arXiv subjects

Xiaoli Wang

Publications and source records attributed to Xiaoli Wang.

At least 19 recordsLinked to original sources

arXiv2026

Layer-Dependent Phonons, Excitons, and Magneto-Optical Phenomena in CrSBr: A Mini Review

Two-dimensional layered magnetic materials offer a versatile platform for exploring low-dimensional magnetism and coupled many-body interactions in these materials. Chromium sulfur bromide (CrSBr) is a promising candidate for advanced spintronic and optoelectronic applications because of its intrinsic air stability, semiconducting nature, strong in-plane anisotropy, and A-type antiferromagnetic ordering. This review summarizes recent advances in the understanding of the layer-dependent vibrational and excitonic properties of CrSBr, as well as its magneto-optical response, from bulk crystals to the monolayer limit. We examined its crystal structure, magnetic anisotropy, and interlayer spin reorientation, followed by insights into vibrational dynamics and spin-phonon coupling. Particular emphasis is placed on the excitonic landscape, including magnetic-field-sensitive photoluminescence, localized excitonic states, and the coexistence of Frenkel- and Wannier-Mott excitons in the bandgap. Finally, we discuss the challenges and prospects of harnessing the unique layer-dependent properties of CrSBr in spintronic, magneto-optical, and quantum photonic technologies.

cond-mat.mes-hall↗

arXiv2026

Bifurcation Analysis of a Reaction-Diffusion System with a Cognitive Map Memory Kernel

This paper investigates a single species reaction-diffusion system incorporating a spatiotemporal delay memory kernel, which models the cognitive map of animals, under Neumann boundary conditions. The model can be used to describe the process in which individuals are influenced by historical information during spatial diffusion. An equivalent system construction method with auxiliary variables is introduced to transform the original system into a delay-free coupled reaction-diffusion equation. By employing Fourier modal decomposition and eigenvalue analysis, we conduct stability and bifurcation analyses for both the exponentially decaying weak kernel and the peak type strong kernel, obtaining explicit expressions for the steady state and Hopf bifurcation points. Compared with the model in which the memory term of the continuous-time integral kernel using its own population density, our model exhibits Hopf bifurcations and steady state bifurcations even under a weak kernel because of the introduce of a dynamic cognitive map. This implies that a dynamic cognitive map introduces sufficient flexibility to generate both steady state bifurcations and Hopf bifurcations across a broader range of temporal kernels. Numerical simulations are presented to demonstrate the influence of stable, steady state and Hopf bifurcation regions on the spatiotemporal distribution of solutions.

math.DS↗

arXiv2026

Analytic first-order non-adiabatic coupling matrix elements of spin-adapted open-shell time-dependent density functional theory

While spin-adapted time-dependent density functional theory (TDDFT) approaches significantly improve the excitation energies and gradients of open-shell molecules, the effect of spin-adaptation on non-adiabatic coupling matrix elements (NACMEs) remains unknown for spin-conserving excitations. In this article, we report the derivation, implementation and benchmark studies of the ground state-excited state and excited state-excited state NACMEs of our spin-adapted TDDFT method, X-TDDFT; to our best knowledge, this represents the first implementation of the analytic NACMEs of a spin-adapted TDDFT method. Similar to the X-TDDFT analytic gradients, X-TDDFT NACMEs can be easily implemented on top of an existing U-TDDFT NACME implementation taking into account the restricted open-shell Kohn-Sham (ROKS) reference and the implicit involvement of doubly excited determinants, with acceptable computational overhead. Benchmark calculations reveal that X-TDDFT reduces the error of U-TDDFT NACMEs by 1/3-2/3 (referenced against high-level multireference NACMEs), which leads to large corrections of internal conversion rates (up to two orders of magnitude). In particular, for copper(II) porphyrin, X-TDDFT leads to qualitative revisions of the relative importance of the excited state relaxation pathways, as well as the substituent effects of the internal conversion (IC) rates, suggesting that the error of U-TDDFT NACMEs is not only large but also unsystematic. It is therefore expected that X-TDDFT NACMEs will prove useful in the photophysics/photochemistry studies of open-shell systems such as radicals and transition metal complexes.

