physics.med-ph↗

arXiv2021

Angle dependent field-driven reorientation transitions in uniaxial antiferromagnet MnBi$_2$Te$_4$ single crystal

MnBi$_2$Te$_4$, a two-dimensional magnetic topological insulator with a uniaxial antiferromagnetic structure, is an ideal platform to realize quantum anomalous Hall effect. However, the strength of magnetic interactions is not clear yet. We performed systematic studies on the magnetization and angle dependent magnetotransport of MnBi$_2$Te$_4$ single crystal. The results show that the direction of the magnetic field has significant effects on the critical field values and magnetic structure of this compound, which leads to different magnetotransport behaviors. The field-driven reorientation transitions can be utilized to estimate the AFM interlayer exchange interaction coupling and uniaxial magnetic anisotropy D. The obtained Hamiltonian can well explain the experimental data by Monte Carlo simulations. Our comprehensive studies on the field-driven magnetic transitions phenomenon in MnBi$_2$Te$_4$ provide a general approach for other topological systems with antiferromagnetism.

cond-mat.mtrl-sci↗

arXiv2021

Rapid Assessments of Light-Duty Gasoline Vehicle Emissions Using On-Road Remote Sensing and Machine Learning

In-time and accurate assessments of on-road vehicle emissions play a central role in urban air quality and health policymaking. However, official insight is hampered by the Inspection/Maintenance (I/M) procedure conducted in the laboratory annually. It not only has a large gap to real-world situations (e.g., meteorological conditions) but also is incapable of regular supervision. Here we build a unique dataset including 103831 light-duty gasoline vehicles, in which on-road remote sensing (ORRS) measurements are linked to the I/M records based on the vehicle identification numbers and license plates. On this basis, we develop an ensemble model framework that integrates three machining learning algorithms, including neural network (NN), extreme gradient boosting (XGBoost), and random forest (RF). We demonstrate that this ensemble model could rapidly assess the vehicle-specific emissions (i.e., CO, HC, and NO). In particular, the model performs quite well for the passing vehicles under normal conditions (i.e., lower VSP (< 18 kw/t), temperature (6 ~ 32 °C), relative humidity (< 80%), and wind speed (< 5m/s)). Together with the current emission standard, we identify a large number of the dirty (2.33%) or clean (74.92%) vehicles in the real world. Our results show that the ORRS measurements, assisted by the machine-learning-based ensemble model developed here, can realize day-to-day supervision of on-road vehicle-specific emissions. This approach framework provides a valuable opportunity to reform the I/M procedures globally and mitigate urban air pollution deeply.

cs.LG↗

arXiv2021

Adversarially Robust Low Dimensional Representations

Many machine learning systems are vulnerable to small perturbations made to inputs either at test time or at training time. This has received much recent interest on the empirical front due to applications where reliability and security are critical. However, theoretical understanding of algorithms that are robust to adversarial perturbations is limited. In this work we focus on Principal Component Analysis (PCA), a ubiquitous algorithmic primitive in machine learning. We formulate a natural robust variant of PCA where the goal is to find a low dimensional subspace to represent the given data with minimum projection error, that is in addition robust to small perturbations measured in $\ell_q$ norm (say $q=\infty$). Unlike PCA which is solvable in polynomial time, our formulation is computationally intractable to optimize as it captures a variant of the well-studied sparse PCA objective as a special case. We show the following results: -Polynomial time algorithm that is constant factor competitive in the worst-case with respect to the best subspace, in terms of the projection error and the robustness criterion. -We show that our algorithmic techniques can also be made robust to adversarial training-time perturbations, in addition to yielding representations that are robust to adversarial perturbations at test time. Specifically, we design algorithms for a strong notion of training-time perturbations, where every point is adversarially perturbed up to a specified amount. -We illustrate the broad applicability of our algorithmic techniques in addressing robustness to adversarial perturbations, both at training time and test time. In particular, our adversarially robust PCA primitive leads to computationally efficient and robust algorithms for both unsupervised and supervised learning problems such as clustering and learning adversarially robust classifiers.

