arXiv subjects

Xin Zhang

Publications and source records attributed to Xin Zhang.

At least 847 records · Page 47Linked to original sources

arXiv2022

Constraints on interacting dark energy models from time-delay cosmography with seven lensed quasars

Measurements of time-delay cosmography of lensed quasars can provide an independent probe to explore the expansion history of the late-time Universe. In this paper, we employ the time-delay cosmography measurements from seven lenses (here abbreviated as the TD data) to constrain interacting dark energy (IDE) models. We mainly focus on the scenario of vacuum energy (with $w=-1$) interacting with cold dark matter, and consider four typical cases of the interaction form $Q$. When the TD data alone are employed, we find that the IDE models with $Q\propto ρ_{\rm de}$ seem to have an advantage in relieving the $H_{0}$ tension between the cosmic microwave background (CMB) and TD data. When the TD data are added to the CMB$+$BAO$+$SN$+H_0$ data, we find that: (i) the coupling parameter $β$ in all the considered IDE models is positive within 1$σ$ range, implying a mild preference for the case of cold dark matter decaying into dark energy; (ii) the IDE model with $Q = βH_{0} ρ_{\rm c}$ slightly relieves the $S_8$ tension, but the other considered IDE models further aggravate this tension; (iii) the Akaike information criteria of the IDE models with $Q \propto ρ_{\rm c}$ are lower than that of the $Λ$CDM model, indicating that these IDE models are more preferred by the current mainstream data. We conclude that the considered IDE models have their own different advantages when the TD data are employed, and none of them can achieve good scores in all aspects.

astro-ph.CO↗

arXiv2022

Anarchic Federated Learning

Present-day federated learning (FL) systems deployed over edge networks consists of a large number of workers with high degrees of heterogeneity in data and/or computing capabilities, which call for flexible worker participation in terms of timing, effort, data heterogeneity, etc. To satisfy the need for flexible worker participation, we consider a new FL paradigm called "Anarchic Federated Learning" (AFL) in this paper. In stark contrast to conventional FL models, each worker in AFL has the freedom to choose i) when to participate in FL, and ii) the number of local steps to perform in each round based on its current situation (e.g., battery level, communication channels, privacy concerns). However, such chaotic worker behaviors in AFL impose many new open questions in algorithm design. In particular, it remains unclear whether one could develop convergent AFL training algorithms, and if yes, under what conditions and how fast the achievable convergence speed is. Toward this end, we propose two Anarchic Federated Averaging (AFA) algorithms with two-sided learning rates for both cross-device and cross-silo settings, which are named AFA-CD and AFA-CS, respectively. Somewhat surprisingly, we show that, under mild anarchic assumptions, both AFL algorithms achieve the best known convergence rate as the state-of-the-art algorithms for conventional FL. Moreover, they retain the highly desirable {\em linear speedup effect} with respect of both the number of workers and local steps in the new AFL paradigm. We validate the proposed algorithms with extensive experiments on real-world datasets.

cs.LG↗

arXiv2022

Gate-Tunable Spin-Orbit Coupling in a Germanium Hole Double Quantum Dot

Hole spins confined in semiconductor quantum dot systems have gained considerable interest for their strong spin-orbit interactions (SOIs) and relatively weak hyperfine interactions. Here we experimentally demonstrate a tunable SOI in a double quantum dot in a Germanium (Ge) hut wire (HW), which could help enable fast all-electric spin manipulations while suppressing unwanted decoherence. Specifically, we measure the transport spectra in the Pauli spin blockade regime in the double quantum dot device.By adjusting the interdot tunnel coupling, we obtain an electric field tuned spin-orbit length lso = 2.0 - 48.9 nm. This tunability of the SOI could pave the way toward the realization of high-fidelity qubits in Ge HW systems.

cond-mat.mes-hall↗

arXiv2022

Ultrafast modulation of the molten metal surface tension under femtosecond laser irradiation

