arXiv subjects

Rui Zhang

Publications and source records attributed to Rui Zhang.

At least 379 records · Page 21Linked to original sources

arXiv2025

Extremely Large-Scale Movable Antenna-Enabled Multiuser Communications: Modeling and Optimization

Movable antenna (MA) has been recognized as a promising technology to improve communication performance in future wireless networks such as 6G. To unleash its potential, this paper proposes a novel architecture, namely extremely large-scale MA (XL-MA), which allows flexible antenna/subarray positioning over an extremely large spatial region for effectively enhancing near-field effects and spatial multiplexing performance. In particular, this paper studies an uplink XL-MA-enabled multiuser system, where single-antenna users distributed in a coverage area are served by a base station (BS) equipped with multiple movable subarrays. We begin by presenting a spatially non-stationary channel model to capture the near-field effects, including positiondependent large-scale channel gains and line-of-sight visibility. To evaluate system performance, we further derive a closedform approximation of the expected weighted sum rate under maximum ratio combining (MRC), revealing that optimizing XLMA placement enhances user channel power gain to increase desired signal power and reduces channel correlation to decreases multiuser interference. Building upon this, we formulate an antenna placement optimization problem to maximize the expected weighted sum rate, leveraging statistical channel conditions and user distribution. To efficiently solve this challenging non-linear binary optimization problem, we propose a polynomial-time successive replacement algorithm. Simulation results demonstrate that the proposed XL-MA placement strategy achieves nearoptimal performance, significantly outperforming benchmark schemes based on conventional fixed-position antennas.

cs.IT↗

arXiv2025

Large Processor Chip Model

Computer System Architecture serves as a crucial bridge between software applications and the underlying hardware, encompassing components like compilers, CPUs, coprocessors, and RTL designs. Its development, from early mainframes to modern domain-specific architectures, has been driven by rising computational demands and advancements in semiconductor technology. However, traditional paradigms in computer system architecture design are confronting significant challenges, including a reliance on manual expertise, fragmented optimization across software and hardware layers, and high costs associated with exploring expansive design spaces. While automated methods leveraging optimization algorithms and machine learning have improved efficiency, they remain constrained by a single-stage focus, limited data availability, and a lack of comprehensive human domain knowledge. The emergence of large language models offers transformative opportunities for the design of computer system architecture. By leveraging the capabilities of LLMs in areas such as code generation, data analysis, and performance modeling, the traditional manual design process can be transitioned to a machine-based automated design approach. To harness this potential, we present the Large Processor Chip Model (LPCM), an LLM-driven framework aimed at achieving end-to-end automated computer architecture design. The LPCM is structured into three levels: Human-Centric; Agent-Orchestrated; and Model-Governed. This paper utilizes 3D Gaussian Splatting as a representative workload and employs the concept of software-hardware collaborative design to examine the implementation of the LPCM at Level 1, demonstrating the effectiveness of the proposed approach. Furthermore, this paper provides an in-depth discussion on the pathway to implementing Level 2 and Level 3 of the LPCM, along with an analysis of the existing challenges.

cs.AR↗

arXiv2025

Polarforming Antenna Enhanced Sensing and Communication: Modeling and Optimization

In this paper, we propose a novel polarforming antenna (PA) to achieve cost-effective wireless sensing and communication. Specifically, the PA can enable polarforming to adaptively control the antenna's polarization electrically as well as tune its position/rotation mechanically, so as to effectively exploit polarization and spatial diversity to reconfigure wireless channels for improving sensing and communication performance. We study an PA-enhanced integrated sensing and communication (ISAC) system that utilizes user location sensing to facilitate communication between an PA-equipped base station (BS) and PA-equipped users. First, we model the PA channel in terms of transceiver antenna polarforming vectors and antenna positions/rotations. We then propose a two-timescale ISAC protocol, where in the slow timescale, user localization is first performed, followed by the optimization of the BS antennas' positions and rotations based on the sensed user locations; subsequently, in the fast timescale, transceiver polarforming is adapted to cater to the instantaneous channel state information (CSI), with the optimized BS antennas' positions and rotations. We propose a new polarforming-based user localization method that uses a structured time-domain pattern of pilot-polarforming vectors to extract the common stable components in the PA channel across different polarizations based on the parallel factor (PARAFAC) tensor model. Moreover, we maximize the achievable average sum-rate of users by jointly optimizing the fast-timescale transceiver polarforming, including phase shifts and amplitude variations, along with the slow-timescale antenna rotations and positions at the BS. Simulation results validate the effectiveness of polarforming-based localization algorithm and demonstrate the performance advantages of polarforming, antenna placement, and their joint design.

