arXiv subjects

Rui Xu

Publications and source records attributed to Rui Xu.

At least 109 records · Page 6Linked to original sources

arXiv2025

URWKV: Unified RWKV Model with Multi-state Perspective for Low-light Image Restoration

Existing low-light image enhancement (LLIE) and joint LLIE and deblurring (LLIE-deblur) models have made strides in addressing predefined degradations, yet they are often constrained by dynamically coupled degradations. To address these challenges, we introduce a Unified Receptance Weighted Key Value (URWKV) model with multi-state perspective, enabling flexible and effective degradation restoration for low-light images. Specifically, we customize the core URWKV block to perceive and analyze complex degradations by leveraging multiple intra- and inter-stage states. First, inspired by the pupil mechanism in the human visual system, we propose Luminance-adaptive Normalization (LAN) that adjusts normalization parameters based on rich inter-stage states, allowing for adaptive, scene-aware luminance modulation. Second, we aggregate multiple intra-stage states through exponential moving average approach, effectively capturing subtle variations while mitigating information loss inherent in the single-state mechanism. To reduce the degradation effects commonly associated with conventional skip connections, we propose the State-aware Selective Fusion (SSF) module, which dynamically aligns and integrates multi-state features across encoder stages, selectively fusing contextual information. In comparison to state-of-the-art models, our URWKV model achieves superior performance on various benchmarks, while requiring significantly fewer parameters and computational resources.

cs.CV↗

arXiv2025

Enhanced Stability and Linearly Polarized Emission from CsPbI$_3$ Perovskite Nanoplatelets through A-site Cation Engineering

The anisotropy of perovskite nanoplatelets (PeNPLs) opens up many opportunities in optoelectronics, including enabling the emission of linearly polarized light. But the limited stability of PeNPLs is a pressing challenge, especially for red-emitting CsPbI$_3$. Herein, we address this limitation by alloying FA into the perovskite cuboctahedral site. Unlike Cs/FA alloying in bulk thin films or nonconfined nanocubes, FA incorporation in nanoplatelets requires meticulous control over the reaction conditions, given that nanoplatelets are obtained in kinetically-driven growth regimes instead of thermodynamically-driven conditions. Through in-situ photoluminescence (PL) measurements, we find that excess FA leads to uncontrolled growth, where phase-impurities and nanoplatelets of multiple thicknesses co-exist. Restricting the FA content to up to 25% Cs substitution enables monodisperse PeNPLs, and increases the PL quantum yield (from 53% to 61%), exciton lifetime (from 18 ns to 27 ns), and stability in ambient air (from ~2 days to >7 days) compared to CsPbI$_3$. This arises due to hydrogen bonding between FA and the oleate and oleylammonium ligands, anchoring them to the surface to improve optoelectronic properties and stability. The reduction in non-radiative recombination, improvement in the nanoplatelet aspect ratio, and higher ligand density lead to FA-containing PeNPLs more effectively forming edge-up superlattices, enhancing the PL degree of linear polarization from 5.1% (CsPbI$_3$) to 9.4% (Cs$_{0.75}$FA$_{0.25}$PbI$_3$). These fundamental insights show how the stability limitations of PeNPLs could be addressed, and these materials grown more precisely to improve their performance as polarized light emitters, critical for utilizing them in next-generation display, bioimaging and communications applications.

cond-mat.mtrl-sci↗

arXiv2025

NeurCross: A Neural Approach to Computing Cross Fields for Quad Mesh Generation

Quadrilateral mesh generation plays a crucial role in numerical simulations within Computer-Aided Design and Engineering (CAD/E). Producing high-quality quadrangulation typically requires satisfying four key criteria. First, the quadrilateral mesh should closely align with principal curvature directions. Second, singular points should be strategically placed and effectively minimized. Third, the mesh should accurately conform to sharp feature edges. Lastly, quadrangulation results should exhibit robustness against noise and minor geometric variations. Existing methods generally involve first computing a regular cross field to represent quad element orientations across the surface, followed by extracting a quadrilateral mesh aligned closely with this cross field. A primary challenge with this approach is balancing the smoothness of the cross field with its alignment to pre-computed principal curvature directions, which are sensitive to small surface perturbations and often ill-defined in spherical or planar regions. To tackle this challenge, we propose NeurCross, a novel framework that simultaneously optimizes a cross field and a neural signed distance function (SDF), whose zero-level set serves as a proxy of the input shape. Our joint optimization is guided by three factors: faithful approximation of the optimized SDF surface to the input surface, alignment between the cross field and the principal curvature field derived from the SDF surface, and smoothness of the cross field. Acting as an intermediary, the neural SDF contributes in two essential ways. First, it provides an alternative, optimizable base surface exhibiting more regular principal curvature directions for guiding the cross field. Second, we leverage the Hessian matrix of the neural SDF to implicitly enforce cross field alignment with principal curvature directions...