cs.DS↗

arXiv2021

Query Complexity of Least Absolute Deviation Regression via Robust Uniform Convergence

Consider a regression problem where the learner is given a large collection of $d$-dimensional data points, but can only query a small subset of the real-valued labels. How many queries are needed to obtain a $1+ε$ relative error approximation of the optimum? While this problem has been extensively studied for least squares regression, little is known for other losses. An important example is least absolute deviation regression ($\ell_1$ regression) which enjoys superior robustness to outliers compared to least squares. We develop a new framework for analyzing importance sampling methods in regression problems, which enables us to show that the query complexity of least absolute deviation regression is $Θ(d/ε^2)$ up to logarithmic factors. We further extend our techniques to show the first bounds on the query complexity for any $\ell_p$ loss with $p\in(1,2)$. As a key novelty in our analysis, we introduce the notion of robust uniform convergence, which is a new approximation guarantee for the empirical loss. While it is inspired by uniform convergence in statistical learning, our approach additionally incorporates a correction term to avoid unnecessary variance due to outliers. This can be viewed as a new connection between statistical learning theory and variance reduction techniques in stochastic optimization, which should be of independent interest.

cs.LG↗

arXiv2021

The FAST Galactic Plane Pulsar Snapshot survey: I. Project design and pulsar discoveries

Discovery of pulsars is one of the main goals for large radio telescopes. The Five-hundred-meter Aperture Spherical radio Telescope (FAST), that incorporates an L-band 19-beam receiver with a system temperature of about 20~K, is the most sensitive radio telescope utilized for discovering pulsars. We designed the {\it snapshot} observation mode for a FAST key science project, the Galactic Plane Pulsar Snapshot (GPPS) survey, in which every four nearby pointings can observe {\it a cover} of a sky patch of 0.1575 square degrees through beam-switching of the L-band 19-beam receiver. The integration time for each pointing is 300 seconds so that the GPPS observations for a cover can be made in 21 minutes. The goal of the GPPS survey is to discover pulsars within the Galactic latitude of $\pm10^{\circ}$ from the Galactic plane, and the highest priority is given to the inner Galaxy within $\pm5^{\circ}$. Up to now, the GPPS survey has discovered 201 pulsars, including currently the faintest pulsars which cannot be detected by other telescopes, pulsars with extremely high dispersion measures (DMs) which challenge the currently widely used models for the Galactic electron density distribution, pulsars coincident with supernova remnants, 40 millisecond pulsars, 16 binary pulsars, some nulling and mode-changing pulsars and rotating radio transients (RRATs). The follow-up observations for confirmation of new pulsars have polarization-signals recorded for polarization profiles of the pulsars. Re-detection of previously known pulsars in the survey data also leads to significant improvements in parameters for 64 pulsars. The GPPS survey discoveries are published and will be updated at http://zmtt.bao.ac.cn/GPPS/ .

astro-ph.HE↗

arXiv2020

Role of Digital Twin in Optical Communication: Fault Management, Hardware Configuration, and Transmission Simulation

Optical communication is developing rapidly in the directions of hardware resource diversification, transmission system flexibility, and network function virtualization. Its proliferation poses a significant challenge to traditional optical communication management and control systems. Digital twin (DT), a technology that utilizes data, models, and algorithms and integrates multiple disciplines, acts as a bridge between the real and virtual worlds for comprehensive connectivity. In the digital space, virtual models are stablished dynamically to simulate and describe the states, behaviors, and rules of physical objects in the physical space. DT has been significantly developed and widely applied in industrial and military fields. This study introduces the DT technology to optical communication through interdisciplinary crossing and proposes a DT framework suitable for optical communication. The intelligent fault management model, flexible hardware configuration model, and dynamic transmission simulation model are established in the digital space with the help of deep learning algorithms to ensure the highreliability operation and high-efficiency management of optical communication systems and networks.

cs.NI↗

arXiv2020

Radio Frequency Interference Mitigation based on the ArPLS and SumThreshold Method

