arXiv subjects

Xinghua Li

Publications and source records attributed to Xinghua Li.

At least 19 recordsLinked to original sources

arXiv2026

Fronto-parietal and fronto-temporal EEG coherence as predictive neuromarkers of transcutaneous auricular vagus nerve stimulation response in treatment-resistant schizophrenia: A machine learning study

Response variability limits the clinical utility of transcutaneous auricular vagus nerve stimulation (taVNS) for negative symptoms in treatment-resistant schizophrenia (TRS). This study aimed to develop an electroencephalography (EEG)-based machine learning (ML) model to predict individual response and explore associated neurophysiological mechanisms. We used ML to develop and validate predictive models based on pre-treatment EEG data features (power, coherence, and dynamic functional connectivity) from 50 TRS patients enrolled in the taVNS trial, within a nested cross-validation framework. Participants received 20 sessions of active or sham taVNS (n = 25 each) over two weeks, followed by a two-week follow-up. The prediction target was the percentage change in the positive and negative syndrome scale-factor score for negative symptoms (PANSS-FSNS) from baseline to post-treatment, with further evaluation of model specificity and neurophysiological relevance.The optimal model accurately predicted taVNS response in the active group, with predicted PANSS-FSNS changes strongly correlated with observed changes (r = 0.87, p < .001); permutation testing confirmed performance above chance (p < .001). Nine consistently retained features were identified, predominantly fronto-parietal and fronto-temporal coherence features. Negligible predictive performance in the sham group and failure to predict positive symptom change support the predictive specificity of this oscillatory signature for taVNS-related negative symptom improvement. Two coherence features within fronto-parietal-temporal networks showed post-taVNS changes significantly associated with symptom improvement, suggesting dual roles as predictors and potential therapeutic targets. EEG oscillatory neuromarkers enable accurate prediction of individual taVNS response in TRS, supporting mechanism-informed precision neuromodulation strategies.

cs.LG↗

arXiv2026

Passive Synchronization of Nonlocal Franson Interferometry for Fiber-Based Quantum Networks Using Co-propagating Classical Clock Signals

We demonstrate a robust, high-visibility nonlocal Franson interferometry for fiber-based quantum networks by co-propagating a classical Radio-over-Fiber clock signal with energy-time entangled photon pairs in the same fiber. Utilizing cross-band allocation (O-band for classical, L-band for quantum signals), the spontaneous Raman scattering noise photons are effectively suppressed. At the same time, their environmental delay fluctuations remain highly correlated for common-mode noise cancellation, achieving a passive synchronization with picoseconds precision. Over 50 km of single-mode fiber, this co-propagation enables nonlocal quantum interference with a visibility of (88.35\pm3.62)%, without relying on external dedicated timing infrastructure. This work provides a practical, scalable synchronization solution for metropolitan-scale entanglement-based quantum networks.

quant-ph↗

arXiv2026

Co-Propagation of Quantum Time Synchronization and Optical Frequency Transfer over a 122 km Hollow-Core Fiber

The co-propagation of quantum and classical signals through shared optical fibers is crucial for scalable quantum networks. However, this coexistence is fundamentally limited by spontaneous Raman scattering (SpRS) from the bright classical light, which generates overwhelming noise that disrupts the single-photon-level quantum signals. Here, we overcome this long-standing challenge by leveraging the inherently ultralow nonlinearity of hollow-core fiber (HCF) to suppress SpRS noise. By operating both the quantum time synchronization (QTS) and classical optical frequency transfer (OFT) signals within the telecom C-band, separated by only ~10 nm, we successfully demonstrate their simultaneous transmission over a 122-km HCF link. With a classical OFT power of 1 mW, the QTS performance shows negligible degradation, maintaining sub-picosecond time stability at 2000 s, while the OFT achieves a fractional frequency instability of 10^-20. Near-sub-picosecond QTS stability is preserved even when the classical power is increased to 3 mW. Furthermore, simulations based on our experimental data indicate that with next-generation low-loss HCF, the platform can tolerate classical powers beyond 10 mW and extend the QTS range to over 500 km. By realizing a unified quantum-classical time-frequency distribution framework, this work establishes HCF as a highly capable and practical platform for future scalable quantum networks.

quant-ph↗

arXiv2025

The Shawshank Redemption of Embodied AI: Understanding and Benchmarking Indirect Environmental Jailbreaks

