arXiv subjects

Qiang Zeng

Publications and source records attributed to Qiang Zeng.

At least 19 recordsLinked to original sources

arXiv2026

Impedance Modeling and Stability Analysis of Droop-Controlled Inverter Under Unbalanced Power Grid Operating Conditions

With the growing integration of renewable energy sources into power grids, the risks of oscillation caused by interactions between grid-tied inverters and the grids are becoming increasingly prominent. Although existing studies have made significant progress in inverter modeling and oscillatory stability analysis, most of them do not sufficiently consider complex mirror frequency coupling effects (MFCE) under unbalanced operating conditions, leading to unreliable models and erroneous stability analysis results. To address this inadequacy, this work develops a novel sequence impedance modeling scheme that can be widely applied to unbalanced operating conditions. In particular, taking a representative type of grid-forming inverter for instance, i.e., droop-controlled inverter (DCI), a single-input single-output sequence impedance modeling method based on harmonic linearization (HL) is proposed to comprehensively model both a given DCI and the connected grid. By accounting for multi-frequency interactions within the DCI, this method captures MFCE and unbalanced factors, leading to a more accurate impedance model. Further, the dominant factors influencing system stability are identified with a combination of normalized sensitivity analysis and proportional weighting. Finally, the detailed impacts of these dominant factors on system stability margin under three typical unbalanced operating conditions are analyzed through the Bode criterion. The effectiveness and reliability of the whole scheme proposed in this work are validated on the constructed grid-connected droop-controlled experimental platform.

eess.SY↗

arXiv2026

Transmitter-device-independent quantum key distribution

Transmitter-side device dependence is a longstanding yet implicit problem in quantum key distribution (QKD), in contrast to the thoroughly addressed measurement-side case. Quantum steering, which intrinsically distinguishes the roles of sender and receiver in entanglement certification, offers a natural route to lifting trust assumptions on the transmitter. Existing one-sided device-independent (1sDI) protocols primarily exploit steering as a security resource, and provoke a signaling loophole in the transmitter-receiver architecture. Here we formalize transmitter-device-independence on the basis of faithful quantum steering and propose a transmitter-device-independent (TDI) QKD protocol within 1sDI framework that closes this loophole through introducing a photon storage, thereby eliminating transmitter-side device vulnerabilities. In a proof-of-principle experiment we validate the feasibility of the TDI protocol, achieving a key-rate in the asymptotic limit of 410~bps over 27~km spool fiber. By delivering TDI security while retaining strong loss tolerance, our approach helps bridge the gap between security and practicality for real-world QKD deployments.

quant-ph↗

arXiv2026

Zero-Shot Vulnerability Detection in Low-Resource Smart Contracts Through Solidity-Only Training

Smart contracts have transformed decentralized finance, but flaws in their logic still create major security threats. Most existing vulnerability detection techniques focus on well-supported languages like Solidity, while low-resource counterparts such as Vyper remain largely underexplored due to scarce analysis tools and limited labeled datasets. Training a robust detection model directly on Vyper is particularly challenging, as collecting sufficiently large and diverse Vyper training datasets is difficult in practice. To address this gap, we introduce Sol2Vy, a novel framework that enables cross-language knowledge transfer from Solidity to Vyper, allowing vulnerability detection on Vyper using models trained exclusively on Solidity. This approach eliminates the need for extensive labeled Vyper datasets typically required to build a robust vulnerability detection model. We implement and evaluate Sol2Vy on various critical vulnerability types, including reentrancy, weak randomness, and unchecked transfer. Experimental results show that Sol2Vy, despite being trained exclusively on Solidity, achieves strong detection performance on Vyper contracts and significantly outperforms prior state-of-the-art methods.

cs.CR↗

arXiv2026

A Directly Modulated Laser Platform for High-Dimensional Quantum Key Distribution