cs.CV↗

arXiv2025

Tailoring composite skyrmionic spin textures in an above-room-temperature ferromagnet Fe3-xGaTe2

Realizing room-temperature tunable skyrmionic objects in van der Waals ferromagnet offers unparalleled prospects for future spintronics. Here, we report an experimental investigation on the emergence and evolution of skyrmionic spin textures in the non-stoichiometric Fe3-xGaTe2 using magnetic force microscopy. The iron-deficiency-specific magnetic states of stripe, striped skyrmionium and striped skyrmion sack are observed. Through zero-field-cooling and field-cooling measurements, we observed distinct topological transitions and trivial transitions (distinguished by changes in topological charge) emerging during the stepwise evolution of topological spin textures, which enabled us to develop an evolution pathway model. Leveraging this model, the room-temperature stable composite topological spin textures of skyrmionium, skyrmion bag and sack states are further controllably realized via the exclusive topological-transition path (regulated by magnetic field and DMI intensity). Our work provides valuable insights into the room-temperature realization of topological spin textures in Fe3-xGaTe2, and inspires further exploration of their potential applications in heterostructure spintronics.

cond-mat.mtrl-sci↗

arXiv2025

Revolutionizing Symbiotic Radio: Exploiting Tradeoffs in Hybrid Active-Passive Communications

Symbiotic radio (SR), a novel energy- and spectrum-sharing paradigm of backscatter communications (BC), has been deemed a promising solution for ambient Internet of Things (A-IoT), enabling ultra-low power consumption and massive connectivity. However, A-IoT nodes utilizing BC suffer from low transmission rates, which may limit the applications of SR in A-IoT scenarios with data transmission requirements. To address this issue, in this article, we introduce hybrid active-passive communications (HAPC) into SR by exploiting tradeoffs between transmission rate and power consumption. We first present an overview of novel BC paradigms including ambient BC and SR. Then, a novel HAPC-enabled SR is proposed to enhance the transmission rate of A-IoT nodes. Furthermore, within this paradigm, we investigate the resource allocation scheme and present preliminary research results. Simulation results show that the transmission rate of A-IoT nodes in the proposed HAPC-enabled SR surpasses that in traditional SR. Finally, we discuss open issues related to HAPC-enabled SR.

cs.IT↗

arXiv2025

The Fourth Monocular Depth Estimation Challenge

This paper presents the results of the fourth edition of the Monocular Depth Estimation Challenge (MDEC), which focuses on zero-shot generalization to the SYNS-Patches benchmark, a dataset featuring challenging environments in both natural and indoor settings. In this edition, we revised the evaluation protocol to use least-squares alignment with two degrees of freedom to support disparity and affine-invariant predictions. We also revised the baselines and included popular off-the-shelf methods: Depth Anything v2 and Marigold. The challenge received a total of 24 submissions that outperformed the baselines on the test set; 10 of these included a report describing their approach, with most leading methods relying on affine-invariant predictions. The challenge winners improved the 3D F-Score over the previous edition's best result, raising it from 22.58% to 23.05%.

