arXiv subjects

Qian Xiao

Publications and source records attributed to Qian Xiao.

At least 19 recordsLinked to original sources

arXiv2026

Space-filling foldover designs for order-of-addition experiments under Kendall tau distance criteria

Order-of-addition experiments arise when the response depends on the order in which a set of components is added. Since the number of possible orders increases factorially with the number of components, full permutation designs are rarely feasible except for small problems. This paper studies space-filling fractional designs for order-of-addition experiments based on the Kendall tau distance, a natural metric for comparing permutations through pairwise ordering disagreements. We consider the maximin Kendall tau distance criterion and related dispersion criteria, and establish their connections with statistical optimality under the pairwise ordering model and a Gaussian process model with the Mallows kernel. To construct such designs, we propose an efficient foldover simulated annealing algorithm, denoted by FSA-KD, based on swap moves in the permutation space, together with foldover and incremental updating strategies. Numerical studies show that the resulting FSA-KD designs have large minimum pairwise Kendall tau distances, denoted by k_min(D), and stable pairwise distance distributions, and perform well in surrogate modeling and permutation-based optimization tasks.

stat.ME↗

arXiv2026

PoliLegalLM: A Technical Report on a Large Language Model for Political and Legal Affairs

Large language models (LLMs) have achieved remarkable success in general-domain tasks, yet their direct application to the legal domain remains challenging due to hallucinated legal citations, incomplete knowledge coverage, and weak structured reasoning. To address these issues, we propose PoliLegalLM, a domain-specific large language model tailored for political and legal applications. Our approach adopts a unified training framework that integrates continued pretraining, progressive supervised fine-tuning, and preference-based reinforcement learning to jointly enhance legal knowledge grounding, task alignment, and reasoning capability. We construct a large-scale, high-quality legal corpus and design a structured post-training pipeline, enabling the model to effectively learn domain-specific knowledge and adapt to diverse legal tasks. We evaluate PoliLegalLM on three representative benchmarks, including LawBench, LexEval, and a real-world dataset, PoliLegal. Experimental results demonstrate that PoliLegalLM achieves strong and consistent performance, outperforming competitive models of similar scale and remaining highly competitive with significantly larger models, while achieving the best results on real-world legal scenarios. These results highlight the effectiveness of our training paradigm and the practical value of domain-specific LLMs for real-world legal applications.

cs.CL↗

arXiv2026

Chemical and optical control of chiral-domain dynamics in 1T-TaS$_2$

Optical control of symmetry-breaking quantum phases is often constrained when one domain is strongly favored in equilibrium. This limitation is exemplified by the chiral charge-density-wave (CDW) order in 1T-TaS$_2$, where pristine samples predominantly select a single chirality. Here we show that free-energy landscape engineering through Ti substitution enables a distinct nonthermal pathway for ultrafast chiral-domain redistribution. Ti doping stabilizes coexisting chiral domains and tunes their relative stability, allowing femtosecond excitation to drive an asymmetric and anisotropic redistribution from the dominant toward the minority chirality. The subpicosecond response follows a $\sim2$ THz amplitude mode and is consistent with a phonon-assisted pathway involving transient domain-wall configurations. Our results establish free-energy landscape engineering as a strategy for selecting nonequilibrium transition pathways.

cond-mat.str-el↗

arXiv2026

A new baseline for edge detection: Make Encoder-Decoder great again

The performance of deep learning based edge detector has far exceeded that of humans, but the huge computational cost and complex training strategy hinder its further development and application. In this paper, we eliminate these complexities with a vanilla encoder-decoder based detector. Firstly, we design a bilateral encoder to decouple the extraction process of location features and semantic features. Since the location branch no longer provides cues for the semantic branch, the richness of features can be further compressed, which is the key to make our model more compact. We propose a cascaded feature fusion decoder, where the location features are progressively refined by semantic features. The refined location features are the only basis for generating the edge map. The coarse original location features and semantic features are avoided from direct contact with the final result. So the noise in the location features and the location error in the semantic features can be suppressed in the generated edge map. The proposed New Baseline for Edge Detection (NBED) achieves superior performance consistently across multiple edge detection benchmarks, even compared with those methods with huge computational cost and complex training strategy. The ODS of NBED on BSDS500 is 0.838, achieving state-of-the-art performance. Our study shows that what really matters in the current edge detection is high-quality features, and we can make the encoder-decoder based detector great again even without complex training strategies and huge computational cost. The code is available at https://github.com/Li-yachuan/NBED.