physics.chem-ph↗

arXiv2026

Data-driven balanced truncation of K-power bilinear systems

As a special type of bilinear systems, K-power bilinear systems possess a special coupled structure along with nice properties in practice. In this paper, we investigate the data-driven counterpart of balanced truncation for K-power systems. As the standard balanced truncation is performed based on the subsystems of K-power systems, the main idea is to approximate the quantities of each reduced subsystem with the evaluations of transfer functions. We exploit the nice properties of Gramians for K-power systems, and establish the explicit relationship between the main quantities of balanced truncation and the evaluation of transfer functions. As a result, reduced models produced via balanced truncation can be assembled approximately by the sample data of transfer functions, leading to a data-driven balancing truncation method for K-power systems. An advanced procedure is also provided to avoid the complex arithmetic completely and produce real-valued reduced models. Two numerical examples confirm the feasibility and effectiveness of the proposed method.

math.OC↗

arXiv2026

Multimodal Graph Representation Learning with Dynamic Information Pathways

Multimodal graphs, where nodes contain heterogeneous features such as images and text, are increasingly common in real-world applications. Effectively learning on such graphs requires both adaptive intra-modal message passing and efficient inter-modal aggregation. However, most existing approaches to multimodal graph learning are typically extended from conventional graph neural networks and rely on static structures or dense attention, which limit flexibility and expressive node embedding learning. In this paper, we propose a novel multimodal graph representation learning framework with Dynamic information Pathways (DiP). By introducing modality-specific pseudo nodes, DiP enables dynamic message routing within each modality via proximity-guided pseudo-node interactions and captures inter-modality dependence through efficient information pathways in a shared state space. This design achieves adaptive, expressive, and sparse message propagation across modalities with linear complexity. We conduct the link prediction and node classification tasks to evaluate performance and carry out full experimental analyses. Extensive experiments across multiple benchmarks demonstrate that DiP consistently outperforms baselines.

cs.CV↗

arXiv2026

Decomposition-Driven Multi-Table Retrieval and Reasoning for Numerical Question Answering

In this paper, we study the problem of numerical multi-table question answering (MTQA) over large-scale table collections (e.g., online data repositories). This task is essential in many analytical applications. Existing MTQA solutions, such as text-to-SQL or open-domain MTQA methods, are designed for databases and struggle when applied to large-scale table collections. The key limitations include: (1) Limited support for complex table relationships; (2) Ineffective retrieval of relevant tables at scale; (3) Inaccurate answer generation. To overcome these limitations, we propose DMRAL, a Decomposition-driven Multi-table Retrieval and Answering framework for MTQA over large-scale table collections, which consists of: (1) constructing a table relationship graph to capture complex relationships among tables; (2) Table-Aligned Question Decomposer and Coverage-Aware Retriever, which jointly enable the effective identification of relevant tables from large-scale corpora by enhancing the question decomposition quality and maximizing the question coverage of retrieved tables; and (3) Sub-question Guided Reasoner, which produces correct answers by progressively generating and refining the reasoning program based on sub-questions. Experiments on two MTQA datasets demonstrate that DMRAL significantly outperforms existing state-of-the-art MTQA methods, with an average improvement of 24% in table retrieval and 55% in answer accuracy.

cs.DB↗

arXiv2025

Unlock Anionic Behavior of Calcium Through Pressure Engineering

An isolated calcium (Ca) atom has empty d-orbitals under ambient conditions. However, s-d band hybridization has been observed in both elemental Ca and compounds by manipulating thermodynamic conditions. Here, we reveal that the Ca 3d-band can even capture electrons from halogen atoms under pressure, exhibiting anionic behaviors in iodides. We predict a CsCl-type monovalent CaI at above 50 GPa by employing first-principles structural searching and successfully identified the phase at 84 GPa using in situ X-ray diffraction. We further reveal that, due to the effect of orbital broadening, unusual charge transfer from the 5p orbitals of I to the 3d orbitals of Ca in CaI, gradually reverses the ionicity of Ca and becomes the anionic ICa at 485 GPa. Multivalent Ca stabilizes a set of metallic iodides with eight- to ten-fold iodine hyper-coordination. Our findings demonstrate that the valence states of Ca can vary from negative to +2, suggesting much greater complexity of Ca chemistry under ultrahigh pressures.

cond-mat.mtrl-sci↗

arXiv2025

Higher Satisfaction, Lower Cost: A Technical Report on How LLMs Revolutionize Meituan's Intelligent Interaction Systems