The adoption of Vision-Language Models (VLMs) in embodied AI agents, while being effective, brings safety concerns such as jailbreaking. Prior work have explored the possibility of directly jailbreaking the embodied agents through elaborated multi-modal prompts. However, no prior work has studied or even reported indirect jailbreaks in embodied AI, where a black-box attacker induces a jailbreak without issuing direct prompts to the embodied agent. In this paper, we propose, for the first time, indirect environmental jailbreak (IEJ), a novel attack to jailbreak embodied AI via indirect prompt injected into the environment, such as malicious instructions written on a wall. Our key insight is that embodied AI does not ''think twice'' about the instructions provided by the environment -- a blind trust that attackers can exploit to jailbreak the embodied agent. We further design and implement open-source prototypes of two fully-automated frameworks: SHAWSHANK, the first automatic attack generation framework for the proposed attack IEJ; and SHAWSHANK-FORGE, the first automatic benchmark generation framework for IEJ. Then, using SHAWSHANK-FORGE, we automatically construct SHAWSHANK-BENCH, the first benchmark for indirectly jailbreaking embodied agents. Together, our two frameworks and one benchmark answer the questions of what content can be used for malicious IEJ instructions, where they should be placed, and how IEJ can be systematically evaluated. Evaluation results show that SHAWSHANK outperforms eleven existing methods across 3,957 task-scene combinations and compromises all six tested VLMs. Furthermore, current defenses only partially mitigate our attack, and we have responsibly disclosed our findings to all affected VLM vendors.

cs.CR↗

arXiv2025

Weakly Supervised Cloud Detection Combining Spectral Features and Multi-Scale Deep Network

Clouds significantly affect the quality of optical satellite images, which seriously limits their precise application. Recently, deep learning has been widely applied to cloud detection and has achieved satisfactory results. However, the lack of distinctive features in thin clouds and the low quality of training samples limit the cloud detection accuracy of deep learning methods, leaving space for further improvements. In this paper, we propose a weakly supervised cloud detection method that combines spectral features and multi-scale scene-level deep network (SpecMCD) to obtain highly accurate pixel-level cloud masks. The method first utilizes a progressive training framework with a multi-scale scene-level dataset to train the multi-scale scene-level cloud detection network. Pixel-level cloud probability maps are then obtained by combining the multi-scale probability maps and cloud thickness map based on the characteristics of clouds in dense cloud coverage and large cloud-area coverage images. Finally, adaptive thresholds are generated based on the differentiated regions of the scene-level cloud masks at different scales and combined with distance-weighted optimization to obtain binary cloud masks. Two datasets, WDCD and GF1MS-WHU, comprising a total of 60 Gaofen-1 multispectral (GF1-MS) images, were used to verify the effectiveness of the proposed method. Compared to the other weakly supervised cloud detection methods such as WDCD and WSFNet, the F1-score of the proposed SpecMCD method shows an improvement of over 7.82%, highlighting the superiority and potential of the SpecMCD method for cloud detection under different cloud coverage conditions.

cs.CV↗

arXiv2025

GeoHopNet: Hopfield-Augmented Sparse Spatial Attention for Dynamic UAV Site Location Problem

The rapid development of urban low-altitude unmanned aerial vehicle (UAV) economy poses new challenges for dynamic site selection of UAV landing points and supply stations. Traditional deep reinforcement learning methods face computational complexity bottlenecks, particularly with standard attention mechanisms, when handling large-scale urban-level location problems. This paper proposes GeoHopNet, a Hopfield-augmented sparse spatial attention network specifically designed for dynamic UAV site location problems. Our approach introduces four core innovations: (1) distance-biased multi-head attention mechanism that explicitly encodes spatial geometric information; (2) K-nearest neighbor sparse attention that reduces computational complexity from $O(N^2)$ to $O(NK)$; (3) a modern Hopfield external memory module; and (4) a memory regularization strategy. Experimental results demonstrate that GeoHopNet extends the boundary of solvable problem sizes. For large-scale instances with 1,000 nodes, where standard attention models become prohibitively slow (over 3 seconds per instance) and traditional solvers fail, GeoHopNet finds high-quality solutions (0.22\% optimality gap) in under 0.1 seconds. Compared to the state-of-the-art ADNet baseline on 100-node instances, our method improves solution quality by 22.2\% and is 1.8$\times$ faster.

cs.LG↗

arXiv2025

Integrated Warehouse Location and Inventory Decisions in a Multi-location Newsvendor Problem