High-dimensional quantum key distribution (HD-QKD) offers a promising approach to enhance secret key rates beyond conventional binary-encoded QKD, addressing the growing demand for secure data transmission. However, the practical application of most HD-QKD systems has been hindered by their complexity, as they require the preparation and detection of quantum states in large Hilbert spaces. Here, we design and experimentally realize a directly modulated laser platform for HD-QKD. It operates at a repetition rate of 312.5 MHz, yielding a remarkably simple and scalable architecture. Through which, we achieve a record transmission distance of 250 km for HD-QKD, demonstrating its feasibility for long-distance quantum communication. Furthermore, we witness that the four-dimensional states outperform their two-dimensional counterpart in secret key rate, highlighting the practical advantage of high-dimensional encoding. This simple and scalable approach shows strong potential for chip-scale integration.

quant-ph↗

arXiv2025

Large deviations for the extremal eigenvalues of Ginibre ensembles

We establish large deviation principles for the extremal eigenvalues of the Ginibre ensembles with good rate functions. In contrast to the typical estimates for the extremal eigenvalues, the large deviations for the real Ginibre ensemble come from the eigenvalues lying on the real line. Moreover, we also derive deviation estimates for the second leading term in the asymptotic expansion of the extremal eigenvalues. These polynomially small deviation estimates are universal for any i.i.d. matrices under a mild moment condition.

math.PR↗

arXiv2025

DEAL-300K: Diffusion-based Editing Area Localization with a 300K-Scale Dataset and Frequency-Prompted Baseline

Diffusion-based image editing has made semantic level image manipulation easy for general users, but it also enables realistic local forgeries that are hard to localize. Existing benchmarks mainly focus on the binary detection of generated images or the localization of manually edited regions and do not reflect the properties of diffusion-based edits, which often blend smoothly into the original content. We present Diffusion-Based Image Editing Area Localization Dataset (DEAL-300K), a large scale dataset for diffusion-based image manipulation localization (DIML) with more than 300,000 annotated images. We build DEAL-300K by using a multi-modal large language model to generate editing instructions, a mask-free diffusion editor to produce manipulated images, and an active-learning change detection pipeline to obtain pixel-level annotations. On top of this dataset, we propose a localization framework that uses a frozen Visual Foundation Model (VFM) together with Multi Frequency Prompt Tuning (MFPT) to capture both semantic and frequency-domain cues of edited regions. Trained on DEAL-300K, our method reaches a pixel-level F1 score of 82.56% on our test split and 80.97% on the external CoCoGlide benchmark, providing strong baselines and a practical foundation for future DIML research.The dataset can be accessed via https://github.com/ymhzyj/DEAL-300K.

cs.CV↗

arXiv2025

RadGS-Reg: Registering Spine CT with Biplanar X-rays via Joint 3D Radiative Gaussians Reconstruction and 3D/3D Registration

Computed Tomography (CT)/X-ray registration in image-guided navigation remains challenging because of its stringent requirements for high accuracy and real-time performance. Traditional "render and compare" methods, relying on iterative projection and comparison, suffer from spatial information loss and domain gap. 3D reconstruction from biplanar X-rays supplements spatial and shape information for 2D/3D registration, but current methods are limited by dense-view requirements and struggles with noisy X-rays. To address these limitations, we introduce RadGS-Reg, a novel framework for vertebral-level CT/X-ray registration through joint 3D Radiative Gaussians (RadGS) reconstruction and 3D/3D registration. Specifically, our biplanar X-rays vertebral RadGS reconstruction module explores learning-based RadGS reconstruction method with a Counterfactual Attention Learning (CAL) mechanism, focusing on vertebral regions in noisy X-rays. Additionally, a patient-specific pre-training strategy progressively adapts the RadGS-Reg from simulated to real data while simultaneously learning vertebral shape prior knowledge. Experiments on in-house datasets demonstrate the state-of-the-art performance for both tasks, surpassing existing methods. The code is available at: https://github.com/shenao1995/RadGS_Reg.

cs.CV↗

arXiv2025

FlowMalTrans: Unsupervised Binary Code Translation for Malware Detection Using Flow-Adapter Architecture

