arXiv subjects

Rui Xi

Publications and source records attributed to Rui Xi.

16 recordsLinked to original sources

arXiv2026

WGAN based Inverse Design of Active Dual Band FSS with Switchable Transmission

This letter presents a novel design method for switchable dual band transmissive frequency selective surface (FSS). The proposed FSS possesses characteristics of maintaining passband characteristics at high frequencies, while switching from transmission to reflection at low frequencies with pin diodes states altering. Specifically, we propose a crystal growth-based topology generation strategy, and utilize a simplified U-Net Wasserstein GAN (WGAN) neural network model to establish an inverse mapping model from electromagnetic response to structure topology parameters. The trained WGAN achieves training and validation accuracies of 95.59% and 90.84%, while the simplified U-Net attains training and validation accuracies of 98.5% and 94.1%. Using the trained WGAN. The generated structural topologies were validated through full-wave simulations and experimental measurements. The proposed method enhances the design flexibility and overcomes the time- consuming drawbacks of conventional FSS design.

physics.app-ph↗

arXiv2026

Analysis and Design of Double-Transmitting Coil Systems based on Parity-Time Symmetry

The wireless power transfer (WPT) system based on parity-time (PT) symmetry has the advantages of robustness, stability, and efficient power transmission. However, traditional PT symmetry structures have limited voltage power output and are susceptible to horizontal misalignment effects. Multiple transmission coils have been proven to improve the power and misalignment tolerance of WPT systems, but variations in inter-coil coupling significantly affect transmission power. Towards this end, this article proposes a double-transmitting coil WPT system based on PT symmetry, which is innovative in that a novel negative resistance structure based on operational amplifier (OA) is proposed, and the parallel structure of double-transmitting coil is applied to this negative resistance structure. Compared with the traditional WPT system based on a single-transmitting coil PT, this system improves the power and misalignment tolerance in the PT symmetry region. The large-sized and small-sized receiving coils increase the load power to 313% and 185% respectively. Moreover, in the symmetry region, the displacement of the receiving coil in the horizontal and vertical directions results in variations of the equivalent coupling coefficient, which can achieve stable power transmission in three dimensions. The voltage fluctuation rate of both receiving coils does not exceed 3.4%, greatly improving the degree of freedom of the receiving system.

physics.app-ph↗

arXiv2026

Web Agents Should Use Typed Actions Instead of Click-Based Browsing

This position paper argues that building a reliable agentic Web requires shifting from low-level interaction primitives to typed actions supported by a semantic layer. Today's web agents primarily operate through clicks, keystrokes, and DOM manipulation, which leads to brittle long-horizon behavior, high execution cost, and limited auditability. We propose web verbs as a concrete design for this layer. A verb exposes a web operation as a typed function with structured inputs, structured outputs, and documented behavior, whether it is backed by a server-side Web API or a maintained client-side workflow. Verb calls can carry preconditions, postconditions, policy tags, and logging hooks, allowing agents to synthesize concise programs with explicit control flow and data flow and to produce checkable execution traces. Using representative case studies, we illustrate how verb-level composition can produce correct, reproducible outcomes, while browser agents using low-level interaction primitives may produce brittle behavior or incorrect reasoning. We conclude with a call to action on standardization, developer tooling, and community processes needed to make this semantic layer deployable and trustworthy at web scale.

cs.AI↗

arXiv2025

MCTuner: Spatial Decomposition-Enhanced Database Tuning via LLM-Guided Exploration

Database knob tuning is essential for optimizing the performance of modern database management systems, which often expose hundreds of knobs with continuous or categorical values. However, the large number of knobs and the vast configuration space make it difficult to identify optimal settings efficiently. Although learning-based tuning has shown promise, existing approaches either ignore domain knowledge by relying solely on benchmark feedback or struggle to explore the high-dimensional knob space, resulting in high tuning costs and suboptimal performance. To address these challenges, we propose MCTuner, an adaptive knob tuning framework that minimizes exploration in ineffective regions of the configuration space. MCTuner employs a Mixture-of-Experts (MoE) mechanism with specialized LLMs to identify performance-critical knobs. In further, MCTuner introduces the first spatial decomposition algorithm that recursively partitions the space into hierarchical subspaces, on which Bayesian Optimization is performed to efficiently search for near-optimal configurations. Evaluated on different benchmarks (OLAP, OLTP, and HTAP), MCTuner achieves up to 19.2% performance gains and 1.4x faster configuration discovery per iteration compared to state-of-the-art methods.

cs.DB↗

arXiv2025

Livia: An Emotion-Aware AR Companion Powered by Modular AI Agents and Progressive Memory Compression