We predict ultrafast modulation of the pure molten metal surface stress fields under the irradiation of the single femtosecond laser pulse through the two-temperature model molecular-dynamics simulations. High-resolution and precision calculations are used to resolve the ultrafast laser-induced anisotropic relaxations of the pressure components on the time-scale comparable to the intrinsic liquid density relaxation time. The magnitudes of the dynamic surface tensions are found being modulated sharply within picoseconds after the irradiation, due to the development of the nanometer scale non-hydrostatic regime behind the exterior atomic layer of the liquid surfaces. The reported novel regulation mechanism of the liquid surface stress field and the dynamic surface tension hints at levitating the manipulation of liquid surfaces, such as ultrafast steering the surface directional transport and patterning.

cond-mat.mtrl-sci↗

arXiv2022

Strongly lensed type Ia supernovae as a precise late-universe probe of measuring the Hubble constant and cosmic curvature

Strongly lensed type Ia supernovae (SNe Ia) are expected to have some advantages in measuring time delays of multiple images, and so they have a great potential to be developed into a powerful late-universe cosmological probe. In this paper, we simulate a sample of lensed SNe Ia with time-delay measurements in the era of the Legacy Survey of Space and Time (LSST). Based on the distance sum rule, we use lensed SNe Ia to implement model-independent constraints on the Hubble constant $H_0$ and cosmic curvature parameter $Ω_K$ in the late universe. We find that if 20 lensed SNe Ia could be observed, the constraint on $H_{0}$ is better than the measurement by the SH0ES collaboration. When the event number of lensed SNe Ia increases to 100, the constraint precision of $H_{0}$ is comparable with the result from Planck 2018 data. Considering 200 lensed SNe Ia events as the optimistic estimation, we obtain $ΔH_0=0.33$ $\rm km\ s^{-1}\ Mpc^{-1}$ and $ΔΩ_K=0.053$. In addition, we also simulate lensed quasars in different scenarios to make a comparison and we find that they are still a useful cosmological probe even though the constraint precision from them is much less than that obtained from lensed SNe Ia. In the era of LSST, the measurements of time delay from both lensed SNe Ia and lensed quasars are expected to yield the results of $ΔH_0=0.26 ~\rm km\ s^{-1}\ Mpc^{-1}$ and $ΔΩ_K=0.044$.

astro-ph.CO↗

arXiv2022

Eliminating Primary Beam Effect in Foreground Subtraction of Neutral Hydrogen Intensity Mapping Survey with Deep Learning

In the neutral hydrogen (HI) intensity mapping (IM) survey, the foreground contamination on the cosmological signals is extremely severe, and the systematic effects caused by radio telescopes themselves further aggravate the difficulties in subtracting foreground. In this work, we investigate whether the deep learning method, concretely the 3D U-Net algorithm here, can play a crucial role in foreground subtraction when considering the systematic effect caused by the telescope's primary beam. We consider two beam models, i.e., the Gaussian beam model as a simple case and the Cosine beam model as a sophisticated case. The traditional principal component analysis (PCA) method is employed as a comparison and, more importantly, as the preprocessing step for the U-Net method to reduce the sky map dynamic range. We find that in the case of the Gaussian beam, the PCA method can effectively clean the foreground. However, the PCA method cannot handle the systematic effect induced by the Cosine beam, and the additional U-Net method can improve the result significantly. In order to show how well the PCA and U-Net methods can recover the HI signals, we also derive the HI angular power spectra, as well as the HI 2D power spectra, after performing the foreground subtractions. It is found that, in the case of Gaussian beam, the concordance with the original HI map using U-Net is better than that using PCA by $27.4\%$, and in the case of Cosine beam, the concordance using U-Net is better than that using PCA by $144.8\%$. Therefore, the U-Net based foreground subtraction can efficiently eliminate the telescope primary beam effect and shed new light on recovering the HI power spectrum for future HI IM experiments.

astro-ph.IM↗

arXiv2022

Approaching a Minimal Topological Electronic Structure in Antiferromagnetic Topological Insulator MnBi2Te4 via Surface Modification

