arXiv subjects

Xuan Liang

Publications and source records attributed to Xuan Liang.

8 recordsLinked to original sources

arXiv2026

Quasi two dimensional magnetic structure of the triclinic double perovskite Ca$_2$CuWO$_6$

We present the antiferromagnetic ground state of the triclinic double perovskite Ca$_2$CuWO$_6$solved by neutron powder diffraction, and analyze it in direct comparison with tetragonal Sr$_2$CuWO$_6$. Below $T_\mathrm{N} \simeq 32$ K, the magnetic Bragg reflections of Ca$_2$CuWO$_6$ are indexed by the commensurate propagation vector $\mathbf{k}=(\tfrac{1}{2},\tfrac{1}{2},0)$ in its native $P\bar{1}$ cell. Rietveld refinement yields a collinear structure with equal-magnitude, antiparallel moments on the crystallographically inequivalent Cu1 and Cu2 sites and an ordered moment of $0.66(3)~\mu_{\mathrm B}$ per Cu at 1.5 K. A direct comparison with Sr$_2$CuWO$_6$ is obscured by different crystallographic settings and orientations of the cooperative Jahn-Teller elongation axes. Hence, we introduce a common crystallographic supercell through which effects of symmetry lowering from tetragonal to triclinic in the double perovskite are explored: Apparently different magnetic propagation vectors map onto the same supercell wave vector, revealing a common magnetic structure stabilized by tungsten-mediated, second-neighbor interactions. Mean field calculations further show that tetragonal symmetry preserves the degeneracy of four magnetic $\mathbf{k}$-domains in Sr$_2$CuWO$_6$, whereas the triclinic splitting of symmetry-related exchange pathways in Ca$_2$CuWO$_6$, most strongly within the Cu2 network, selects a single $\mathbf{k}$-domain. These results establish the magnetic ground state of Ca$_2$CuWO$_6$ and show how symmetry breaking selects a given ordered state without changing the underlying magnetic motif of the Sr analogue.

cond-mat.str-el↗

arXiv2026

High-pressure synthesis of quantum magnet M-YbTaO4 with a stretched diamond lattice

We report bulk magnetic properties of ytterbium tantalate in its monoclinic fergusonite modification, M-YbTaO4. The spin-1/2 Yb3+ ions in this phase are arranged on a geometrically frustrated "stretched diamond" lattice. M-YbTaO4 cannot be prepared at ambient pressure and was instead prepared in a belt-type apparatus at 6 GPa and 1800 C. Susceptibility and specific heat data show no long-range ordering down to 1.8 K and are consistent with a Jeff = 1/2 Kramers doublet which splits in an applied field. Furthermore, under high-pressure synthesis the entire solid solution YbNbxTa1-xO4 (0 < x < 1) can be stabilised in the M phase, in contrast to ambient-pressure synthesis which favours the competing M' phase for Ta-rich compositions. Subsequent annealing of the Nb-Ta mixed samples resulted in colour changes, suggesting oxygen deficiency in some of the as-prepared high pressure samples. There was little variation in the bulk magnetic properties upon varying either the Nb/Ta ratio or the annealing conditions.

cond-mat.mtrl-sci↗

arXiv2025

Subbagging Variable Selection for Big Data

This article introduces a subbagging (subsample aggregating) approach for variable selection in regression within the context of big data. The proposed subbagging approach not only ensures that variable selection is scalable given the constraints of available computational resources, but also preserves the statistical efficiency of the resulting estimator. In particular, we propose a subbagging loss function that aggregates the least-squares approximations of the loss function for each subsample. Subsequently, we penalize the subbagging loss function via an adaptive LASSO-type regularizer, and obtain a regularized estimator to achieve variable selection. We then demonstrate that the regularized estimator exhibits $\sqrt{N}$-consistency and possesses the oracle properties, where $N$ represents the size of the full sample in the big data. In addition, we propose a subbagging Bayesian information criterion to select the regularization parameter, ensuring that the regularized estimator achieves selection consistency. Simulation experiments are conducted to demonstrate the numerical performance. A U.S. census dataset is analyzed to illustrate the usefulness and computational scalability of the subbagging variable selection method.

stat.ME↗

arXiv2025

On Robust Aggregation for Distributed Data

When data are stored across multiple locations, directly pooling all the data together for statistical analysis may be impossible due to communication costs and privacy concerns. Distributed computing systems allow the analysis of such data, by getting local servers to separately process their own statistical analyses and using a central processor to aggregate the local statistical results. Naive aggregation of local statistics using simple or weighted averages, is vulnerable to contamination within a distributed computing system. This paper develops and investigates a Huber-type aggregation method for locally computed M-estimators to handle contamination in the local estimates. Our implementation of this aggregation method requires estimating the asymptotic variance-covariance matrix of the M-estimator, which we accomplish using a robust spatial median approach. Theoretically, the Huber-type aggregation achieves the same convergence rate as if all the data were pooled. We establish its asymptotic normality for making inferences, including justifying a two-step approach for detecting contamination in the distributed computing system. Extensive simulation studies are conducted to validate the theoretical results and the usefulness of our proposed approach is demonstrated on U.S. airline data.