cs.CV↗

arXiv2026

Evolution from three-dimensional charge density wave to one-dimensional stripe order in CsV$_{3-x}$Ti$_x$Sb$_5$

Understanding intertwined phases near quantum criticality is a central challenge in correlated electron systems. The kagome metal CsV$_{3-x}$Ti$_x$Sb$_5$ provides a fertile platform to investigate the interplay between charge-density-wave (CDW) and superconductivity. Here, combining x-ray diffraction (XRD) and scanning tunneling microscopy (STM), we uncover a dimensional evolution of the CDW upon Ti substitution. We find that even infinitesimal Ti doping (x = 0.009) completely suppresses the three-dimensional 2 $\times$ 2 $\times$ 4 CDW present in pristine CsV3Sb5, while reducing the remaining 2 $\times$ 2 $\times$ 2 CDW to a quasi-two-dimensional order. With further Ti substitution, although no CDW transition is discernible in resistivity measurements, our XRD and STM data reveal the emergence of a (quasi-)one-dimensional CDW with a short correlation length of $\sim$ 20 $\AA$ at x = 0.2. The stripelike CDW undergoes a continuous second-order phase transition, characterized by a gradual increase in intensity and correlation length below $\sim$ 56 K. Our results elucidate the dimensional evolution of CDW order in CsV$_{3-x}$Ti$_x$Sb$_5$ and provide new insight into understanding the unconventional CDWs and their role in kagome superconductors.

cond-mat.str-el↗

arXiv2025

Tongyi DeepResearch Technical Report

We present Tongyi DeepResearch, an agentic large language model, which is specifically designed for long-horizon, deep information-seeking research tasks. To incentivize autonomous deep research agency, Tongyi DeepResearch is developed through an end-to-end training framework that combines agentic mid-training and agentic post-training, enabling scalable reasoning and information seeking across complex tasks. We design a highly scalable data synthesis pipeline that is fully automatic, without relying on costly human annotation, and empowers all training stages. By constructing customized environments for each stage, our system enables stable and consistent interactions throughout. Tongyi DeepResearch, featuring 30.5 billion total parameters, with only 3.3 billion activated per token, achieves state-of-the-art performance across a range of agentic deep research benchmarks, including Humanity's Last Exam, BrowseComp, BrowseComp-ZH, WebWalkerQA, xbench-DeepSearch, FRAMES and xbench-DeepSearch-2510. We open-source the model, framework, and complete solutions to empower the community.

cs.CL↗

arXiv2025

Distinct orbital contributions to electronic and magnetic structures in La$_{4}$Ni$_{3}$O$_{10}$

High-T$_c$ superconductivity has recently been discovered in Ruddlesden-Popper phase nickelates under pressure, where the low-energy electronic structure is dominated by Ni $d_{x^2 - y^2}$ and $d_{z^2}$ orbitals. However, the respective roles of these orbitals in superconductivity remain unclear. Here, by combining X-ray absorption, electron energy loss spectroscopy, and density functional theory calculations on La$_{4}$Ni$_{3}$O$_{10}$ single crystals, we identify ligand holes in the $p_{x,y}$ orbitals of planar oxygen and the $p_z$ orbitals of apical oxygen, which hybridize with the Ni $d_{x^2-y^2}$ and $d_{z^2}$ orbitals, respectively. These ligand holes enable orbital-selective O K-edge resonant inelastic X-ray scattering (RIXS) study, which reveals that $d_{x^2-y^2}$ states dominate the low-energy charge excitations and are more itinerant. We also observe a $\sim$0.1 eV bimagnon through RIXS and Raman spectroscopy, which leads to an interlayer superexchange interaction J$_z$ of $\sim$50 meV. Our results reveal distinct contributions of Ni $d_{x^2-y^2}$ and $d_{z^2}$ orbitals to the electronic and magnetic structure and provide direct experimental insights to understand the RP-phase nickelate superconductors.