Loneliness and social isolation pose significant emotional and health challenges, prompting the development of technology-based solutions for companionship and emotional support. This paper introduces Livia, an emotion-aware augmented reality (AR) companion app designed to provide personalized emotional support by combining modular artificial intelligence (AI) agents, multimodal affective computing, progressive memory compression, and AR driven embodied interaction. Livia employs a modular AI architecture with specialized agents responsible for emotion analysis, dialogue generation, memory management, and behavioral orchestration, ensuring robust and adaptive interactions. Two novel algorithms-Temporal Binary Compression (TBC) and Dynamic Importance Memory Filter (DIMF)-effectively manage and prioritize long-term memory, significantly reducing storage requirements while retaining critical context. Our multimodal emotion detection approach achieves high accuracy, enhancing proactive and empathetic engagement. User evaluations demonstrated increased emotional bonds, improved satisfaction, and statistically significant reductions in loneliness. Users particularly valued Livia's adaptive personality evolution and realistic AR embodiment. Future research directions include expanding gesture and tactile interactions, supporting multi-user experiences, and exploring customized hardware implementations.

cs.HC↗

arXiv2025

Anomaly Detection and Generation with Diffusion Models: A Survey

Anomaly detection (AD) plays a pivotal role across diverse domains, including cybersecurity, finance, healthcare, and industrial manufacturing, by identifying unexpected patterns that deviate from established norms in real-world data. Recent advancements in deep learning, specifically diffusion models (DMs), have sparked significant interest due to their ability to learn complex data distributions and generate high-fidelity samples, offering a robust framework for unsupervised AD. In this survey, we comprehensively review anomaly detection and generation with diffusion models (ADGDM), presenting a tutorial-style analysis of the theoretical foundations and practical implementations and spanning images, videos, time series, tabular, and multimodal data. Crucially, unlike existing surveys that often treat anomaly detection and generation as separate problems, we highlight their inherent synergistic relationship. We reveal how DMs enable a reinforcing cycle where generation techniques directly address the fundamental challenge of anomaly data scarcity, while detection methods provide critical feedback to improve generation fidelity and relevance, advancing both capabilities beyond their individual potential. A detailed taxonomy categorizes ADGDM methods based on anomaly scoring mechanisms, conditioning strategies, and architectural designs, analyzing their strengths and limitations. We final discuss key challenges including scalability and computational efficiency, and outline promising future directions such as efficient architectures, conditioning strategies, and integration with foundation models (e.g., visual-language models and large language models). By synthesizing recent advances and outlining open research questions, this survey aims to guide researchers and practitioners in leveraging DMs for innovative AD solutions across diverse applications.

cs.LG↗

arXiv2024

Outlier Detection Bias Busted: Understanding Sources of Algorithmic Bias through Data-centric Factors

The astonishing successes of ML have raised growing concern for the fairness of modern methods when deployed in real world settings. However, studies on fairness have mostly focused on supervised ML, while unsupervised outlier detection (OD), with numerous applications in finance, security, etc., have attracted little attention. While a few studies proposed fairness-enhanced OD algorithms, they remain agnostic to the underlying driving mechanisms or sources of unfairness. Even within the supervised ML literature, there exists debate on whether unfairness stems solely from algorithmic biases (i.e. design choices) or from the biases encoded in the data on which they are trained. To close this gap, this work aims to shed light on the possible sources of unfairness in OD by auditing detection models under different data-centric factors. By injecting various known biases into the input data -- as pertain to sample size disparity, under-representation, feature measurement noise, and group membership obfuscation -- we find that the OD algorithms under the study all exhibit fairness pitfalls, although differing in which types of data bias they are more susceptible to. Most notable of our study is to demonstrate that OD algorithm bias is not merely a data bias problem. A key realization is that the data properties that emerge from bias injection could as well be organic -- as pertain to natural group differences w.r.t. sparsity, base rate, variance, and multi-modality. Either natural or biased, such data properties can give rise to unfairness as they interact with certain algorithmic design choices.

cs.LG↗

arXiv2024

Enhancing HNSW Index for Real-Time Updates: Addressing Unreachable Points and Performance Degradation