cs.IT↗

arXiv2025

Polarforming for Wireless Networks: Opportunities and Challenges

Polarforming emerges as a promising technique for manipulating the polarization of electromagnetic (EM) waves by shaping the polarization of an antenna into a desired state. By dynamically adjusting antenna polarization, polarforming enables real-time polarization matching or mismatching with received EM waves, thereby leveraging polarization degrees of freedom (DoFs) to enhance wireless communication performance. In this article, we first present an overview of the fundamental principles and design approaches underlying the polarforming technique. We then analyze the key advantages of polarforming, including hardware cost reduction, depolarization mitigation, channel adaptation, signal power enhancement, and interference suppression. Furthermore, we explore promising applications of polarforming for next-generation wireless networks. Numerical case studies demonstrate the substantial performance gains of polarforming over conventional fixed-polarization antenna (FPA) systems, along with a discussion of implementation challenges to motivate future research.

cs.IT↗

arXiv2025

One Prompt to Verify Your Models: Black-Box Text-to-Image Models Verification via Non-Transferable Adversarial Attacks

Recently, various types of Text-to-Image (T2I) models have emerged (such as DALL-E and Stable Diffusion), and showing their advantages in different aspects. Therefore, some third-party service platforms collect different model interfaces and provide cheaper API services and more flexibility in T2I model selections. However, this also raises a new security concern: Are these third-party services truly offering the models they claim? To answer this question, we first define the concept of T2I model verification, which aims to determine whether a black-box target model is identical to a given white-box reference T2I model. After that, we propose VerifyPrompt, which performs T2I model verification through a special designed verify prompt. Intuitionally, the verify prompt is an adversarial prompt for the target model without transferability for other models. It makes the target model generate a specific image while making other models produce entirely different images. Specifically, VerifyPrompt utilizes the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to optimize the cosine similarity of a prompt's text encoding, generating verify prompts. Finally, by computing the CLIP-text similarity scores between the prompts the generated images, VerifyPrompt can determine whether the target model aligns with the reference model. Experimental results demonstrate that VerifyPrompt consistently achieves over 90\% accuracy across various T2I models, confirming its effectiveness in practical model platforms (such as Hugging Face).

cs.CV↗

arXiv2025

Enhancing the sensitivity of quantum optomechanical gyroscope by optical Kerr effect

We propose a theoretical scheme to enhance the sensitivity of a quantum optomechanical gyroscope (QOMG) by optical Kerr effect. We utilize quantum Fisher information (QFI) to evaluate the metrological potential of the QOMG scheme. It is found that the Kerr interaction can significantly enhances the sensitivity of the QOMG. We observe the super-Hesenberg scaling of parameter estimation precision. Furthermore, we also evaluate the performance of QOMG for the quadrature measurement. It is indicated that the sensitivity in the quadrature measurement scheme can saturate the quantum Crmamér-Rao bound. We study the effect of the driving and dissipation of the optical cavity on the QFI, and find that the sensitivity can be manipulated by changing the driving while dissipation decreases the sensitivity. The work shows that the photon nonlinear interaction can improve sensitivity of QOMG, and demonstrates a valuable quantum resource for the QOMG. These results could have a wide-ranging impact on developing high-performance QOMG in the future.

quant-ph↗

arXiv2025

Polarforming Design with Phase Shifter Based Polarization Reconfigurable Antennas

In this paper, we propose a new form of polarization reconfigurable antennas (PRAs) that can form linear, circular, and general elliptical polarizations assisted by phase shifters (PSs). With PRAs, polarforming is achieved, which enables the antenna to shape its polarization into a desired state for aligning with that of the received electromagnetic (EM) wave or reconfiguring that of the transmit EM wave. To demonstrate the benefits of polarforming, we investigate a PRA-aided single-input single-output (SISO) communication system equipped with tunable PSs for polarization adaptation. We characterize the achievable signal-to-noise ratio (SNR) at the receiver as a function of the phase shifts of PS-based PRAs. Moreover, we develop an alternating optimization approach to maximize the SNR by optimizing the phase shifts at both the transmitter and receiver. Finally, comprehensive simulation results are presented, which not only validate the effectiveness of polarforming in mitigating the channel depolarization effects, but also demonstrate its substantial performance improvement over conventional systems.

eess.SP↗

arXiv2025

Unsupervised neural-implicit laser absorption tomography for quantitative imaging of unsteady flames