As radio telescopes become sensitive, radio frequency interference (RFI) is more and more serious for interesting signals of radio astronomy. There exist demands for developing an automatic, accurate and efficient RFI mitigation method. Therefore, this work investigated the RFI detection algorithm. Firstly, we introduced an Asymmetrically Reweighted Penalized Least Squares (ArPLS) method to estimate baseline more accurately. After removing the estimated baseline, several novel strategies were proposed based on the SumThreshold algorithm for detecting different types of RFI. The threshold parameter in the SumThreshold can be determined automatically and adaptively. The adaptiveness is essential for reducing human interventions and the online RFI processing pipeline. Applications to FAST (Five-hundred-meter Aperture Spherical Telescope) data show that the proposed scheme based on the ArPLS and SumThreshold is superior to some typically available methods for RFI detection with respect to efficiency and performance.

astro-ph.IM↗

arXiv2020

Estimating Principal Components under Adversarial Perturbations

Robustness is a key requirement for widespread deployment of machine learning algorithms, and has received much attention in both statistics and computer science. We study a natural model of robustness for high-dimensional statistical estimation problems that we call the adversarial perturbation model. An adversary can perturb every sample arbitrarily up to a specified magnitude $δ$ measured in some $\ell_q$ norm, say $\ell_\infty$. Our model is motivated by emerging paradigms such as low precision machine learning and adversarial training. We study the classical problem of estimating the top-$r$ principal subspace of the Gaussian covariance matrix in high dimensions, under the adversarial perturbation model. We design a computationally efficient algorithm that given corrupted data, recovers an estimate of the top-$r$ principal subspace with error that depends on a robustness parameter $κ$ that we identify. This parameter corresponds to the $q \to 2$ operator norm of the projector onto the principal subspace, and generalizes well-studied analytic notions of sparsity. Additionally, in the absence of corruptions, our algorithmic guarantees recover existing bounds for problems such as sparse PCA and its higher rank analogs. We also prove that the above dependence on the parameter $κ$ is almost optimal asymptotically, not just in a minimax sense, but remarkably for every instance of the problem. This instance-optimal guarantee shows that the $q \to 2$ operator norm of the subspace essentially characterizes the estimation error under adversarial perturbations.

cs.LG↗

arXiv2019

Testing noisy linear functions for sparsity

We consider the following basic inference problem: there is an unknown high-dimensional vector $w \in \mathbb{R}^n$, and an algorithm is given access to labeled pairs $(x,y)$ where $x \in \mathbb{R}^n$ is a measurement and $y = w \cdot x + \mathrm{noise}$. What is the complexity of deciding whether the target vector $w$ is (approximately) $k$-sparse? The recovery analogue of this problem --- given the promise that $w$ is sparse, find or approximate the vector $w$ --- is the famous sparse recovery problem, with a rich body of work in signal processing, statistics, and computer science. We study the decision version of this problem (i.e.~deciding whether the unknown $w$ is $k$-sparse) from the vantage point of property testing. Our focus is on answering the following high-level question: when is it possible to efficiently test whether the unknown target vector $w$ is sparse versus far-from-sparse using a number of samples which is completely independent of the dimension $n$? We consider the natural setting in which $x$ is drawn from a i.i.d.~product distribution $\mathcal{D}$ over $\mathbb{R}^n$ and the $\mathrm{noise}$ process is independent of the input $x$. As our main result, we give a general algorithm which solves the above-described testing problem using a number of samples which is completely independent of the ambient dimension $n$, as long as $\mathcal{D}$ is not a Gaussian. In fact, our algorithm is fully noise tolerant, in the sense that for an arbitrary $w$, it approximately computes the distance of $w$ to the closest $k$-sparse vector. To complement this algorithmic result, we show that weakening any of our condition makes it information-theoretically impossible for any algorithm to solve the testing problem with fewer than essentially $\log n$ samples.