cs.CV↗

arXiv2025

DEFOM-Stereo: Depth Foundation Model Based Stereo Matching

Stereo matching is a key technique for metric depth estimation in computer vision and robotics. Real-world challenges like occlusion and non-texture hinder accurate disparity estimation from binocular matching cues. Recently, monocular relative depth estimation has shown remarkable generalization using vision foundation models. Thus, to facilitate robust stereo matching with monocular depth cues, we incorporate a robust monocular relative depth model into the recurrent stereo-matching framework, building a new framework for depth foundation model-based stereo-matching, DEFOM-Stereo. In the feature extraction stage, we construct the combined context and matching feature encoder by integrating features from conventional CNNs and DEFOM. In the update stage, we use the depth predicted by DEFOM to initialize the recurrent disparity and introduce a scale update module to refine the disparity at the correct scale. DEFOM-Stereo is verified to have much stronger zero-shot generalization compared with SOTA methods. Moreover, DEFOM-Stereo achieves top performance on the KITTI 2012, KITTI 2015, Middlebury, and ETH3D benchmarks, ranking $1^{st}$ on many metrics. In the joint evaluation under the robust vision challenge, our model simultaneously outperforms previous models on the individual benchmarks, further demonstrating its outstanding capabilities.

cs.CV↗

arXiv2025

Protecting Copyright of Medical Pre-trained Language Models: Training-Free Backdoor Model Watermarking

With the advancement of intelligent healthcare, medical pre-trained language models (Med-PLMs) have emerged and demonstrated significant effectiveness in downstream medical tasks. While these models are valuable assets, they are vulnerable to misuse and theft, requiring copyright protection. However, existing watermarking methods for pre-trained language models (PLMs) cannot be directly applied to Med-PLMs due to domain-task mismatch and inefficient watermark embedding. To fill this gap, we propose the first training-free backdoor model watermarking for Med-PLMs. Our method employs low-frequency words as triggers, embedding the watermark by replacing their embeddings in the model's word embedding layer with those of specific medical terms. The watermarked Med-PLMs produce the same output for triggers as for the corresponding specified medical terms. We leverage this unique mapping to design tailored watermark extraction schemes for different downstream tasks, thereby addressing the challenge of domain-task mismatch in previous methods. Experiments demonstrate superior effectiveness of our watermarking method across medical downstream tasks. Moreover, the method exhibits robustness against model extraction, pruning, fusion-based backdoor removal attacks, while maintaining high efficiency with 10-second watermark embedding.

cs.LG↗

arXiv2025

Scalarized neutron stars in massive scalar-tensor gravity: X-ray pulsars and tidal deformability

Neutron stars (NSs) in scalar-tensor theories of gravitation with the phenomenon of spontaneous scalarization can develop significant deviations from general relativity. Cases with a massless scalar were studied widely. Here we compare the NS scalarizations in the Damour--Esposito-Far{è}se theory, the Mendes-Ortiz theory, and the $ξ$-theory with a massive scalar field. Numerical solutions for slowly rotating NSs are obtained. They are used to construct the X-ray pulse profiles of a pair of extended hot spots on the surface of NSs. We also calculate the tidal deformability for NSs with spontaneous scalarization which is done for the first time with a massive scalar field. We show the universal relation between the moment of inertia and the tidal deformability. The X-ray pulse profiles, the tidal deformability, and the universal relation may help to constrain the massive scalar-tensor theories in X-ray and gravitational-wave observations of NSs, including the Neutron star Interior Composition Explorer (NICER) satellite, Square Kilometre Array (SKA) telescope, and LIGO/Virgo/KAGRA laser interferometers.

gr-qc↗

arXiv2025

Is a Pure Transformer Effective for Separated and Online Multi-Object Tracking?

Recent advances in Multi-Object Tracking (MOT) have demonstrated significant success in short-term association within the separated tracking-by-detection online paradigm. However, long-term tracking remains challenging. While graph-based approaches address this by modeling trajectories as global graphs, these methods are unsuitable for real-time applications due to their non-online nature. In this paper, we review the concept of trajectory graphs and propose a novel perspective by representing them as directed acyclic graphs. This representation can be described using frame-ordered object sequences and binary adjacency matrices. We observe that this structure naturally aligns with Transformer attention mechanisms, enabling us to model the association problem using a classic Transformer architecture. Based on this insight, we introduce a concise Pure Transformer (PuTR) to validate the effectiveness of Transformer in unifying short- and long-term tracking for separated online MOT. Extensive experiments on four diverse datasets (SportsMOT, DanceTrack, MOT17, and MOT20) demonstrate that PuTR effectively establishes a solid baseline compared to existing foundational online methods while exhibiting superior domain adaptation capabilities. Furthermore, the separated nature enables efficient training and inference, making it suitable for practical applications. Implementation code and trained models are available at https://github.com/chongweiliu/PuTR .