The topological electronic structure plays a central role in the non-trivial physical properties in topological quantum materials. A minimal, hydrogen-atom-like topological electronic structure is desired for researches. In this work, we demonstrate an effort towards the realization of such a system in the intrinsic magnetic topological insulator MnBi2Te4, by manipulating the topological surface state (TSS) via surface modification. Using high resolution laser- and synchrotron-based angle-resolved photoemission spectroscopy (ARPES), we found the TSS in MnBi2Te4 is heavily hybridized with a trivial Rashba-type surface state (RSS), which could be efficiently removed by the in situ surface potassium (K) dosing. By employing multiple experimental methods to characterize K dosed surface, we attribute such a modification to the electrochemical reactions of K clusters on the surface. Our work not only gives a clear band assignment in MnBi2Te4, but also provides possible new routes in accentuating the topological behavior in the magnetic topological quantum materials.

cond-mat.mtrl-sci↗

arXiv2022

An introduction to variational inference in Geophysical inverse problems

In a variety of scientific applications we wish to characterize a physical system using measurements or observations. This often requires us to solve an inverse problem, which usually has non-unique solutions so uncertainty must be quantified in order to define the family of all possible solutions. Bayesian inference provides a powerful theoretical framework which defines the set of solutions to inverse problems, and variational inference is a method to solve Bayesian inference problems using optimization while still producing fully probabilistic solutions. This chapter provides an introduction to variational inference, and reviews its applications to a range of geophysical problems, including petrophysical inversion, travel time tomography and full-waveform inversion. We demonstrate that variational inference is an efficient and scalable method which can be deployed in many practical scenarios.

physics.geo-ph↗

arXiv2022

Next-to-tribimaximal mixing against CP violation and baryon asymmetry signs

Four next-to-tribimaximal (NTBM) mixing patterns are widely considered as the feasible candidates for the observed leptonic mixing structure. With the recent measurements from T2K, as well as intensive global fits, the interval of $\sinδ_{ CP} > 0$ for the Dirac CP-violating phase is persistently small in the normal ordering while $\sinδ_{ CP} < 0$ is successively obtained up to $3σ$ level in the inverted ordering. In this paper, we advocate the fitting results of Dirac CP-violating phase as a constraint, and show that, it can basically rule out half of the regions allowed by the constraint from three mixing angles only. In addition, given the small $\sinδ_{ CP} > 0$ interval, we find that a unique NTBM pattern can be selected out of the four candidates within $3σ$ uncertainty of the latest NuFIT 5.1, when the patterns are exposed to a Yukawa texture-independent leptogenesis for the baryon asymmetry of the Universe. For the surviving pattern, we find an interesting correlation between the Dirac CP-violating phase and the octant of atmospheric angle $θ_{23}$ , where $θ_{23}>45^\circ$ can be predicted once $5π/6 < δ_{CP} <π$ in the normal ordering can be confirmed in the future measurements.

hep-ph↗

arXiv2022

K-core in percolated dense graph sequences

We determine the size of $k$-core in a large class of dense graph sequences. Let $G_n$ be a sequence of undirected, $n$-vertex graphs with edge weights $\{a^n_{i,j}\}_{i,j \in [n]}$ that converges to a kernel $W:[0,1]^2\to [0,+\infty)$ in the cut metric. Keeping an edge $(i,j)$ of $G_n$ with probability $\min \{ {a^n_{i,j}}/{n},1 \}$ independently, we obtain a sequence of random graphs $G_n(\frac{1}{n})$. Denote by $\mathcal{A}$ the property of a branching process that the initial particle has at least $k$ children, each of which has at least $k-1$ children, each of which has at least $k-1$ children, and so on. Using branching process and the theory of dense graph limits, under mild assumptions we obtain the size of $k$-core of random graphs $G_n(\frac{1}{n})$, \begin{align*} \text{size of $k$-core of } G_n\left(\frac{1}{n}\right) =n \mathbb{P}_{X^W}\left(\mathcal{A}\right) +o_p(n). \end{align*} Our result can also be used to obtain the threshold of appearance of a $k$-core of order $n$.

math.PR↗

arXiv2022

Outage and Finite-SNR DMT Analysis for IRS-aided MIMO Systems: How Large IRSs Need to Be?