Enhancing customer experience is essential for business success, particularly as service demands grow in scale and complexity. Generative artificial intelligence and Large Language Models (LLMs) have empowered intelligent interaction systems to deliver efficient, personalized, and 24/7 support. In practice, intelligent interaction systems encounter several challenges: (1) Constructing high-quality data for cold-start training is difficult, hindering self-evolution and raising labor costs. (2) Multi-turn dialogue performance remains suboptimal due to inadequate intent understanding, rule compliance, and solution extraction. (3) Frequent evolution of business rules affects system operability and transferability, constraining low-cost expansion and adaptability. (4) Reliance on a single LLM is insufficient in complex scenarios, where the absence of multi-agent frameworks and effective collaboration undermines process completeness and service quality. (5) The open-domain nature of multi-turn dialogues, lacking unified golden answers, hampers quantitative evaluation and continuous optimization. To address these challenges, we introduce WOWService, an intelligent interaction system tailored for industrial applications. With the integration of LLMs and multi-agent architectures, WOWService enables autonomous task management and collaborative problem-solving. Specifically, WOWService focuses on core modules including data construction, general capability enhancement, business scenario adaptation, multi-agent coordination, and automated evaluation. Currently, WOWService is deployed on the Meituan App, achieving significant gains in key metrics, e.g., User Satisfaction Metric 1 (USM 1) -27.53% and User Satisfaction Metric 2 (USM 2) +25.51%, demonstrating its effectiveness in capturing user needs and advancing personalized service.

cs.CL↗

arXiv2025

Grazing duration and intensity modulate vegetation dynamics in semi-arid ecosystems with seasonal succession

This study investigates the impacts of grazing duration and intensity on vegetation population dynamics in semi-arid ecosystems characterized by seasonal succession. A novel piecewise periodic model is proposed, dividing the annual cycle into three distinct phases: dry season, growth period and grazing period in wet season. We derive critical thresholds for the durations of the dry season and grazing period that determine the persistence or extinction of a single vegetation species. For two competing species, we analyze how grazing parameters influence competitive outcomes, including exclusion, coexistence, and bistability. Theoretical results are supported by numerical simulations, which illustrate bifurcation diagrams and phase transitions under varying grazing regimes. Our findings provide actionable insights for sustainable grazing management in arid and semi-arid regions.

math.DS↗

arXiv2025

Dynamic Coupling of Infiltration-Soil Moisture Feedback:Emergent Vegetation Patterns in a Water-Vegetation Model

We present a modified water-vegetation model to investigate the mechanistic relationship between infiltration-soil moisture feedback and vegetation pattern in arid/semi-arid ecosystems. Employing Turing pattern formation theory, we drive conditions for diffusion-induced instability and analyze spatiotemporal dynamics near Turing-Hopf bifurcation points. Our key findings include: (i) The system exhibits rich dynamics including multiple stable equilibria, supercritical/subcritical Hopf bifurcations, bubble loops of limit cycles and homoclinic bifurcations. (ii) The system admits Turing-Hopf bifurcation. Using normal form theory, we establish the existence of quasiperiodic solutions and mixedmode oscillations near critical thresholds, providing a mathematical framework for predicting nonlinear ecological regime shifts. (iii) Soil moisture feedbacks govern critical transitions between three distinct ecosystem states: uniform vegetation covering, self-organized spatial patterns (labyrinth/gapped vegetation), and bare soil state, which demonstrates that soil moisture thresholds control the final state selection in this system.

math.DS↗

arXiv2025

Effect of protection zone on the dynamics of a diffusion-advection population-toxicant model

This paper develops and analyzes a diffusion-advection model coupling population dynamics with toxicant transport, incorporating a boundary protection zone. For both upstream and downstream protection zone configurations, we investigate the combined influence of protected zones and key ecological factors on population persistence or extinction. Employing monotone dynamical system theory and eigenvalue analysis, we establish the global dynamics of the population-toxicant coexistence equilibrium. Furthermore, we characterize the parameter dependence governing the stability of the toxicant-only steady state, specifically examining the protected zone length, toxicant effect coefficient on population growth, per-unit contaminant discharge rate, toxicant input rate, diffusion/advection rates, and population natural growth rate. Finally, numerical simulations reveal the complex interplay between the protection zone and toxicant advection rate in significantly shaping population persistence domains.