cs.CV↗

arXiv2025

Phase Transitions in the Early Universe: Impacts on BBN and CMB Observables

We explore the effects of low-scale cosmological first-order phase transitions on Big Bang Nucleosynthesis (BBN) and Cosmic Microwave Background (CMB) anisotropy and distortion. We examine two scenarios: the distribution of the phase-transition energy in the cosmic background and the phase transition as an additional source of energy injection. Our analysis reveals that the CMB spectrum from the Planck 2018 dataset imposes stringent constraints on sub-MeV-scale phase transitions, while keV-scale phase transitions can induce significant CMB distortions. Consequently, BBN and CMB observables serve as complementary tools to constrain phase-transition parameters, alongside gravitational wave detections.

hep-ph↗

arXiv2025

Integrating mobile and fixed monitoring data for high-resolution PM2.5 mapping using machine learning

Constructing high resolution air pollution maps at lower cost is crucial for sustainable city management and public health risk assessment. However, traditional fixed-site monitoring lacks spatial coverage, while mobile low-cost sensors exhibit significant data instability. This study integrates PM2.5 data from 320 taxi-mounted mobile low-cost sensors and 52 fixed monitoring stations to address these limitations. By employing the machine learning methods, an appropriate mapping relationship was established between fixed and mobile monitoring concentration. The resulting pollution maps achieved 500-meter spatial and 5-minute temporal resolutions, showing close alignment with fixed monitoring data (+4.35% bias) but significant deviation from raw mobile data (-31.77%). The fused map exhibits the fine-scale spatial variability also observed in the mobile pollution map, while showing the stable temporal variability closer to that of the fixed pollution map (fixed: 1.12 plus or minus 0.73%, mobile: 3.15 plus or minus 2.44%, mapped: 1.01 plus or minus 0.65%). These findings demonstrate the potential of large-scale mobile low-cost sensor networks for high-resolution air quality mapping, supporting targeted urban environmental governance and health risk mitigation.

cs.LG↗

arXiv2025

Guess What I am Thinking: A Benchmark for Inner Thought Reasoning of Role-Playing Language Agents

Recent advances in LLM-based role-playing language agents (RPLAs) have attracted broad attention in various applications. While chain-of-thought reasoning has shown importance in many tasks for LLMs, the internal thinking processes of RPLAs remain unexplored. Understanding characters' inner thoughts is crucial for developing advanced RPLAs. In this paper, we introduce ROLETHINK, a novel benchmark constructed from literature for evaluating character thought generation. We propose the task of inner thought reasoning, which includes two sets: the gold set that compares generated thoughts with original character monologues, and the silver set that uses expert synthesized character analyses as references. To address this challenge, we propose MIRROR, a chain-of-thought approach that generates character thoughts by retrieving memories, predicting character reactions, and synthesizing motivations. Through extensive experiments, we demonstrate the importance of inner thought reasoning for RPLAs, and MIRROR consistently outperforms existing methods. Resources are available at https://github.com/airaer1998/RPA_Thought.

cs.AI↗

arXiv2025

Wasserstein-regularized Conformal Prediction under General Distribution Shift

Conformal prediction yields a prediction set with guaranteed $1-α$ coverage of the true target under the i.i.d. assumption, which may not hold and lead to a gap between $1-α$ and the actual coverage. Prior studies bound the gap using total variation distance, which cannot identify the gap changes under distribution shift at a given $α$. Besides, existing methods are mostly limited to covariate shift,while general joint distribution shifts are more common in practice but less researched.In response, we first propose a Wasserstein distance-based upper bound of the coverage gap and analyze the bound using probability measure pushforwards between the shifted joint data and conformal score distributions, enabling a separation of the effect of covariate and concept shifts over the coverage gap. We exploit the separation to design an algorithm based on importance weighting and regularized representation learning (WR-CP) to reduce the Wasserstein bound with a finite-sample error bound.WR-CP achieves a controllable balance between conformal prediction accuracy and efficiency. Experiments on six datasets prove that WR-CP can reduce coverage gaps to $3.2\%$ across different confidence levels and outputs prediction sets 37$\%$ smaller than the worst-case approach on average.

cs.LG↗

arXiv2025

Filling-dependent intertwined electronic and atomic orders in the flat-band state of 1T-TaS2