stat.ME↗

arXiv2023

Quasi-Score Matching Estimation for Spatial Autoregressive Model with Random Weights Matrix and Regressors

With the rapid advancements in technology for data collection, the application of the spatial autoregressive (SAR) model has become increasingly prevalent in real-world analysis, particularly when dealing with large datasets. However, the commonly used quasi-maximum likelihood estimation (QMLE) for the SAR model is not computationally scalable to handle the data with a large size. In addition, when establishing the asymptotic properties of the parameter estimators of the SAR model, both weights matrix and regressors are assumed to be nonstochastic in classical spatial econometrics, which is perhaps not realistic in real applications. Motivated by the machine learning literature, this paper proposes quasi-score matching estimation for the SAR model. This new estimation approach is developed based on the likelihood, but significantly reduces the computational complexity of the QMLE. The asymptotic properties of parameter estimators under the random weights matrix and regressors are established, which provides a new theoretical framework for the asymptotic inference of the SAR-type models. The usefulness of the quasi-score matching estimation and its asymptotic inference is illustrated via extensive simulation studies and a case study of an anti-conflict social network experiment for middle school students.

econ.EM↗

arXiv2021

On the Subbagging Estimation for Massive Data

This article introduces subbagging (subsample aggregating) estimation approaches for big data analysis with memory constraints of computers. Specifically, for the whole dataset with size $N$, $m_N$ subsamples are randomly drawn, and each subsample with a subsample size $k_N\ll N$ to meet the memory constraint is sampled uniformly without replacement. Aggregating the estimators of $m_N$ subsamples can lead to subbagging estimation. To analyze the theoretical properties of the subbagging estimator, we adapt the incomplete $U$-statistics theory with an infinite order kernel to allow overlapping drawn subsamples in the sampling procedure. Utilizing this novel theoretical framework, we demonstrate that via a proper hyperparameter selection of $k_N$ and $m_N$, the subbagging estimator can achieve $\sqrt{N}$-consistency and asymptotic normality under the condition $(k_Nm_N)/N\to α\in (0,\infty]$. Compared to the full sample estimator, we theoretically show that the $\sqrt{N}$-consistent subbagging estimator has an inflation rate of $1/α$ in its asymptotic variance. Simulation experiments are presented to demonstrate the finite sample performances. An American airline dataset is analyzed to illustrate that the subbagging estimate is numerically close to the full sample estimate, and can be computationally fast under the memory constraint.

stat.ME↗

arXiv2019

Actions Generation from Captions

Sequence transduction models have been widely explored in many natural language processing tasks. However, the target sequence usually consists of discrete tokens which represent word indices in a given vocabulary. We barely see the case where target sequence is composed of continuous vectors, where each vector is an element of a time series taken successively in a temporal domain. In this work, we introduce a new data set, named Action Generation Data Set (AGDS) which is specifically designed to carry out the task of caption-to-action generation. This data set contains caption-action pairs. The caption is comprised of a sequence of words describing the interactive movement between two people, and the action is a captured sequence of poses representing the movement. This data set is introduced to study the ability of generating continuous sequences through sequence transduction models. We also propose a model to innovatively combine Multi-Head Attention (MHA) and Generative Adversarial Network (GAN) together. In our model, we have one generator to generate actions from captions and three discriminators where each of them is designed to carry out a unique functionality: caption-action consistency discriminator, pose discriminator and pose transition discriminator. This novel design allowed us to achieve plausible generation performance which is demonstrated in the experiments.

cs.CV↗

arXiv2014

Metric Entropy and the Optimal Prediction of Chaotic Signals

Suppose we are given a time series or a signal $x(t)$ for $0\leq t\leq T$. We consider the problem of predicting the signal in the interval $T<t\leq T+t_{f}$ from a knowledge of its history and nothing more. We ask the following question: what is the largest value of $t_{f}$ for which a prediction can be made? We show that the answer to this question is contained in a fundamental result of information theory due to Wyner, Ziv, Ornstein, and Weiss. In particular, for the class of chaotic signals, the upper bound is $t_{f}\leq\log_{2}T/H$ in the limit $T\rightarrow\infty$, with $H$ being entropy in a sense that is explained in the text. If $\bigl|x(T-s)-x(t^{\ast}-s)\bigr|$ is small for $0\leq s\leqτ$, where $τ$ is of the order of a characteristic time scale, the pattern of events leading up to $t=T$ is similar to the pattern of events leading up to $t=t^{\ast}$. It is reasonable to expect $x(t^{\ast}+t_{f})$ to be a good predictor of $x(T+t_{f}).$ All existing methods for prediction use this idea in some way or the other. Unfortunately, this intuitively reasonable idea is fundamentally deficient and all existing methods fall well short of the Wyner-Ziv entropy bound on $t_{f}$. An optimal predictor should decompose the distance between the pattern of events leading up to $t=T$ and the pattern leading up to $t=t^{\ast}$ into stable and unstable components. A good match should have suitably small unstable components but will in general allow stable components which are as large as the tolerance for correct prediction. For the special case of hyperbolic toral automorphisms, we derive an optimal predictor using Pade approximation.

nlin.CD↗