cs.CC↗

arXiv2019

Reconstruction under outliers for Fourier-sparse functions

We consider the problem of learning an unknown $f$ with a sparse Fourier spectrum in the presence of outlier noise. In particular, the algorithm has access to a noisy oracle for (an unknown) $f$ such that (i) the Fourier spectrum of $f$ is $k$-sparse; (ii) at any query point $x$, the oracle returns $y$ such that with probability $1-ρ$, $|y-f(x)| \le ε$. However, with probability $ρ$, the error $y-f(x)$ can be arbitrarily large. We study Fourier sparse functions over both the discrete cube $\{0,1\}^n$ and the torus $[0,1)$ and for both these domains, we design efficient algorithms which can tolerate any $ρ<1/2$ fraction of outliers. We note that the analogous problem for low-degree polynomials has recently been studied in several works~[AK03, GZ16, KKP17] and similar algorithmic guarantees are known in that setting. While our main results pertain to the case where the location of the outliers, i.e., $x$ such that $|y-f(x)|>ε$ is randomly distributed, we also study the case where the outliers are adversarially located. In particular, we show that over the torus, assuming that the Fourier transform satisfies a certain \emph{granularity} condition, there is a sample efficient algorithm to tolerate $ρ=Ω(1)$ fraction of outliers and further, that this is not possible without such a granularity condition. Finally, while not the principal thrust, our techniques also allow us non-trivially improve on learning low-degree functions $f$ on the hypercube in the presence of adversarial outlier noise. Our techniques combine a diverse array of tools from compressive sensing, sparse Fourier transform, chaining arguments and complex analysis.

cs.DS↗

arXiv2019

Whittaker modules for the twisted affine Nappi-Witten Lie algebra $\widehat{H}_{4}[τ]$

The Whittaker module $M_ψ$ and its quotient Whittaker module $L_{ψ, ξ}$ for the twisted affine Nappi-Witten Lie algebra $\widehat{H}_{4}[τ]$ are studied. For nonsingular type, it is proved that if $ξ\neq 0$, then $L_{ψ,ξ}$ is irreducible and any irreducible Whittaker $\widehat{H}_{4}[τ]$-module of type $ψ$ with ${\bf k}$ acting as a non-zero scalar $ξ$ is isomorphic to $L_{ψ,ξ}$. Furthermore, for $ξ=0$, all Whittaker vectors of $L_{ψ, 0}$ are completely determined. For singular type, the Whittaker vectors of $L_{ψ, ξ}$ with $ξ\neq 0$ are fully characterized.

math.RT↗

arXiv2019

Estimating the Frequency of a Clustered Signal

We consider the problem of locating a signal whose frequencies are "off grid" and clustered in a narrow band. Given noisy sample access to a function $g(t)$ with Fourier spectrum in a narrow range $[f_0 - Δ, f_0 + Δ]$, how accurately is it possible to identify $f_0$? We present generic conditions on $g$ that allow for efficient, accurate estimates of the frequency. We then show bounds on these conditions for $k$-Fourier-sparse signals that imply recovery of $f_0$ to within $Δ+ \tilde{O}(k^3)$ from samples on $[-1, 1]$. This improves upon the best previous bound of $O\big( Δ+ \tilde{O}(k^5) \big)^{1.5}$. We also show that no algorithm can do better than $Δ+ \tilde{O}(k^2)$. In the process we provide a new $\tilde{O}(k^3)$ bound on the ratio between the maximum and average value of continuous $k$-Fourier-sparse signals, which has independent application.

cs.DS↗

arXiv2018

Turbulence-Resistant All Optical Relaying Based on Few-Mode EDFA in Free-Space Optical Systems

We investigate the communication performance of a few-mode EDFA based all-optical relaying system for atmospheric channels in this paper. A dual-hop free space optical communication model based on the relay with two-mode EDFA is derived. The BER performance is numerically calculated. Compared with all-optical relaying system with single-mode EDFA, the power budget is increased by 4 dB, 7.5 dB and 11.5 dB at BER = 1E-4 under the refractive index structure constant Cn2 = 2E-14, 5E-14 and 1E-13 respectively when a few mode fiber supporting 4 modes is utilized as the receiving fiber at the destination. The optimal relay location is slightly backward from the middle of the link. The BER performance is the best when mode-dependent gain of FM-EDFA is zero.

eess.SP↗