Intelligent reflecting surfaces (IRSs) are promising enablers for high-capacity wireless communication systems by constructing favorable channels between the transmitter and receiver. However, general, accurate, and tractable outage analysis for IRS-aided multiple-input-multiple-output (MIMO) systems is not available in the literature. In this paper, we first characterize the mutual information (MI) of IRS-aided MIMO systems by capitalizing on large random matrix theory (RMT). Based on this result, a closed-form approximation for the outage probability is derived and a gradient-based algorithm is proposed to minimize the outage probability with statistical channel state information (CSI). We also investigate the diversity-multiplexing tradeoff (DMT) with the finite signal-to-noise ratio (SNR). Based on these theoretical results, we further study the impact of the IRS size on system performance. In the high SNR regime, we provide closed-form expressions for the ergodic mutual information (EMI) and outage probability as a function of the IRS size, which analytically reveal that the benefit of increasing the IRS size saturates quickly. Simulation results validate the accuracy of the theoretical analysis and confirm the increasing cost for deploying larger IRSs to improve system performance. For example, for an IRS-aided MIMO system with 20 antennas at both the transmitter and receiver, we need to double the size of the IRS to increase the throughout from 90% to 95% of its maximum value.

cs.IT↗

arXiv2022

Distributional Robust Portfolio Construction based on Investor Aversion

In behavioral finance, aversion affects investors' judgment of future uncertainty when profit and loss occur. Considering investors' aversion to loss and risk, and the ambiguous uncertainty characterizing asset returns, we construct a distributional robust portfolio model (DRP) under the condition that the distribution of risky asset returns is unknown. Specifically, our objective is to find an optimal portfolio of assets that maximizes the worst-case utility level on the Wasserstein ball, which is centered on the empirical distribution of sample returns and the radius of the ball quantifies the investor's ambiguity level. The model is also formulated as a mixed-integer quadratic programming problem with cardinality constraints. In addition, we propose a hybrid algorithm to improve the efficiency of the solution and make it more suitable for large-scale problems. The distributional robust portfolio model considering aversion is empirically tested for superior performance in asset allocation, and we also compare common asset allocation strategies to further enhance the credibility of the portfolio.

math.OC↗

arXiv2022

Identifying Chinese Opinion Expressions with Extremely-Noisy Crowdsourcing Annotations

Recent works of opinion expression identification (OEI) rely heavily on the quality and scale of the manually-constructed training corpus, which could be extremely difficult to satisfy. Crowdsourcing is one practical solution for this problem, aiming to create a large-scale but quality-unguaranteed corpus. In this work, we investigate Chinese OEI with extremely-noisy crowdsourcing annotations, constructing a dataset at a very low cost. Following zhang et al. (2021), we train the annotator-adapter model by regarding all annotations as gold-standard in terms of crowd annotators, and test the model by using a synthetic expert, which is a mixture of all annotators. As this annotator-mixture for testing is never modeled explicitly in the training phase, we propose to generate synthetic training samples by a pertinent mixup strategy to make the training and testing highly consistent. The simulation experiments on our constructed dataset show that crowdsourcing is highly promising for OEI, and our proposed annotator-mixup can further enhance the crowdsourcing modeling.

cs.CL↗

arXiv2022

Cosmological Parameter Estimation Using Current and Future Observations of Strong Gravitational Lensing

Remarkable development of cosmology is benefited from the increasingly improved measurements of cosmic distances including absolute distances and relative distances. In recent years, however, the emerged cosmological tensions motivate us to explore the independent and precise late-universe probes. The two observational effects of strong gravitational lensing (SGL), the velocity dispersions of lens galaxies and the time delays between multiple images, can provide measurements of relative and absolute distances respectively, and their combination is possible to break the degeneracies between cosmological parameters and enable tight constraints on cosmological parameters. In this paper, we combine the observed 130 SGL systems with velocity-dispersion measurements and 7 SGL systems with time-delay measurements to constrain dark-energy cosmological models. It is found that the combination of the two effects does not significantly break the degeneracies between cosmological parameters as expected. However, with the simulations of 8000 SGL systems with well-measured velocity dispersions and 55 SGL systems with well-measured time delays based on the forthcoming LSST survey, we find that the combination of two effects can significantly break the parameter degeneracies, and make the constraint precision of cosmological parameters meet the standard of precision cosmology. We conclude that the observations of SGL will become a useful late-universe probe for precisely measuring cosmological parameters.