q-bio.PE↗

arXiv2025

Rethinking LSM-tree based Key-Value Stores: A Survey

LSM-tree is a widely adopted data structure in modern key-value store systems that optimizes write performance in write-heavy applications by using append writes to achieve sequential writes. However, the unpredictability of LSM-tree compaction introduces significant challenges, including performance variability during peak workloads and in resource-constrained environments, write amplification caused by data rewriting during compactions, read amplification from multi-level queries, trade-off between read and write performance, as well as efficient space utilization to mitigate space amplification. Prior studies on LSM-tree optimizations have addressed the above challenges; however, in recent years, research on LSM-tree optimization has continued to propose. The goal of this survey is to review LSM-tree optimization, focusing on representative works in the past five years. This survey first studies existing solutions on how to mitigate the performance impact of LSM-tree flush and compaction and how to improve basic key-value operations. In addition, distributed key-value stores serve multi-tenants, ranging from tens of thousands to millions of users with diverse requirements. We then analyze the new challenges and opportunities in these modern architectures and across various application scenarios. Unlike the existing survey papers, this survey provides a detailed discussion of the state-of-the-art work on LSM-tree optimizations and gives future research directions.

cs.DB↗

arXiv2025

$La_3Pd_2NaO_9$: A High-Valent Insulating Palladate

A high-valent palladate, $La_3Pd_2NaO_9$, has been synthesized for the first time. Single crystals with dimensions of 20 ${\mu}$m on edge were successfully grown using the flux method at 420 $^o$C and 70 bar oxygen pressure. Energy dispersive spectroscopy (EDS) and inductively coupled plasma mass spectroscopy (ICP) measurements show that the atomic ratio of La: (Pd+Na) is 3: 3 and Pd: Na is 2: 1. X-ray photoelectron spectroscopy (XPS) measurements show that the oxidation state of Pd is dominated by +4. Synchrotron X-ray single-crystal diffraction measurements revealed that this material crystallizes in the monoclinic $P2_1/c$ space group with charge ordering of Na and Pd. Real-space imaging via scanning transmission electron microscopy (STEM) confirmed the crystal structure and revealed excellent sample homogeneity. Electrical resistivity measurements show an insulating behavior. Magnetic measurements show an unexpected paramagnetic behavior, which probably originate from a small fraction of high-spin Pd$^{2+}$ evidenced by XPS. The successful growth of $La_3Pd_2NaO_9$ single crystals with a high-valent oxidation state of Pd offers an approach for exploring interesting palladates, including potential bilayer Ruddlesden-Popper palladates analogous to the high temperature superconducting $La_3Ni_2O_7$.

cond-mat.str-el↗

arXiv2025

Data-driven balanced truncation for second-order systems via the approximate Gramians

This paper studies the data-driven balanced truncation (BT) method for second-order systems based on the measurements in the frequency domain. The basic idea is to approximate Gramians used the numerical quadrature rules, and establish the relationship between the main quantities in the procedure of BT with the sample data, which paves the way for the execution of BT in a nonintrusive manner. We construct the structure-preserving reduced models approximately based on the samples of second-order systems with proportional damping, and provide the detailed execution of the data-driven counterpart of BT in real-value arithmetic. The low-rank approximation to the solution of Sylvester equations is also introduced to speed up the process of the proposed approach when a large amount of samples involved in the modeling. The performance of our approach is illustrated in detail via two numerical examples.

math.NA↗

arXiv2025

A New Spatiotemporal Correlation Anomaly Detection Method that Integrates Contrastive Learning and Few-Shot Learning in Wireless Sensor Networks

Detecting anomalies in the data collected by WSNs can provide crucial evidence for assessing the reliability and stability of WSNs. Existing methods for WSN anomaly detection often face challenges such as the limited extraction of spatiotemporal correlation features, the absence of sample labels, few anomaly samples, and an imbalanced sample distribution. To address these issues, a spatiotemporal correlation detection model (MTAD-RD) considering both model architecture and a two-stage training strategy perspective is proposed. In terms of model structure design, the proposed MTAD-RD backbone network includes a retentive network (RetNet) enhanced by a cross-retention (CR) module, a multigranular feature fusion module, and a graph attention network module to extract internode correlation information. This proposed model can integrate the intermodal correlation features and spatial features of WSN neighbor nodes while extracting global information from time series data. Moreover, its serialized inference characteristic can remarkably reduce inference overhead. For model training, a two-stage training approach was designed. First, a contrastive learning proxy task was designed for time series data with graph structure information in WSNs, enabling the backbone network to learn transferable features from unlabeled data using unsupervised contrastive learning methods, thereby addressing the issue of missing sample labels in the dataset. Then, a caching-based sample sampler was designed to divide samples into few-shot and contrastive learning data. A specific joint loss function was developed to jointly train the dual-graph discriminator network to address the problem of sample imbalance effectively. In experiments carried out on real public datasets, the designed MTAD-RD anomaly detection method achieved an F1 score of 90.97%, outperforming existing supervised WSN anomaly detection methods.

