arXiv subjects

Xiaotong Xu

Publications and source records attributed to Xiaotong Xu.

11 recordsLinked to original sources

arXiv2026

A Kagome-Derived Mosaic Lattice Family A3V9Te13 (A = Cs, Rb) with Tunable Strong Electronic Correlations

The pursuit of geometrically frustrated lattices beyond conventional paradigms remains a central challenge in the design of quantum materials. Herein, we report the discovery of the A3V9Te13 (A = Cs, Rb) family of vanadium-based intermetallic compounds, which host a unique two-dimensional Mosaic lattice derived from the Kagome network, composed of an ordered tessellation of triangles, squares, and pentagons. The Cs compound (CVT) exhibits strong electronic correlations, characterized by non-Fermi liquid behavior at low temperatures, an exceptionally large Sommerfeld coefficient, and a bulk phase transition at T* $\approx$ 47 K with possible charge- or spin-related origin. Inspired by pressure-tuning in related Kagome systems, we demonstrate that the electronic ground state of this lattice is exquisitely tunable via chemical pressure. Systematic substitution of Cs with smaller Rb ions suppresses the T* phase transition and the correlated electronic response, ultimately driving the system into a highly frustrated semiconducting ground state without long-range magnetic order down to 60 mK. This work unveils a new structural platform for exploring the interplay between geometric frustration and strong electron correlations, providing a chemically controllable platform for exploring the phase space between distinct correlated electronic states.

cond-mat.mtrl-sci↗

arXiv2026

Efficient Language-to-Vision Feature Injection for Referring Single-Object Tracking

Referring single-object tracking enables language-grounded target initialization and subsequent tracking by jointly leveraging semantic cues and visual templates. The core difficulty is to use language differently across stages: it is indispensable for grounding but can induce semantic drift during tracking when overemphasized. Meanwhile, current methods often require costly vision-language alignment training. We present LVTrack, a pure transformer framework that introduces a mode-conditioned Gated Feature Injector to adaptively regulate textual guidance and alleviate semantic drift. Together with targeted adaptations, it directly harnesses a frozen vision-language pretrained model, greatly reducing training cost and preserving strong language understanding. To further improve temporal localization, LVTrack integrates hybrid relative-absolute positional encodings with a lightweight memory mechanism and optimizes autoregressive box prediction using a Gaussian-smoothed KL loss. Extensive experiments on standard benchmarks demonstrate that LVTrack achieves strong performance.

cs.CV↗

arXiv2026

A Coin Flip Per Token: Bernoulli Sparse Steering of Large Language Models

Activation steering via sparse autoencoders (SAEs) enables behavioral control of large language models without task-specific fine-tuning, but standard methods apply the steering signal at every generated token, incurring constant per-token perturbation that risks degrading fluency. We ask: is dense intervention necessary? We introduce Stochastic Token Steering (STS), which gates each token independently with probability $p$, and Stochastic Block Steering (SBS), which gates a leading window once per sequence; neither requires a reward model or learned gating policy. Across two model families and two behavioral tasks, steering only 50% of the tokens recovers most of the dense-steering effect while preserving fluency, and steering as few as 30% surpasses prompt-based control. The optimal steering magnitude scales inversely with the intervention ratio, revealing that SAE-mediated control is rate-limited: the behavioral outcome depends on cumulative signal dosage across a sequence.

cs.LG↗

arXiv2026

Interfacial Magnetotransport in a NiI$_2$/Graphene Heterostructure

We investigate magnetotransport in a van der Waals heterostructure composed of monolayer graphene and the insulating helical antiferromagnet nickel iodide (NiI$_2$). While NiI$_2$ is highly resistive and thus poorly suited for direct transport measurements, we demonstrate that magnetotransport in an adjacent graphene layer provides an electrical readout of magnetic-state-dependent interfacial behavior. Most notably, first-harmonic longitudinal magnetoresistance under in-plane magnetic fields exhibits large, anisotropic low-field peaks that are absent from a monolayer graphene/h-BN control device and are suppressed above the multiferroic transition temperature of NiI$_2$. Temperature-dependent harmonic measurements provide complementary evidence: the second-harmonic resistance shows the clearest nonlinear contrast relative to the control device, while the third harmonic contains a larger generic nonlinear and thermal background that is nevertheless modified in the heterostructure. These results demonstrate that graphene-based transport measurements offer a sensitive, non-invasive probe of magnetic phase behavior in electrically insulating van der Waals magnets, opening routes toward spintronic devices based on insulating vdW multiferroics.