cond-mat.supr-con↗

arXiv2025

Who Is Lagging Behind: Profiling Student Behaviors with Graph-Level Encoding in Curriculum-Based Online Learning Systems

The surge in the adoption of Intelligent Tutoring Systems (ITSs) in education, while being integral to curriculum-based learning, can inadvertently exacerbate performance gaps. To address this problem, student profiling becomes crucial for tracking progress, identifying struggling students, and alleviating disparities among students. Such profiling requires measuring student behaviors and performance across different aspects, such as content coverage, learning intensity, and proficiency in different concepts within a learning topic. In this study, we introduce CTGraph, a graph-level representation learning approach to profile learner behaviors and performance in a self-supervised manner. Our experiments demonstrate that CTGraph can provide a holistic view of student learning journeys, accounting for different aspects of student behaviors and performance, as well as variations in their learning paths as aligned to the curriculum structure. We also show that our approach can identify struggling students and provide comparative analysis of diverse groups to pinpoint when and where students are struggling. As such, our approach opens more opportunities to empower educators with rich insights into student learning journeys and paves the way for more targeted interventions.

cs.AI↗

arXiv2025

Metric Theory for Continued Fractions with Multiple Large Partial Quotients

The presence of large partial quotients can invalidate many classical limit theorems in the metric theory of continued fractions. A commonly employed strategy to overcome this problem is to discard the largest partial quotient when formulating variant forms of such theorems. However, this method will fail when dealing with at least two large partial quotients. Motivated by recent work of Tan, Tian, and Wang [Sci. China Math., 2023], we investigate the metric theory of real numbers that contain at least $r$ large partial quotients among the first $n$ terms of their continued fraction expansions. Specifically, let $[a_1(x), a_2(x), \ldots ]$ be the continued fraction expansion of a real number $x \in [0, 1)$. We determine the Lebesgue measure and Hausdorff dimension of the following set: \[ F(r, ψ)=\Big\{ x \in [0,1): \exists 1 \leq k_1< \cdots < k_r \leq n, a_{k_i} (x) \geq ψ(n)~(i=1, \ldots, r)\ \text{for i.m.}~n \in \mathbb{N} \Big\}, \] where `i.m.' stands for `infinitely many', $r \geq 1$ and $ψ$ is a positive function defined on $\mathbb{N}$.

math.NT↗

arXiv2025

Mixed spin states for robust ferromagnetism in strained SrCoO$_3$ thin films

Epitaxial strain in transition-metal oxides can induce dramatic changes in electronic and magnetic properties. A recent study on the epitaxially strained SrCoO$_3$ thin films revealed persistent ferromagnetism even across a metal-insulator transition. This challenges the current theoretical predictions, and the nature of the local spin state underlying this robustness remains unresolved. Here, we employ high-resolution resonant inelastic x-ray scattering (RIXS) at the Co-$L_3$ edge to probe the spin states of strained SrCoO$_3$ thin films. Compared with CoO$_6$ cluster multiplet calculations, we identify a ground state composed of a mixed high- and low-spin configuration, distinct from the previously proposed intermediate-spin state. Our results demonstrate that the robustness of ferromagnetism arises from the interplay between this mixed spin state and the presence of ligand holes associated with negative charge transfer. These findings provide direct experimental evidence for a nontrivial magnetic ground state in SrCoO$_3$ and offer new pathways for designing robust ferromagnetic systems in correlated oxides.

cond-mat.str-el↗

arXiv2025

Mediation Analysis for Sparse and Irregularly Spaced Longitudinal Outcomes with Application to the MrOS Sleep Study