The approximate nearest neighbor search (ANNS) is a fundamental and essential component in data mining and information retrieval, with graph-based methodologies demonstrating superior performance compared to alternative approaches. Extensive research efforts have been dedicated to improving search efficiency by developing various graph-based indices, such as HNSW (Hierarchical Navigable Small World). However, the performance of HNSW and most graph-based indices become unacceptable when faced with a large number of real-time deletions, insertions, and updates. Furthermore, during update operations, HNSW can result in some data points becoming unreachable, a situation we refer to as the `unreachable points phenomenon'. This phenomenon could significantly affect the search accuracy of the graph in certain situations. To address these issues, we present efficient measures to overcome the shortcomings of HNSW, specifically addressing poor performance over long periods of delete and update operations and resolving the issues caused by the unreachable points phenomenon. Our proposed MN-RU algorithm effectively improves update efficiency and suppresses the growth rate of unreachable points, ensuring better overall performance and maintaining the integrity of the graph. Our results demonstrate that our methods outperform existing approaches. Furthermore, since our methods are based on HNSW, they can be easily integrated with existing indices widely used in the industrial field, making them practical for future real-world applications. Code is available at \url{https://github.com/xwt1/MN-RU.git}

cs.IR↗

arXiv2023

mmHawkeye: Passive UAV Detection with a COTS mmWave Radar

Small Unmanned Aerial Vehicles (UAVs) are becoming potential threats to security-sensitive areas and personal privacy. A UAV can shoot photos at height, but how to detect such an uninvited intruder is an open problem. This paper presents mmHawkeye, a passive approach for UAV detection with a COTS millimeter wave (mmWave) radar. mmHawkeye doesn't require prior knowledge of the type, motions, and flight trajectory of the UAV, while exploiting the signal feature induced by the UAV's periodic micro-motion (PMM) for long-range accurate detection. The design is therefore effective in dealing with low-SNR and uncertain reflected signals from the UAV. mmHawkeye can further track the UAV's position with dynamic programming and particle filtering, and identify it with a Long Short-Term Memory (LSTM) based detector. We implement mmHawkeye on a commercial mmWave radar and evaluate its performance under varied settings. The experimental results show that mmHawkeye has a detection accuracy of 95.8% and can realize detection at a range up to 80m.

cs.NI↗

arXiv2023

A Survey of mmWave-based Human Sensing: Technology, Platform and Applications

With the rapid development of the Internet of Things (IoT) and the rise of 5G communication networks and automatic driving, millimeter wave (mmWave) sensing is emerging and starts impacting our life and workspace. mmWave sensing can sense humans and objects in a contactless way, providing fine-grained sensing ability. In the past few years, many mmWave sensing techniques have been proposed and applied in various human sensing applications (e.g., human localization, gesture recognition, and vital monitoring). We discover the need of a comprehensive survey to summarize the technology, platforms and applications of mmWave-based human sensing. In this survey, we first present the mmWave hardware platforms and some key techniques of mmWave sensing. We then provide a comprehensive review of existing mmWave-based human sensing works. Specifically, we divide existing works into four categories according to the sensing granularity: human tracking and localization, motion recognition, biometric measurement and human imaging. Finally, we discuss the potential research challenges and present future directions in this area.

cs.NI↗

arXiv2022

Wi-attack: Cross-technology Impersonation Attack against iBeacon Services

iBeacon protocol is widely deployed to provide location-based services. By receiving its BLE advertisements, nearby devices can estimate the proximity to the iBeacon or calculate indoor positions. However, the open nature of these advertisements brings vulnerability to impersonation attacks. Such attacks could lead to spam, unreliable positioning, and even security breaches. In this paper, we propose Wi-attack, revealing the feasibility of using WiFi devices to conduct impersonation attacks on iBeacon services. Different from impersonation attacks using BLE compatible hardware, Wi-attack is not restricted by broadcasting intervals and is able to impersonate multiple iBeacons at the same time. Effective attacks can be launched on iBeacon services without modifications to WiFi hardware or firmware. To enable direct communication from WiFi to BLE, we use the digital emulation technique of cross technology communication. To enhance the packet reception along with its stability, we add redundant packets to eliminate cyclic prefix error entirely. The emulation provides an iBeacon packet reception rate up to 66.2%. We conduct attacks on three iBeacon services scenarios, point deployment, multilateration, and fingerprint-based localization. The evaluation results show that Wi-attack can bring an average distance error of more than 20 meters on fingerprint-based localization using only 3 APs.

cs.CR↗

arXiv2021

Observation of topologically enabled complete polarization conversion

Exploiting topological ideas has been a major theme in modern photonics, which provides unprecedented opportunities to design photonic devices with robustness against defects and flaws. While most previous works in topological photonics have focused on band theory, recent theoretical advances extend the topological concepts to the analysis of scattering matrices and suggest a topological route to complete polarization conversion (CPC), a unique photonic phenomenon without an electronic counterpart. Here, we report on the experimental observation of the topological effect in reflection matrices of a photonic crystal slab, enabling CPC between two linear polarizations over a wide range of frequencies. Using angle-resolved reflection measurements, we observe CPC occurring at vortex singularities of reflection coefficients in momentum space, verifying the topological nature of CPC. In addition, the topological effect also guarantees the spin-preserved reflection of a circularly polarized wave. Remarkably, we experimentally establish a connection between two seemingly unrelated topological phenomena--CPC and bound states in the continuum (BICs): BICs lie on the critical coupling curves that define the condition for CPC. Our work paves the way to exploring the topological properties in scattering matrices for controlling light polarization and spin and creating robust photonic devices.