This paper presents a novel neural-implicit approach to laser absorption tomography (LAT) with an experimental demonstration. A coordinate neural network is used to represent thermochemical state variables as continuous functions of space and time. Unlike most existing neural methods for LAT, which rely on prior simulations and supervised training, our approach is based solely on LAT measurements, utilizing a differentiable observation operator with line parameters provided in a standard spectroscopy database format. Although reconstructing scalar fields from multi-beam absorbance data is an inherently ill-posed, nonlinear inverse problem, our continuous space-time parameterization supports physics-inspired regularization strategies and enables data assimilation. Synthetic and experimental tests are conducted to validate the method, demonstrating robust performance and reproducibility. We show that our neural-implicit approach to LAT can capture the dominant spatial modes of an unsteady flame from very sparse measurement data, indicating its potential to reveal combustion instabilities in measurement domains with minimal optical access.

physics.optics↗

arXiv2025

Identifying Compton-thick AGNs with Machine learning algorithm in Chandra Deep Field-South

Compton-thick active galactic nuclei (CT-AGNs), which are defined by column density $\mathrm{N_H} \geqslant 1.5 \times 10^{24} \ \mathrm{cm}^{-2}$, emit feeble X-ray radiation, even undetectable by X-ray instruments. Despite this, the X-ray emissions from CT-AGNs are believed to be a substantial contributor to the cosmic X-ray background (CXB). According to synthesis models of AGNs, CT-AGNs are expected to make up a significant fraction of the AGN population, likely around 30% or more. However, only $\sim$11% of AGNs have been identified as CT-AGNs in the Chandra Deep Field-South (CDFS). To identify hitherto unknown CT-AGNs in the field, we used a Random Forest algorithm for identifying them. First, we build a secure classified subset of 210 AGNs to train and evaluate our algorithm. Our algorithm achieved an accuracy rate of 90% on the test set after training. Then, we applied our algorithm to an additional subset of 254 AGNs, successfully identifying 67 CT-AGNs within this group. This result significantly increased the fraction of CT-AGNs in the CDFS, which is closer to the theoretical predictions of the CXB. Finally, we compared the properties of host galaxies between CT-AGNs and non-CT-AGNs and found that the host galaxies of CT-AGNs exhibit higher levels of star formation activity.

astro-ph.GA↗

arXiv2025

EvdCLIP: Improving Vision-Language Retrieval with Entity Visual Descriptions from Large Language Models

Vision-language retrieval (VLR) has attracted significant attention in both academia and industry, which involves using text (or images) as queries to retrieve corresponding images (or text). However, existing methods often neglect the rich visual semantics knowledge of entities, thus leading to incorrect retrieval results. To address this problem, we propose the Entity Visual Description enhanced CLIP (EvdCLIP), designed to leverage the visual knowledge of entities to enrich queries. Specifically, since humans recognize entities through visual cues, we employ a large language model (LLM) to generate Entity Visual Descriptions (EVDs) as alignment cues to complement textual data. These EVDs are then integrated into raw queries to create visually-rich, EVD-enhanced queries. Furthermore, recognizing that EVD-enhanced queries may introduce noise or low-quality expansions, we develop a novel, trainable EVD-aware Rewriter (EaRW) for vision-language retrieval tasks. EaRW utilizes EVD knowledge and the generative capabilities of the language model to effectively rewrite queries. With our specialized training strategy, EaRW can generate high-quality and low-noise EVD-enhanced queries. Extensive quantitative and qualitative experiments on image-text retrieval benchmarks validate the superiority of EvdCLIP on vision-language retrieval tasks.

cs.CV↗

arXiv2025

On the global Gaussian bending measure and its applications in stationary spacetimes

Modified gravity theories have been suggested to address the limitations of general relativity, each exhibiting differences, particularly in their strong-field limits. Nonetheless, there lacks effective means to distinguish or test these theories through local strong-field measurements. In this work, we define a global Gaussian bending measure over singular spacetime regions, establish a corresponding global theory, and demonstrate its applications in a general stationary spacetime. The global theory is based on differential geometry, rather than on specific gravity theories, allowing it to depict various physics within general relativity and beyond. For example, it can be applied to describe the gravitational bending of massless or massive messengers, such as photons, neutrinos, cosmic rays, and possibly massive gravitational waves predicted in certain theories of gravity. Besides, the global theory is applicable to any stationary spacetime regions outside a rotating black hole. As an instance of its direct applications, we investigate the highly-curved spacetime effects of the black hole in its immediate surrounding regions and design local strong-field experiments involving different shapes of singular lensing patches. New means can be therefore anticipated to be developed according to the global theory to differentiate between different gravity theories and test them in their strong-field regions.