Mediation analysis has become a widely used method for identifying the pathways through which an independent variable influences a dependent variable via intermediate mediators. However, limited research addresses the case where mediators are high-dimensional and the outcome is represented by sparse, irregularly spaced longitudinal data. To address these challenges, we propose a mediation analysis approach for scalar exposures, high-dimensional mediators, and sparse longitudinal outcomes. This approach effectively identifies significant mediators by addressing two key issues: (i) the underlying correlation structure within the sparse and irregular cognitive measurements, and (ii) adjusting mediation effects to handle the high-dimensional set of candidate mediators. In the MrOS Sleep study, our primary objective is to explore lipid pathways that may mediate the relationship between rest-activity rhythms and longitudinal cognitive decline in older men. Our findings suggest a potential mechanism involving rest-activity rhythms, lipid metabolites, and cognitive decline, and highlight significant mediators identified through multiple testing procedures.

stat.ME↗

arXiv2025

On Dirichlet non-improvable numbers and shrinking target problems

In one-dimensional Diophantine approximation, the Diophantine properties of a real number are characterized by its partial quotients, especially the growth of its large partial quotients. Notably, Kleinbock and Wadleigh [Proc. Amer. Math. Soc. 2018] made a seminal contribution by linking the improvability of Dirichlet's theorem to the growth of the product of consecutive partial quotients. In this paper, we extend the concept of Dirichlet non-improvable sets within the framework of shrinking target problems. Specifically, consider the dynamical system $([0,1), T)$ of continued fractions. Let $\{z_n\}_{n \ge 1}$ be a sequence of real numbers in $[0,1]$ and let $B > 1$. We determine the Hausdorff dimension of the following set: \[ \begin{split} \{x\in[0,1):|T^nx-z_n||T^{n+1}x-Tz_n|<B^{-n}\text{ infinitely often}\}. \end{split} \]

math.DS↗

arXiv2025

Dominant apical-oxygen electron-phonon coupling in HgBa$_2$Ca$_2$Cu$_3$O$_{8+δ}$

How electron-phonon interactions influence high-temperature superconductivity in cuprates remains contested, and their role outside the CuO$_2$ planes has been largely overlooked. The most conspicuous evidence for such coupling is the ubiquitous 70-meV dispersion kink seen by photoemission, yet its microscopic origin is still debated. Here we use oxygen-$K$-edge resonant inelastic X-ray scattering (RIXS) to probe the trilayer cuprate HgBa$_2$Ca$_2$Cu$_3$O$_{8+δ}$ (Hg1223). When both incident photon energy and polarization are tuned to the apical-oxygen $1s\!\rightarrow\!2p_z$ transition, the RIXS spectra exhibit a ladder of at least ten phonon overtones, evenly spaced by 70 meV, whose intensities follow a Franck-Condon envelope, signalling exceptionally strong electron-phonon coupling. Quantitative modelling that incorporates core-hole lifetime evaluation yields an apical-phonon coupling energy of 0.25(1) eV, significantly larger than that of the planar stretching mode. Such a coupling strength offers a strong contender for explaining the universal 70-meV kink and suggests that the dominant electron-phonon channel resides outside the CuO$_2$ planes. By elevating inter-layer lattice dynamics from a peripheral factor to a central actor, our results provide a fresh starting point for theories seeking to reconcile strong correlations, lattice dynamics and high-temperature superconductivity.

cond-mat.supr-con↗

arXiv2025

Chart-HQA: A Benchmark for Hypothetical Question Answering in Charts

Multimodal Large Language Models (MLLMs) have garnered significant attention for their strong visual-semantic understanding. Most existing chart benchmarks evaluate MLLMs' ability to parse information from charts to answer questions. However, they overlook the inherent output biases of MLLMs, where models rely on their parametric memory to answer questions rather than genuinely understanding the chart content. To address this limitation, we introduce a novel Chart Hypothetical Question Answering (HQA) task, which imposes assumptions on the same question to compel models to engage in counterfactual reasoning based on the chart content. Furthermore, we introduce HAI, a human-AI interactive data synthesis approach that leverages the efficient text-editing capabilities of LLMs alongside human expert knowledge to generate diverse and high-quality HQA data at a low cost. Using HAI, we construct Chart-HQA, a challenging benchmark synthesized from publicly available data sources. Evaluation results on 18 MLLMs of varying model sizes reveal that current models face significant generalization challenges and exhibit imbalanced reasoning performance on the HQA task.