The delicate interplay among the complex intra-/inter-layer electron-electron and electron-lattice interactions is the fundamental prerequisite of these exotic quantum states, such as superconductivity, nematic order, and checkerboard charge order. Here we explore the filling-dependent multiple stable intertwined electronic and atomic orders of flat-band state of 1T-TaS2 encompassing hole order, phase orders, coexisting left- and right-chiral orders and mixed phase/chiral orders via scanning tunneling microscopy (STM). Combining first-principles calculations, the emergent electronic/atomic orders can be attributed to the weakening of electron-electron correlations and stacking-dependent interlayer interactions. Moreover, achiral intermediate ring-like clusters and nematic charge density wave (CDW) states are successfully realized in intralayer chiral domain wall and interlayer heterochiral stacking regions through chiral overlap configurations. Our study not only deepens the understanding of filling-dependent electronic/atomic orders in flat-band systems, but also offers perspectives for exploring exotic quantum states in correlated electronic systems.

cond-mat.str-el↗

arXiv2025

FactCG: Enhancing Fact Checkers with Graph-Based Multi-Hop Data

Prior research on training grounded factuality classification models to detect hallucinations in large language models (LLMs) has relied on public natural language inference (NLI) data and synthetic data. However, conventional NLI datasets are not well-suited for document-level reasoning, which is critical for detecting LLM hallucinations. Recent approaches to document-level synthetic data generation involve iteratively removing sentences from documents and annotating factuality using LLM-based prompts. While effective, this method is computationally expensive for long documents and limited by the LLM's capabilities. In this work, we analyze the differences between existing synthetic training data used in state-of-the-art models and real LLM output claims. Based on our findings, we propose a novel approach for synthetic data generation, CG2C, that leverages multi-hop reasoning on context graphs extracted from documents. Our fact checker model, FactCG, demonstrates improved performance with more connected reasoning, using the same backbone models. Experiments show it even outperforms GPT-4-o on the LLM-Aggrefact benchmark with much smaller model size.

cs.CL↗

arXiv2025

The I-Love universal relation for polytropic stars under Newtonian gravity

The moment of inertia and tidal deformability of idealized stars with polytropic equations of state (EOSs) are numerically calculated under both Newtonian gravity and general relativity (GR). The results explicitly confirm that the relation between the moment of inertia and tidal deformability, parameterized by the star's mass, exhibits variations of 1% to 10% for different polytropic indices in Newtonian gravity and GR, respectively. This indicates a more robust I-Love universal relation in the Newtonian framework. The theoretically derived I-Love universal relation for polytropic stars is subsequently tested against observational data for the moment of inertia and tidal deformability of the 8 planets and some moons in our solar system. The analysis reveals that the theoretical I-Love universal relation aligns well with the observational data, suggesting that it can serve as an empirical relation. Consequently, it enables the estimation of either the moment of inertia or the tidal deformability of an exoplanet if one of these quantities, along with the mass of the exoplanet, is known.

gr-qc↗

arXiv2024

Gravity potential determination based on China Space Station Dual-frequency microwave links frequency transfer

The China Space Station (CSS) is currently in orbit and carries the high-precision optical atomic clock with stability of approximately $2.0 \times 10^{-15} / \sqrtτ$ in its experiment module. We have developed a model to determine the gravity potential (GP) based on the gravity frequency shift equation and have created both one-way and dual-frequency transfer models up to $c^{-4}$. These models consider effects from the troposphere, ionosphere, and solid Earth tides. The proposed model is suitable for measurements at the magnitude of $10^{-19}$. Based on the CSS mission, we conducted the simulation experiments. The results indicate that when processing the simulation frequency signal using the proposed model, we can obtain the GP with the accuracies of $ (1.13\pm0.71)\,\mathrm{m^2/s^2}$, $ (0.09\pm0.89)\,\mathrm{m^2/s^2}$, and $(0.66\pm1.18)\,\mathrm{m^2/s^2}$ for cutoff elevation angles of $5^{\circ}$, $10^{\circ}$ and $15^{\circ}$, respectively. With the high-precision optical atomic clock onboard the CSS, the proposed model enables us to measure the GP differences in the magnitude of centimeter-level accuracy.

physics.geo-ph↗