astro-ph.CO↗

arXiv2022

Invariant subspaces and explicit Bethe vectors in the integrable open spin $1/2$ $\XYZ$ chain

We derive a criterion under which splitting of all eigenstates of an open $\XYZ$ Hamiltonian with boundary fields into two invariant subspaces, spanned by chiral shock states, occurs. The splitting is governed by an integer number, which has the geometrical meaning of the maximal number of kinks in the basis states. We describe the generic structure of the respective Bethe vectors. We obtain explicit expressions for Bethe vectors, in the absence of Bethe roots, and those generated by one Bethe root, and investigate the \multiplet. We also describe in detail an elliptic analogue of the spin-helix state, appearing in both the periodic and the open $\XYZ$ model, and derive the eigenstate condition. The elliptic analogue of the spin-helix state is characterized by a quasi-periodic modulation of the magnetization profile, governed by Jacobi elliptic functions.

cond-mat.stat-mech↗

arXiv2022

Surface wave dispersion inversion using an energy likelihood function

Seismic surface wave dispersion inversion is used widely to study the subsurface structure of the Earth. The dispersion property is usually measured by using frequency-phase velocity (f-c) analysis and by picking phase velocities from the obtained f-c spectrum. However, because of potential contamination the f-c spectrum often has multimodalities at each frequency for each mode. These introduce uncertainty and errors in the picked phase velocities, and consequently the obtained shear velocity structure is biased. To overcome this issue, in this study we introduce a new method which directly uses the spectrum as data. We achieve this by solving the inverse problem in a Bayesian framework and define a new likelihood function, the energy likelihood function, which uses the spectrum energy to define data fit. We apply the new method to a land dataset recorded by a dense receiver array, and compare the results to those obtained using the traditional method. The results show that the new method produces more accurate results since they better match independent data from refraction tomography. This real-data application also shows that it can be applied efficiently since it removes the need to pick phase velocities, and with relatively little adjustment to current practice since it uses standard f-c panels to define the likelihood. We therefore recommend using the energy likelihood function rather than explicitly picking phase velocities in surface wave dispersion inversion.

physics.geo-ph↗

arXiv2022

Dynamic Cournot-Nash Equilibrium: The Non-Potential Case

We consider a large population dynamic game in discrete time where players are characterized by time-evolving types. It is a natural assumption that the players' actions cannot anticipate future values of their types. Such games go under the name of dynamic Cournot-Nash equilibria, and were first studied by Acciaio et al., as a time/information dependent version of the games devised by Blanchet and Carlier for the static situation, under an extra assumption that the game is of potential type. The latter means that the game can be reduced to the resolution of an auxiliary variational problem. In the present work we study dynamic Cournot-Nash equilibria in their natural generality, namely going beyond the potential case. As a first result, we derive existence and uniqueness of equilibria under suitable assumptions. Second, we study the convergence of the natural fixed-point iterations scheme in the quadratic case. Finally we illustrate the previously mentioned results in a toy model of optimal liquidation with price impact, which is a game of non-potential kind.

math.OC↗

arXiv2022

Contrastive-mixup learning for improved speaker verification

This paper proposes a novel formulation of prototypical loss with mixup for speaker verification. Mixup is a simple yet efficient data augmentation technique that fabricates a weighted combination of random data point and label pairs for deep neural network training. Mixup has attracted increasing attention due to its ability to improve robustness and generalization of deep neural networks. Although mixup has shown success in diverse domains, most applications have centered around closed-set classification tasks. In this work, we propose contrastive-mixup, a novel augmentation strategy that learns distinguishing representations based on a distance metric. During training, mixup operations generate convex interpolations of both inputs and virtual labels. Moreover, we have reformulated the prototypical loss function such that mixup is enabled on metric learning objectives. To demonstrate its generalization given limited training data, we conduct experiments by varying the number of available utterances from each speaker in the VoxCeleb database. Experimental results show that applying contrastive-mixup outperforms the existing baseline, reducing error rate by 16% relatively, especially when the number of training utterances per speaker is limited.

eess.AS↗