In this paper, we investigate a supply chain network with a supplier and multiple retailers. The supplier can either take orders from retailers directly, or choose to build a warehouse somewhere in the network to centralize the ordering from retailers. Meanwhile, we take three modes of transportation cost into account, including distance-dependent, quantity-dependent, and distance-quantity-dependent costs, to formulate six models. For the three decentralized models, we provide closed-form solutions to compute the optimal order quantity of each retailer. For the centralized models, we develop closed-form solutions for the first two models as the transportation cost only depends on either distance or order quantity; but when it depends on both, the model becomes a non-linear programming problem. We develop a solution algorithm named Q-search to find a high-quality solution that includes the order quantity and the warehouse location. The experiment results show that the centralized model outperforms the decentralized model in large networks in terms of profit and service level.

econ.GN↗

arXiv2025

Selective Newsvendor Problem with Dependent Leadtime and Joint Marketing Decisions

In this paper, we investigate a joint decision-making pattern for a two-stage supply chain network, including a supplier, a company, and its customers. We investigate two types of demand patterns, associated with dependent lead time and service level considerations. We define two novel models, including all-or-nothing selective newsvendor problem (AON-SNP) and selective news Vendor Problem with Dependent Lead Time and Price Related Demands (DLSNP). The proposed models are applicable to numerous areas such as the fashion, furniture, and electronic industries. We develop an efficient solution algorithm, referred to as R-search, to identify an optimal solution for the DL-selectivity problem. We examine various responses of the system through parameter sensitivity analysis. Our model proves that if the total demand is lower than the upper limit of the order quantity, the best strategy for Q is to match that demand. If the market increases, more demand comes in, leading to a shortage, and forcing the company to find other local suppliers to fill the additional demand. The results demonstrate that our model well explains various behavior for all involved parties in the network, and provide guidance on intelligent decision making for the company.

econ.GN↗

arXiv2025

CPA-RAG:Covert Poisoning Attacks on Retrieval-Augmented Generation in Large Language Models

Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by incorporating external knowledge, but its openness introduces vulnerabilities that can be exploited by poisoning attacks. Existing poisoning methods for RAG systems have limitations, such as poor generalization and lack of fluency in adversarial texts. In this paper, we propose CPA-RAG, a black-box adversarial framework that generates query-relevant texts capable of manipulating the retrieval process to induce target answers. The proposed method integrates prompt-based text generation, cross-guided optimization through multiple LLMs, and retriever-based scoring to construct high-quality adversarial samples. We conduct extensive experiments across multiple datasets and LLMs to evaluate its effectiveness. Results show that the framework achieves over 90\% attack success when the top-k retrieval setting is 5, matching white-box performance, and maintains a consistent advantage of approximately 5 percentage points across different top-k values. It also outperforms existing black-box baselines by 14.5 percentage points under various defense strategies. Furthermore, our method successfully compromises a commercial RAG system deployed on Alibaba's BaiLian platform, demonstrating its practical threat in real-world applications. These findings underscore the need for more robust and secure RAG frameworks to defend against poisoning attacks.

cs.CR↗

arXiv2025

A Secure Communication Protocol for Remote Keyless Entry System with Adaptive Adjustment of Transmission Parameters

Remote Keyless Entry (RKE) systems have become a standard feature in modern vehicles, yet their unidirectional fixed-frequency radio communication renders them vulnerable to replay attacks, impersonation attacks, cryptanalysis, and intentional interference. Existing cryptographic authentication methods enhance security but often fail to address real-world constraints such as computational efficiency and radio interference. To mitigate these threats, we designed the Adaptive Frequency-Hopping Algorithm and the Adaptive TXP and PHY Mode Control Algorithm that can dynamically optimize channel selection, transmission power, and PHY modes based on real-time channel quality assessment. To enhance the security and reliability of RKE systems, we propose the Lightweight Vehicle-Key Authentication Protocol. In addition, a prototype of the proposed scheme was implemented to verify its effectiveness in mitigating interference and preventing unauthorized access.Experimental results show that our scheme significantly enhances communication security and reliability while maintaining low computational overhead. Under mild interference conditions, the packet delivery rate (PDR) of the adaptive scheme increases from 93% to 99.23%, and under strong interference, it improves from 85% to 99.01%. Additionally, the scheme effectively prevents replay and impersonation attacks, ensuring secure vehicle access control by dynamically optimizing communication parameters to maintain stable and reliable transmission.

cs.CR↗

arXiv2024

A Survey of Protocol Fuzzing

Communication protocols form the bedrock of our interconnected world, yet vulnerabilities within their implementations pose significant security threats. Recent developments have seen a surge in fuzzing-based research dedicated to uncovering these vulnerabilities within protocol implementations. However, there still lacks a systematic overview of protocol fuzzing for answering the essential questions such as what the unique challenges are, how existing works solve them, etc. To bridge this gap, we conducted a comprehensive investigation of related works from both academia and industry. Our study includes a detailed summary of the specific challenges in protocol fuzzing, and provides a systematic categorization and overview of existing research efforts. Furthermore, we explore and discuss potential future research directions in protocol fuzzing. This survey serves as a foundational guideline for researchers and practitioners in the field.