physics.class-ph↗

arXiv2021

Topological chiral edge states in deep-subwavelength valley photonic metamaterials

Topological valley photonics has emerged as a new frontier in photonics with many promising applications. Previous valley boundary transport relies on kink states at internal boundaries between two topologically distinct domains. However, recent studies have revealed a novel class of topological chiral edge states (CESs) at external boundaries of valley materials, which have remained elusive in photonics. Here, we propose and experimentally demonstrate the topological CESs in valley photonic metamaterials (VPMMs) by accurately tuning on-site edge potentials. Moreover, the VPMMs work at deep-subwavelength scales. Thus, the supported CESs are highly confined and self-guiding without relying on a cladding layer to prevent leakage radiation. Via direct near-field measurements, we observe the bulk bandgap, the edge dispersions, and the robust edge transport passing through sharp corners, which are hallmarks of the CESs. Our work paves a way to explore novel topological edge states in valley photonics and sheds light on robust and miniaturized photonic devices.

physics.app-ph↗

arXiv2021

Unconventional Weyl exceptional contours in non-Hermitian photonic continua

Unconventional Weyl points with topological charges higher than 1 can transform into various complex unconventional Weyl exceptional contours under non-Hermitian perturbations. However, theoretical studies of these exceptional contours have been limited to tight-binding models. Here, we propose to realize unconventional Weyl exceptional contours in photonic continua -- non-Hermitian anisotropic chiral plasma, based on ab initio calculation by Maxwell's equations. By perturbing in-plane permittivity, an unconventional Weyl point can transform into a quadratic Weyl exceptional circle, a Type-I Weyl exceptional chain with one chain point, a Type-II Weyl exceptional chain with two chain points, or other forms. Realistic metamaterials with effective constitutive parameters are proposed to implement these unconventional Weyl exceptional contours. Our work paves a way toward exploration of exotic physics of unconventional Weyl exceptional contours in non-Hermitian topological photonic continua.

physics.app-ph↗

arXiv2021

Acoustic non-Hermitian skin effect from twisted winding topology

The recently discovered non-Hermitian skin effect (NHSE) manifests the breakdown of current classification of topological phases in energy-nonconservative systems, and necessitates the introduction of non-Hermitian band topology. So far, all NHSE observations are based on one type of non-Hermitian band topology, in which the complex energy spectrum winds along a closed loop. As recently characterized along a synthetic dimension on a photonic platform, non-Hermitian band topology can exhibit almost arbitrary windings in momentum space, but their actual phenomena in real physical systems remain unclear. Here, we report the experimental realization of NHSE in a one-dimensional (1D) non-reciprocal acoustic crystal. With direct acoustic measurement, we demonstrate that a twisted winding, whose topology consists of two oppositely oriented loops in contact rather than a single loop, will dramatically change the NHSE, following previous predictions of unique features such as the bipolar localization and the Bloch point for a Bloch-wave-like extended state. This work reveals previously unnoticed features of NHSE, and provides the observation of physical phenomena originating from complex non-Hermitian winding topology.

physics.app-ph↗

arXiv2020

Photonic topological valley-locked waveguides

Topological valley kink states have become a significant research frontier with considerable intriguing applications such as robust on-chip communications and topological lasers. Unlike guided modes with adjustable widths in most conventional waveguides, the valley kink states are usually highly confined around the domain walls and thus lack the mode width degree of freedom (DOF), posing a serious limitation to potential device applications. Here, by adding a photonic crystal (PhC) featuring a Dirac point between two valley PhCs with opposite valley-Chern numbers, we design and experimentally demonstrate topological valley-locked waveguides (TVLWs) with tunable mode widths. The photoinc TVLWs could find unique applications, such as high-energy-capacity topological channel intersections, valley-locked energy concentrators, and topological cavities with designable confinement, as verified numerically and experimentally. The TVLWs with width DOF could be beneficial to interface with the exsisting photonic waveguides and devices, and serve as a novel platform for practical use of topological lasing, field enhancement, on-chip communicaitons, and high-capacity energy transport.

physics.app-ph↗