cond-mat.mes-hall↗

arXiv2026

Stoichiometry-Controlled Structural Order and Tunable Antiferromagnetism in $\mathrm{Fe}_{x}\mathrm{NbSe_2}$ ($0.05 \le x \le 0.38$)

Transition metal dichalcogenides (TMDs) enable magnetic property engineering via intercalation, but stoichiometry-structure-magnetism correlations remain poorly defined for Fe-intercalated $\mathrm{NbSe_2}$. Here, we report a systematic study of $\mathrm{Fe}_{x}\mathrm{NbSe_2}$ across an extended composition range $0.05 \le x \le 0.38$, synthesized via chemical vapor transport and verified by rigorous energy-dispersive x-ray spectroscopy (EDS) microanalysis. X-ray diffraction, magnetic, and transport measurements reveal an intrinsic correlation between Fe content, structural ordering, and magnetic ground states. With increasing $x$, the system undergoes a successive transition from paramagnetism to a spin-glass state, then to long-range antiferromagnetism (AFM), and ultimately to a reentrant spin-glass phase, with the transition temperatures exhibiting a nonmonotonic dependence on Fe content. The maximum Néel temperature ($T_{\mathrm{N}}$ = $\mathrm{175K}$) and strongest AFM coupling occur at $x=0.25$, where Fe atoms form a well-ordered $2a_0 \times 2a_0 $ superlattice within van der Waals gaps. Beyond $x = 0.25$, the superlattice transforms or disorders, weakening Ruderman-Kittel-Kasuya-Yosida (RKKY) interactions and significantly reducing $T_{\mathrm{N}}$. Electrical transport exhibits distinct anomalies at magnetic transition temperatures, corroborating the magnetic state evolution. Our work extends the compositional boundary of Fe-intercalated $\mathrm{NbSe_2}$, establishes precise stoichiometry-structure-magnetism correlations, and identifies structural ordering as a key tuning parameter for AFM. These findings provide a quantitative framework for engineering altermagnetic or switchable antiferromagnetic states in van der Waals materials.

cond-mat.mtrl-sci↗

arXiv2026

Joint Precoding and Phase-Shift Optimization for Beyond-Diagonal RIS-Aided ISAC System

Beyond diagonal reconfigurable intelligent surfaces (BD-RIS) can realize the interconnection between reflecting elements through the impedance network, thereby providing a new approach for the performance improvement of integrated sensing and communication (ISAC) systems. This paper investigates the optimization problem of BD-RIS-aided multiuser ISAC system, aiming to achieve the flexible design of trade-offs between communication and sensing performance. Specifically, we propose an optimization framework jointly combining the multiuser interference management and sensing beam gain approximation method. By jointly optimizing the precoding vector and RIS phase-shift matrix, improving the multiuser communication sum rate through the proposed interference management method, and enhancing the system sensing performance through the beam gain approximation method. For the resulting non-convex weighted optimization problem, we employ the alternating optimization (AO) algorithm to decouple it into two subproblems of precoding vector and phase-shift matrix optimization, with each step admitting closed-form solutions.Simulation results demonstrate that the proposed BD-RIS-aided ISAC system can achieve significant improvement in the trade-offs between communication and sensing performance than the traditional diagonal RIS, verifying the effectiveness of the proposed optimization framework.

cs.IT↗

arXiv2026

Beamforming Optimization for Extremely Large-Scale RIS-Aided Near-Field Secure Communications

This paper studies an extremely large-scale reconfigurable intelligent surface (XL-RIS)-aided near-field physical layer security (PLS) communication system, aiming to maximize the secrecy rate by jointly optimizing precoding vector at the BS and the reflection coefficient matrix at the XL-RIS. Artifi-cial jamming was introduced to further enhance communication security. To solve the non-convex secrecy rate problem, an alternate optimization-based algorithm is adopted to decompose it into two sub-problems. Specifically, when optimizing the transmit beamformer at the BS, the non-convex prob-lem is transformed into a convex one through the weighted minimum mean-square error and the successive convex approximation-based algorithms. For the optimization problem of the XL-RIS phase-shifting matrix, a low-complexity alternating direction method of multipliers-based algorithm is employed to enhance the flexibility of the design. The proposed algorithm is capable of accommodating discrete phase optimization for the XL-RIS, thus better aligning with practical system requirements. Simulation results demonstrate that when the eavesdropper reside in the same direction as the legitimate user and is located closer to the XL-RIS, the proposed scheme in this paper can still ensure the secure communication.