cs.CR↗

arXiv2024

Quantum microwave photonic mixer with a large spurious-free dynamic range

As one of the most fundamental functionalities of microwave photonics, microwave frequency mixing plays an essential role in modern radars and wireless communication systems. However, the commonly utilized intensity modulation in the systems often leads to inadequate spurious-free dynamic range (SFDR) for many sought-after applications. Quantum microwave photonics technique offers a promising solution for improving SFDR in terms of higher-order harmonic distortion. In this paper, we demonstrate two types of quantum microwave photonic mixers based on the configuration of the intensity modulators: cascade-type and parallel-type. Leveraging the nonlocal RF signal encoding capability, both types of quantum microwave photonic mixers not only exhibit the advantage of dual-channel output but also present significant improvement in SFDR. Specifically, the parallel-type quantum microwave photonic mixer achieves a remarkable SFDR value of 113.6 dB.Hz1/2, which is 30 dB better than that of the cascade-type quantum microwave photonic mixer. When compared to the classical microwave photonic mixer, this enhancement reaches a notable 53.6 dB at the expense of 8 dB conversion loss. These results highlight the superiority of quantum microwave photonic mixers in the fields of microwave and millimeter-wave systems. Further applying multi-photon frequency entangled sources as optical carriers, the dual-channel microwave frequency conversion capability endowed by the quantum microwave photonic mixer can be extended to enhance the performance of multiple-paths microwave mixing which is essential for radar net systems.

physics.optics↗

arXiv2024

A versatile quantum microwave photonic signal processing platform based on coincidence window selection technique

Quantum microwave photonics (QMWP) is an innovative approach that combines energy-time entangled biphoton sources as the optical carrier with time-correlated single-photon detection for high-speed RF signal recovery. This groundbreaking method offers unique advantages such as nonlocal RF signal encoding and robust resistance to dispersion-induced frequency fading. This paper explores the versatility of processing the quantum microwave photonic signal by utilizing coincidence window selection on the biphoton coincidence distribution. The demonstration includes finely-tunable RF phase shifting, flexible multi-tap transversal filtering (with up to 15 taps), and photonically implemented RF mixing, leveraging the nonlocal RF mapping characteristic of QMWP. These accomplishments significantly enhance the capability of microwave photonic systems in processing ultra-weak signals, opening up new possibilities for various applications.

physics.optics↗

arXiv2024

AI-Empowered Multiple Access for 6G: A Survey of Spectrum Sensing, Protocol Designs, and Optimizations

With the rapidly increasing number of bandwidth-intensive terminals capable of intelligent computing and communication, such as smart devices equipped with shallow neural network models, the complexity of multiple access for these intelligent terminals is increasing due to the dynamic network environment and ubiquitous connectivity in 6G systems. Traditional multiple access (MA) design and optimization methods are gradually losing ground to artificial intelligence (AI) techniques that have proven their superiority in handling complexity. AI-empowered MA and its optimization strategies aimed at achieving high Quality-of-Service (QoS) are attracting more attention, especially in the area of latency-sensitive applications in 6G systems. In this work, we aim to: 1) present the development and comparative evaluation of AI-enabled MA; 2) provide a timely survey focusing on spectrum sensing, protocol design, and optimization for AI-empowered MA; and 3) explore the potential use cases of AI-empowered MA in the typical application scenarios within 6G systems. Specifically, we first present a unified framework of AI-empowered MA for 6G systems by incorporating various promising machine learning techniques in spectrum sensing, resource allocation, MA protocol design, and optimization. We then introduce AI-empowered MA spectrum sensing related to spectrum sharing and spectrum interference management. Next, we discuss the AI-empowered MA protocol designs and implementation methods by reviewing and comparing the state-of-the-art, and we further explore the optimization algorithms related to dynamic resource management, parameter adjustment, and access scheme switching. Finally, we discuss the current challenges, point out open issues, and outline potential future research directions in this field.

cs.NI↗

arXiv2023

Developing a Comprehensive Model for Feasibility Analysis of Separated Bike Lanes and Electric Bike Lanes: A Case Study in Shanghai, China