cs.LG↗

arXiv2025

Enhanced Probabilistic Collision Detection for Motion Planning Under Sensing Uncertainty

Probabilistic collision detection (PCD) is essential in motion planning for robots operating in unstructured environments, where considering sensing uncertainty helps prevent damage. Existing PCD methods mainly used simplified geometric models and addressed only position estimation errors. This paper presents an enhanced PCD method with two key advancements: (a) using superquadrics for more accurate shape approximation and (b) accounting for both position and orientation estimation errors to improve robustness under sensing uncertainty. Our method first computes an enlarged surface for each object that encapsulates its observed rotated copies, thereby addressing the orientation estimation errors. Then, the collision probability under the position estimation errors is formulated as a chance-constraint problem that is solved with a tight upper bound. Both the two steps leverage the recently developed normal parameterization of superquadric surfaces. Results show that our PCD method is twice as close to the Monte-Carlo sampled baseline as the best existing PCD method and reduces path length by 30% and planning time by 37%, respectively. A Real2Sim2Real pipeline further validates the importance of considering orientation estimation errors, showing that the collision probability of executing the planned path in simulation is only 2%, compared to 9% and 29% when considering only position estimation errors or no errors at all.

cs.RO↗

arXiv2025

TempoGPT: Enhancing Time Series Reasoning via Quantizing Embedding

Multi-modal language model has made advanced progress in vision and audio, but still faces significant challenges in dealing with complex reasoning tasks in the time series domain. The reasons are twofold. First, labels for multi-modal time series data are coarse and devoid of analysis or reasoning processes. Training with these data cannot improve the model's reasoning capabilities. Second, due to the lack of precise tokenization in processing time series, the representation patterns for temporal and textual information are inconsistent, which hampers the effectiveness of multi-modal alignment. To address these challenges, we propose a multi-modal time series data construction approach and a multi-modal time series language model (TLM), TempoGPT. Specially, we construct multi-modal data for complex reasoning tasks by analyzing the variable-system relationships within a white-box system. Additionally, proposed TempoGPT achieves consistent representation between temporal and textual information by quantizing temporal embeddings, where temporal embeddings are quantized into a series of discrete tokens using a predefined codebook; subsequently, a shared embedding layer processes both temporal and textual tokens. Extensive experiments demonstrate that TempoGPT accurately perceives temporal information, logically infers conclusions, and achieves state-of-the-art in the constructed complex time series reasoning tasks. Moreover, we quantitatively demonstrate the effectiveness of quantizing temporal embeddings in enhancing multi-modal alignment and the reasoning capabilities of TLMs. Code and data are available at https://github.com/zhanghaochuan20/TempoGPT.

cs.LG↗

arXiv2025

Sensorformer: Cross-patch attention with global-patch compression is effective for high-dimensional multivariate time series forecasting

Among the existing Transformer-based multivariate time series forecasting methods, iTransformer, which treats each variable sequence as a token and only explicitly extracts cross-variable dependencies, and PatchTST, which adopts a channel-independent strategy and only explicitly extracts cross-time dependencies, both significantly outperform most Channel-Dependent Transformer that simultaneously extract cross-time and cross-variable dependencies. This indicates that existing Transformer-based multivariate time series forecasting methods still struggle to effectively fuse these two types of information. We attribute this issue to the dynamic time lags in the causal relationships between different variables. Therefore, we propose a new multivariate time series forecasting Transformer, Sensorformer, which first compresses the global patch information and then simultaneously extracts cross-variable and cross-time dependencies from the compressed representations. Sensorformer can effectively capture the correct inter-variable correlations and causal relationships, even in the presence of dynamic causal lags between variables, while also reducing the computational complexity of pure cross-patch self-attention from $O(D^2 \cdot Patch\_num^2 \cdot d\_model)$ to $O(D^2 \cdot Patch\_num \cdot d\_model)$. Extensive comparative and ablation experiments on 9 mainstream real-world multivariate time series forecasting datasets demonstrate the superiority of Sensorformer. The implementation of Sensorformer, following the style of the Time-series-library and scripts for reproducing the main results, is publicly available at https://github.com/BigYellowTiger/Sensorformer

cs.LG↗