Applying deep learning to malware detection has drawn great attention due to its notable performance. With the increasing prevalence of cyberattacks targeting IoT devices, there is a parallel rise in the development of malware across various Instruction Set Architectures (ISAs). It is thus important to extend malware detection capacity to multiple ISAs. However, training a deep learning-based malware detection model usually requires a large number of labeled malware samples. The process of collecting and labeling sufficient malware samples to build datasets for each ISA is labor-intensive and time-consuming. To reduce the burden of data collection, we propose to leverage the ideas of Neural Machine Translation (NMT) and Normalizing Flows (NFs) for malware detection. Specifically, when dealing with malware in a certain ISA, we translate it to an ISA with sufficient malware samples (like X86-64). This allows us to apply a model trained on one ISA to analyze malware from another ISA. Our approach reduces the data collection effort by enabling malware detection across multiple ISAs using a model trained on a single ISA.

cs.CR↗

arXiv2025

High-Performance Fully Passive Discrete-State Continuous-Variable Quantum Key Distribution With Local Local Oscillator

We propose and demonstrate a fully passive discrete-state continuous-variable quantum key distribution (CV-QKD), which can eliminate all modulator side channels on the source side, using a local local oscillator (LLO). The CV-QKD system achieves a maximum transmission length of 100 km with a repetition rate of 1 GHz using specially designed phase rotation and discretization methods, and the corresponding secret key bit rate is 127 kbps, as estimated based on the amplitude of prepared states at the transmitter, as well as the first- and second-order moments of quadratures at the receiver by employing the convex optimization without imposing any assumptions on the quantum channel. The performance of the protocol is similar to that of modulated CV LLO protocols and better than those of passive discrete-variable and CV protocols. Our protocol is expected to play an important role in the quantum metropolitan area networks and quantum access networks with high realistic security.

quant-ph↗

arXiv2025

Quantitative Damping Calculation and Compensation Method for Global Stability Improvement of Inverter-Based Systems

Small-signal stability issues-induced broadband oscillations pose significant threats to the secure operation of multi-inverter systems, attracting extensive research attention. Researches revealed that system instability is led by the lacking of positive damping, yet it has not been clearly specified how much the exact amount of damping compensation required to sufficiently ensure system global stability. This paper presents a feasible solution for quantitative damping calculation and compensation to enhance the global stability of inverter-based systems. First, based on the system nodal admittance model, a quantitative damping calculation algorithm is presented, which can suggest the required damping compensation as well as compensation location for sufficient stability improvement. Then, we propose a specific AD with output current feedforward control strategy, which make the AD be quasi-pure resistive and can effectively enhance system damping efficiency. Finally, a testing system with three inverters is used as case study, showing that the proposed method provides a promising solution to efficiently enhance the global stability improvement of inverter-based systems. Simulations and experiments validate the proposed method.

eess.SY↗

arXiv2025

Impact of Fine-Tuning Methods on Memorization in Large Language Models

As the capabilities of pre-trained large language models (LLMs) continue to advance, the "pre-train and fine-tune" paradigm has become increasingly mainstream, leading to the development of various fine-tuning methods. However, the privacy risks arising from memorization during fine-tuning have received relatively little attention. To address this gap, we categorize popular fine-tuning approaches and assess their impact on memorization through the lens of membership inference attacks (MIAs). Our results show that, compared to parameter-based fine-tuning, prompt-based fine-tuning achieves competitive performance while exhibiting lower vulnerability to MIAs. Furthermore, prompt-based methods maintain low memorization regardless of model scale. These findings suggest that parameter-based fine-tuning is more prone to leaking private information, whereas prompt-based fine-tuning serves as a more privacy-preserving option.

cs.CL↗

arXiv2025

Lane Departure Accident Prevention in Foggy Conditions: A Prior-Guided Dynamic Feature Fusion Transformer Framework for Real-Time Lane Detection