Electric bikes (e-bikes), including lightweight e-bikes with pedals and e-bikes in scooter form, are gaining popularity around the world because of their convenience and affordability. At the same time, e-bike-related accidents are also on the rise and many policymakers and practitioners are debating the feasibility of building e-bike lanes in their communities. By collecting e-bikes and bikes data in Shanghai City, the study first recalibrates the capacity of the conventional bike lane based on the traffic movement characteristics of the mixed bikes flow. Then, the study evaluates the traffic safety performance of the mixed bike flow in the conventional bike lane by the observed passing events. Finally, this study proposes a comprehensive model for evaluating the feasibility of building an e-bike lane by integrating the Analytic Hierarchy Process and fuzzy mathematics by considering the three objectives: capacity, safety, and budget constraint. The proposed model, one of the first of its kind, can be used to (i) evaluate the existing road capacity and safety performance improvement of a mixed bike flow with e-bikes and human-powered bikes by analyzing the mixed bike flow arrival rate and passing maneuvers, and (ii) quantify the changes to the road capacity and safety performance if a new e-bike lane is constructed. Numerical experiments are performed to calibrate the proposed model and evaluate its performance using non-motorized vehicles' trajectories in Shanghai, China. The numerical experiment results suggest that the proposed model can be used by policymakers and practitioners to evaluate the feasibility of building e-bike lanes.

physics.soc-ph↗

arXiv2022

An attention mechanism based convolutional network for satellite precipitation downscaling over China

Precipitation is a key part of hydrological circulation and is a sensitive indicator of climate change. The Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG) datasets are widely used for global and regional precipitation investigations. However, their local application is limited by the relatively coarse spatial resolution. Therefore, in this paper, an attention mechanism based convolutional network (AMCN) is proposed to downscale GPM IMERG monthly precipitation data. The proposed method is an end-to-end network, which consists of a global cross-attention module, a multi-factor cross-attention module, and a residual convolutional module, comprehensively considering the potential relationships between precipitation and complicated surface characteristics. In addition, a degradation loss function based on low-resolution precipitation is designed to physically constrain the network training, to improve the robustness of the proposed network under different time and scale variations. The experiments demonstrate that the proposed network significantly outperforms three baseline methods. Finally, a geographic difference analysis method is introduced to further improve the downscaled results by incorporating in-situ measurements for high-quality and fine-scale precipitation estimation.

cs.CV↗

arXiv2021

Fully Polarimetric SAR and Single-Polarization SAR Image Fusion Network

The data fusion technology aims to aggregate the characteristics of different data and obtain products with multiple data advantages. To solves the problem of reduced resolution of PolSAR images due to system limitations, we propose a fully polarimetric synthetic aperture radar (PolSAR) images and single-polarization synthetic aperture radar SAR (SinSAR) images fusion network to generate high-resolution PolSAR (HR-PolSAR) images. To take advantage of the polarimetric information of the low-resolution PolSAR (LR-PolSAR) image and the spatial information of the high-resolution single-polarization SAR (HR-SinSAR) image, we propose a fusion framework for joint LR-PolSAR image and HR-SinSAR image and design a cross-attention mechanism to extract features from the joint input data. Besides, based on the physical imaging mechanism, we designed the PolSAR polarimetric loss function for constrained network training. The experimental results confirm the superiority of fusion network over traditional algorithms. The average PSNR is increased by more than 3.6db, and the average MAE is reduced to less than 0.07. Experiments on polarimetric decomposition and polarimetric signature show that it maintains polarimetric information well.

eess.IV↗

arXiv2021

Enhanced metal-insulator transition in freestanding VO2 down to 5 nm thickness

Ultrathin freestanding membranes with a pronounced metal-insulator transition (MIT) provides huge potential in future flexible electronic applications as well as a unique aspect of the study of lattice-electron interplay. However, the reduction of the thickness to an ultrathin region (a few nm) is typically detrimental to the MIT in epitaxial films, and even catastrophic for their freestanding form. Here, we report an enhanced MIT in VO2-based freestanding membranes, with a lateral size up to millimetres and VO2 thickness down to 5 nm. The VO2-membranes were detached by dissolving a Sr3Al2O6 sacrificial layer between the VO2 thin film and c-Al2O3(0001) substrate, allowing a transfer onto arbitrary surfaces. Furthermore, the MIT in the VO2-membrane was greatly enhanced by inserting an intermediate Al2O3 buffer layer. In comparison to the best available ultrathin VO2-membranes, the enhancement of MIT is over 400% at 5 nm VO2 thickness and more than one order of magnitude for VO2 above 10 nm. Our study widens the spectrum of functionality in ultrathin and large-scale membranes, and enables the potential integration of MIT into flexible electronics and photonics.

cond-mat.mtrl-sci↗