cs.CL↗

arXiv2025

Optimal design of experiments with quantitative-sequence factors

A new type of experiment with joint considerations of quantitative and sequence factors is recently drawing much attention in medical science, bio-engineering, and many other disciplines. The input spaces of such experiments are semi-discrete and often very large. Thus, efficient and economical experimental designs are required. Based on the transformations and aggregations of good lattice point sets, we construct a new class of optimal quantitative-sequence (QS) designs that are marginally coupled, pair-balanced, space-filling, and asymptotically orthogonal. The proposed QS designs have a certain flexibility in run and factor sizes and are especially appealing for high-dimensional cases.

stat.ME↗

arXiv2025

Photo-induced Dynamics and Momentum Distribution of Chiral Charge Density Waves in 1T-TiSe$_{2}$

Exploring the photoinduced dynamics of chiral states offers promising avenues for advanced control of condensed matter systems. Photoinduced or photoenhanced chirality in 1T-TiSe$_{2}$ has been suggested as a fascinating platform for optical manipulation of chiral states. However, the mechanisms underlying chirality training and its interplay with the charge density wave (CDW) phase remain elusive. Here, we use time-resolved X-ray diffraction (tr-XRD) with circularly polarized pump lasers to probe the photoinduced dynamics of chirality in 1T-TiSe$_{2}$. We observe a notable ($\sim$20%) difference in CDW intensity suppression between left- and right-circularly polarized pumps. Additionally, we reveal momentum-resolved circular dichroism arising from domains of different chirality, providing a direct link between CDW and chirality. An immediate increase in CDW correlation length upon laser pumping is detected, suggesting the photoinduced expansion of chiral domains. These results both advance the potential of light-driven chirality by elucidating the mechanism driving chirality manipulation in TiSe$_2$, and they demonstrate that tr-XRD with circularly polarized pumps is an effective tool for chirality detection in condensed matter systems.

cond-mat.str-el↗

arXiv2025

EDMB: Edge Detector with Mamba

Transformer-based models have made significant progress in edge detection, but their high computational cost is prohibitive. Recently, vision Mamba have shown excellent ability in efficiently capturing long-range dependencies. Drawing inspiration from this, we propose a novel edge detector with Mamba, termed EDMB, to efficiently generate high-quality multi-granularity edges. In EDMB, Mamba is combined with a global-local architecture, therefore it can focus on both global information and fine-grained cues. The fine-grained cues play a crucial role in edge detection, but are usually ignored by ordinary Mamba. We design a novel decoder to construct learnable Gaussian distributions by fusing global features and fine-grained features. And the multi-grained edges are generated by sampling from the distributions. In order to make multi-granularity edges applicable to single-label data, we introduce Evidence Lower Bound loss to supervise the learning of the distributions. On the multi-label dataset BSDS500, our proposed EDMB achieves competitive single-granularity ODS 0.837 and multi-granularity ODS 0.851 without multi-scale test or extra PASCAL-VOC data. Remarkably, EDMB can be extended to single-label datasets such as NYUDv2 and BIPED. The source code is available at https://github.com/Li-yachuan/EDMB.

cs.CV↗

arXiv2024

Model Failure or Data Corruption? Exploring Inconsistencies in Building Energy Ratings with Self-Supervised Contrastive Learning

Building Energy Rating (BER) stands as a pivotal metric, enabling building owners, policymakers, and urban planners to understand the energy-saving potential through improving building energy efficiency. As such, enhancing buildings' BER levels is expected to directly contribute to the reduction of carbon emissions and promote climate improvement. Nonetheless, the BER assessment process is vulnerable to missing and inaccurate measurements. In this study, we introduce \texttt{CLEAR}, a data-driven approach designed to scrutinize the inconsistencies in BER assessments through self-supervised contrastive learning. We validated the effectiveness of \texttt{CLEAR} using a dataset representing Irish building stocks. Our experiments uncovered evidence of inconsistent BER assessments, highlighting measurement data corruption within this real-world dataset.

cs.LG↗