Lane departure accident prevention plays a critical role in enhancing road safety, and lane detection is a core technology to achieve this goal, especially under complex weather conditions. While existing lane detection algorithms perform well under favorable weather conditions, their effectiveness significantly degrades in foggy environments, which increases the risk of traffic accidents. In response to this challenge, we propose PDT-Net, a robust Prior-Guided Dynamic Feature Fusion Transformer framework designed for real-time lane detection in foggy conditions. This framework integrates three key modules: a Global Feature Fusion Module (GFFM) to capture the relationship between local and global features in foggy images, a Dynamic Feature Fusion Module (DFFM) to model the structural and positional relationships of lane instances, and a Prior-Guided Edge Enhancement Module (PEM) to recover lost edge details in foggy environments. Furthermore, we introduce the FoggyLane dataset, a real-world dataset that specifically targets lane detection in foggy conditions, along with two synthesized datasets, FoggyCULane and FoggyTusimple, to address the lack of fog-specific data for lane detection. Extensive experiments show that PDT-Net achieves state-of-the-art performance with F1-scores of 95.04% on FoggyLane, 79.85% on FoggyCULane, and 96.95% on FoggyTusimple. Moreover, with TensorRT acceleration, our method achieves a processing speed of 38.4 FPS on the NVIDIA Jetson AGX Orin, confirming its real-time capability and robustness in challenging foggy environments. By improving the precision of lane detection, our framework can contribute to active safety warning systems, helping to prevent accidents in foggy conditions.

cs.CV↗

arXiv2024

BinSimDB: Benchmark Dataset Construction for Fine-Grained Binary Code Similarity Analysis

Binary Code Similarity Analysis (BCSA) has a wide spectrum of applications, including plagiarism detection, vulnerability discovery, and malware analysis, thus drawing significant attention from the security community. However, conventional techniques often face challenges in balancing both accuracy and scalability simultaneously. To overcome these existing problems, a surge of deep learning-based work has been recently proposed. Unfortunately, many researchers still find it extremely difficult to conduct relevant studies or extend existing approaches. First, prior work typically relies on proprietary benchmark without making the entire dataset publicly accessible. Consequently, a large-scale, well-labeled dataset for binary code similarity analysis remains precious and scarce. Moreover, previous work has primarily focused on comparing at the function level, rather than exploring other finer granularities. Therefore, we argue that the lack of a fine-grained dataset for BCSA leaves a critical gap in current research. To address these challenges, we construct a benchmark dataset for fine-grained binary code similarity analysis called BinSimDB, which contains equivalent pairs of smaller binary code snippets, such as basic blocks. Specifically, we propose BMerge and BPair algorithms to bridge the discrepancies between two binary code snippets caused by different optimization levels or platforms. Furthermore, we empirically study the properties of our dataset and evaluate its effectiveness for the BCSA research. The experimental results demonstrate that BinSimDB significantly improves the performance of binary code similarity comparison.

cs.CR↗

arXiv2024

Touch to Pair: Secure and Usable IoT Pairing without Information Loss

Secure pairing is essential for trustworthy deployment and operation of Internet of Things (IoT) devices. However, traditional pairing methods are unsuitable due to the lack of user interfaces such as keyboards. Proximity-based approaches are usable but vulnerable to nearby attackers, while methods relying on physical operations (e.g., shaking) offer higher security but require inertial sensors that most IoT devices lack. We introduceUniversal Operation Sensing, which enables IoT devices to detect user operations without inertial sensors. With this technique, users can complete pairing within seconds through simple actions, such as pressing a button or twisting a knob, using either a smartphone or a smartwatch. We further identify an accuracy issue caused by information loss in the commonly used fuzzy-commitment protocol. To address this issue, we propose TimeWall, an accurate pairing protocol that avoids fuzzy commitment and incurs zero information loss. A comprehensive evaluation shows that it is secure, usable, and efficient.

cs.CR↗

arXiv2024

Post-Measurement Pairing Quantum Key Distribution with Local Optical Frequency Standard