gr-qc↗

arXiv2025

Directional Sparsity Based Statistical Channel Estimation for 6D Movable Antenna Communications

Six-dimensional movable antenna (6DMA) is an innovative and transformative technology to improve wireless network capacity by adjusting the 3D positions and 3D rotations of antennas/surfaces (sub-arrays) based on the channel spatial distribution. For optimization of the antenna positions and rotations, the acquisition of statistical channel state information (CSI) is essential for 6DMA systems. In this paper, we unveil for the first time a new \textbf{\textit{directional sparsity}} property of the 6DMA channels between the base station (BS) and the distributed users, where each user has significant channel gains only with a (small) subset of 6DMA position-rotation pairs, which can receive direct/reflected signals from the user. By exploiting this property, a covariance-based algorithm is proposed for estimating the statistical CSI in terms of the average channel power at a small number of 6DMA positions and rotations. Based on such limited channel power estimation, the average channel powers for all possible 6DMA positions and rotations in the BS movement region are reconstructed by further estimating the multi-path average power and direction-of-arrival (DOA) vectors of all users. Simulation results show that the proposed directional sparsity-based algorithm can achieve higher channel power estimation accuracy than existing benchmark schemes, while requiring a lower pilot overhead.

cs.IT↗

arXiv2025

Low-Complexity Near-Field Beam Training with DFT Codebook based on Beam Pattern Analysis

Extremely large antenna arrays (ELAAs) operating in high-frequency bands have spurred the development of near-field communication, driving advancements in beam training design. This paper introduces an efficient near-field beam training method that utilizes the discrete Fourier transform (DFT) codebook traditionally employed for far-field users (FUs). We begin by analyzing the received beam pattern and deriving closed-form expressions for its width and central beam gain as a function of user location. Building on these derived expressions, we propose a beam training method that estimates user distance from the received signal powers using DFT beams, with a computational complexity of O(1). Simulation results confirm the efficacy of our approach, demonstrating its ability to perform low-complexity near-field beam training with high estimation accuracy. Notably, our proposed scheme achieves up to a 1.96 dB SNR gain over exhaustive search methods in multi-user beamforming scenarios.

eess.SP↗

arXiv2025

Simulations of Three-dimensional Nematic Guidance of Microswimmers

It has been shown that an anisotropic liquid crystalline (LC) environment can be used to guide the self-propulsion dynamics of dispersed microswimmers, such as bacteria. This type of composite system is named "living nematic" (LN). In the dilute limit, bacteria are found to mainly follow the local director field. Beyond the dilute limit, however, they exhibit novel dynamical behaviors, from swirling around a spiral +1 defect pattern to forming undulating waves, and to active turbulence. Our current knowledge of how these different behaviors emerge at different population densities remains limited. Here we develop a hybrid method to simulate the dynamics of microswimmers dispersed in a nematic LC. Specifically, we model the microswimmers using active Brownian dynamics method, which is coupled to a hydrodynamic model of nematic LCs to describe the evolution of the flow field and the LC structure. Our method is validated by comparing to existing quasi-two-dimensional (2D) experiments, including undulated swirling around a spiral pattern and stabilized undulated jets on a periodic C-pattern. We further extend our method to three-dimensional (3D) systems by examining loop-defect dynamics. We find that the morphodynamics and destiny of a loop defect not only depend on the activity (self-propulsion velocity), effective size, and the initial distribution of the swimmers, but also rely on its winding profile. Specifically, +1/2 wedge and radial twist winding can dictate the dynamics of loop defects. By varying the characteristic reversal time, we predict that microswimmers not necessarily accumulate in splay regions. Taken together, our hybrid method provides a faithful tool to explain and guide the experiments of LNs in both 2D and 3D, sheds light on the interplay between microswimmer distribution and defect dynamics, and unravels the design principles of using LCs to control active matter.

cond-mat.soft↗

arXiv2025

Enhancing Knowledge Graph Completion with GNN Distillation and Probabilistic Interaction Modeling