cs.IT↗

arXiv2026

Urban Congestion Patterns under High Electric Vehicle Penetration: A Case Study of 10 U.S. Cities

With the global energy transition and the rapid penetration of electric vehicles (EVs), the widening travel cost gap between EVs and gasoline vehicles (GVs) increasingly affects commuters' route choices and may reshape urban congestion patterns. Existing research remains in its preliminary exploratory phase. On the one hand, multi-class models do not account for fixed user class scenarios, which may not align with actual commuters; on the other hand, there is a lack of systematic quantitative analysis based on real-world complex road networks across multiple cities. As a result, the congestion effects induced by heterogeneous GV-EV cost structures may be mischaracterized or substantially underestimated. To address these limitations, this paper proposes a multi-user equilibrium (MUE) assignment model for mixed GV-EV traffic, constructs a dual algorithm with convergence guarantees, and designs multi-dimensional evaluation metrics for congestion patterns. Using 10 representative U.S. cities as a case study, this research explores the evolution trends of traffic congestion under different EV penetration scenarios based on real city-level road networks and block-level commuter origin-destination (OD) demand. The results show that full EV penetration reduces average system travel time by 2.27%--10.78% across the 10 cities, with New Orleans achieving the largest reduction (10.78%) and San Francisco the smallest (2.27%), but the effectiveness of alleviating congestion exhibits urban heterogeneity. Moreover, for cities with sufficient network redundancy, benefits are primarily concentrated during the low to medium EV penetration stage (0-0.5), though cities with topological constraints (e.g., San Francisco) show more limited improvements throughout all penetration levels. This paper can provide a foundation for formulating differentiated urban planning and congestion management policies.

eess.SY↗

arXiv2024

Ranking protein-protein models with large language models and graph neural networks

Protein-protein interactions (PPIs) are associated with various diseases, including cancer, infections, and neurodegenerative disorders. Obtaining three-dimensional structural information on these PPIs serves as a foundation to interfere with those or to guide drug design. Various strategies can be followed to model those complexes, all typically resulting in a large number of models. A challenging step in this process is the identification of good models (near-native PPI conformations) from the large pool of generated models. To address this challenge, we previously developed DeepRank-GNN-esm, a graph-based deep learning algorithm for ranking modelled PPI structures harnessing the power of protein language models. Here, we detail the use of our software with examples. DeepRank-GNN-esm is freely available at https://github.com/haddocking/DeepRank-GNN-esm

q-bio.BM↗

arXiv2024

"It's like a rubber duck that talks back": Understanding Generative AI-Assisted Data Analysis Workflows through a Participatory Prompting Study

Generative AI tools can help users with many tasks. One such task is data analysis, which is notoriously challenging for non-expert end-users due to its expertise requirements, and where AI holds much potential, such as finding relevant data sources, proposing analysis strategies, and writing analysis code. To understand how data analysis workflows can be assisted or impaired by generative AI, we conducted a study (n=15) using Bing Chat via participatory prompting. Participatory prompting is a recently developed methodology in which users and researchers reflect together on tasks through co-engagement with generative AI. In this paper we demonstrate the value of the participatory prompting method. We found that generative AI benefits the information foraging and sensemaking loops of data analysis in specific ways, but also introduces its own barriers and challenges, arising from the difficulties of query formulation, specifying context, and verifying results.

cs.HC↗

arXiv2022

Anisotropic gap structure and sign reversal symmetry in monolayer Fe(Se,Te)

The iron-based superconductors are an ideal platform to reveal the enigma of the unconventional superconductivity and potential topological superconductivity. Among them, the monolayer Fe(Se,Te)/SrTiO3(001), which is proposed to be topological nontrivial, shows interface-enhanced high-temperature superconductivity in the two dimensional limit. However, the experimental studies on the superconducting pairing mechanism of monolayer Fe(Se,Te) films are still limited. Here, by measuring quasiparticle interference in monolayer Fe(Se,Te)/SrTiO3(001), we report the observation of the anisotropic structure of the large superconducting gap and the sign change of the superconducting gap on different electron pockets. The results are well consistent with the 'bonding-antibonding' s+- wave pairing symmetry driven by spin fluctuations in conjunction with spin-orbit coupling. Our work is of basic significance not only for a unified superconducting formalism in the iron-based superconductors, but also for understanding of topological superconductivity in high-temperature superconductors.

cond-mat.supr-con↗