Knowledge graphs (KGs) serve as fundamental structures for organizing interconnected data across diverse domains. However, most KGs remain incomplete, limiting their effectiveness in downstream applications. Knowledge graph completion (KGC) aims to address this issue by inferring missing links, but existing methods face critical challenges: deep graph neural networks (GNNs) suffer from over-smoothing, while embedding-based models fail to capture abstract relational features. This study aims to overcome these limitations by proposing a unified framework that integrates GNN distillation and abstract probabilistic interaction modeling (APIM). GNN distillation approach introduces an iterative message-feature filtering process to mitigate over-smoothing, preserving the discriminative power of node representations. APIM module complements this by learning structured, abstract interaction patterns through probabilistic signatures and transition matrices, allowing for a richer, more flexible representation of entity and relation interactions. We apply these methods to GNN-based models and the APIM to embedding-based KGC models, conducting extensive evaluations on the widely used WN18RR and FB15K-237 datasets. Our results demonstrate significant performance gains over baseline models, showcasing the effectiveness of the proposed techniques. The findings highlight the importance of both controlling information propagation and leveraging structured probabilistic modeling, offering new avenues for advancing knowledge graph completion. And our codes are available at https://anonymous.4open.science/r/APIM_and_GNN-Distillation-461C.

cs.AI↗

arXiv2025

FIGhost: Fluorescent Ink-based Stealthy and Flexible Backdoor Attacks on Physical Traffic Sign Recognition

Traffic sign recognition (TSR) systems are crucial for autonomous driving but are vulnerable to backdoor attacks. Existing physical backdoor attacks either lack stealth, provide inflexible attack control, or ignore emerging Vision-Large-Language-Models (VLMs). In this paper, we introduce FIGhost, the first physical-world backdoor attack leveraging fluorescent ink as triggers. Fluorescent triggers are invisible under normal conditions and activated stealthily by ultraviolet light, providing superior stealthiness, flexibility, and untraceability. Inspired by real-world graffiti, we derive realistic trigger shapes and enhance their robustness via an interpolation-based fluorescence simulation algorithm. Furthermore, we develop an automated backdoor sample generation method to support three attack objectives. Extensive evaluations in the physical world demonstrate FIGhost's effectiveness against state-of-the-art detectors and VLMs, maintaining robustness under environmental variations and effectively evading existing defenses.

cs.CV↗

arXiv2025

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks

Recent research highlights concerns about the trustworthiness of third-party Pre-Trained Language Models (PTLMs) due to potential backdoor attacks. These backdoored PTLMs, however, are effective only for specific pre-defined downstream tasks. In reality, these PTLMs can be adapted to many other unrelated downstream tasks. Such adaptation may lead to unforeseen consequences in downstream model outputs, consequently raising user suspicion and compromising attack stealthiness. We refer to this phenomenon as backdoor complications. In this paper, we undertake the first comprehensive quantification of backdoor complications. Through extensive experiments using 4 prominent PTLMs and 16 text classification benchmark datasets, we demonstrate the widespread presence of backdoor complications in downstream models fine-tuned from backdoored PTLMs. The output distribution of triggered samples significantly deviates from that of clean samples. Consequently, we propose a backdoor complication reduction method leveraging multi-task learning to mitigate complications without prior knowledge of downstream tasks. The experimental results demonstrate that our proposed method can effectively reduce complications while maintaining the efficacy and consistency of backdoor attacks. Our code is available at https://github.com/zhangrui4041/Backdoor_Complications.

cs.CR↗

arXiv2025

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models

Graph neural networks (GNNs) have exhibited superior performance in various classification tasks on graph-structured data. However, they encounter the potential vulnerability from the link stealing attacks, which can infer the presence of a link between two nodes via measuring the similarity of its incident nodes' prediction vectors produced by a GNN model. Such attacks pose severe security and privacy threats to the training graph used in GNN models. In this work, we propose a novel solution, called Graph Link Disguise (GRID), to defend against link stealing attacks with the formal guarantee of GNN model utility for retaining prediction accuracy. The key idea of GRID is to add carefully crafted noises to the nodes' prediction vectors for disguising adjacent nodes as n-hop indirect neighboring nodes. We take into account the graph topology and select only a subset of nodes (called core nodes) covering all links for adding noises, which can avert the noises offset and have the further advantages of reducing both the distortion loss and the computation cost. Our crafted noises can ensure 1) the noisy prediction vectors of any two adjacent nodes have their similarity level like that of two non-adjacent nodes and 2) the model prediction is unchanged to ensure zero utility loss. Extensive experiments on five datasets are conducted to show the effectiveness of our proposed GRID solution against different representative link-stealing attacks under transductive settings and inductive settings respectively, as well as two influence-based attacks. Meanwhile, it achieves a much better privacy-utility trade-off than existing methods when extended to GNNs.

cs.LG↗