The idea of post-measurement coincidence pairing simplifies substantially long-distance, repeater-like quantum key distribution (QKD) by eliminating the need for tracking the differential phase of the users' lasers. However, optical frequency tracking remains necessary and can become a severe burden in future deployment of multi-node quantum networks. Here, we resolve this problem by referencing each user's laser to an absolute frequency standard and demonstrate a practical post-measurement pairing QKD with excellent long-term stability. We confirm the setup's repeater-like behavior and achieve a finite-size secure key rate (SKR) of 15.94 bit/s over 504 km fiber, which overcomes the absolute repeaterless bound by 1.28 times. Over a fiber length 100 km, the setup delivers an impressive SKR of 285.68 kbit/s. Our work paves the way towards an efficient muti-user quantum network with the local frequency standard.

quant-ph↗

arXiv2024

Bright Heralded Single-Photon Source Saturating Theoretical Single-photon Purity

Single-photon source is the cornerstone for modern quantum information processing. The present work derives the theoretical limit of single-photon purity for general parametric heralded single-photon sources, and subsequently demonstrates a bright, gigahertz-pulsed heralded source with the purity saturating the limit. By stimulating spontaneous four-wave mixing effect in the silicon spiral waveguide, this on-chip source is measured to have a coincidence rate exceeding 1.5~MHz at a coincidence-to-accidental ratio (CAR) of 16.77 in the photon pair correlation experiment. The single-photon purity of this source, quantified by the heralded auto-correlation function $g^{(2)}_\text{h}(0)$, is measured by a heralded Hanbury Brown--Twiss setup to reach the theoretical limit with the lowest value of $0.00094 \pm 0.00002$ obtained at a coincidence rate of 0.8~kHz and a CAR of 2220. The performance improvements are attributed to effective spectral filtering suppressing the noise as well as the coherent pump condition helped by optical injection locking. The reported results provide a reliable standard for benchmarking heralded single-photon sources and present a state-of-the-art heralded source for quantum information processing.

quant-ph↗

arXiv2024

Steering nonlocality in high-speed telecommunication system without detection loophole

Nonlocal correlation represents the key feature of quantum mechanics, and is an exploitable resource in quantum information processing. However, the loophole issues and the associated applicability compromises hamper the practical applications. We report the first time-bin entangled detection-loophole-free steering nonlocality demonstration in a fully chip-fiber telecommunication system, with an ultra-fast measurement switching rate (1.25~GHz). In this endeavor, we propose the phase-encoding measurement scheme to adapt the system to time-bin degree of freedom, and design and fabricate a low-loss silicon chip for efficient entanglement generation. An asymmetric configuration is introduced to mimic the active measurement implementation at the steering party thus bypassing the phase modulation loss. Consequently, we build a fiber-optic setup that can overcome the detection efficiency required by conclusive quantum steering with multiple actively switched measurement settings. Our setup presents an immediate platform for exploring applications based on steering nonlocality, especially for quantum communication.

quant-ph↗

arXiv2024

Progress in artificial intelligence applications based on the combination of self-driven sensors and deep learning

In the era of Internet of Things, how to develop a smart sensor system with sustainable power supply, easy deployment and flexible use has become a difficult problem to be solved. The traditional power supply has problems such as frequent replacement or charging when in use, which limits the development of wearable devices. The contact-to-separate friction nanogenerator (TENG) was prepared by using polychotomy thy lene (PTFE) and aluminum (AI) foils. Human motion energy was collected by human body arrangement, and human motion posture was monitored according to the changes of output electrical signals. In 2012, Academician Wang Zhong lin and his team invented the triboelectric nanogenerator (TENG), which uses Maxwell displacement current as a driving force to directly convert mechanical stimuli into electrical signals, so it can be used as a self-driven sensor. Teng-based sensors have the advantages of simple structure and high instantaneous power density, which provides an important means for building intelligent sensor systems. At the same time, machine learning, as a technology with low cost, short development cycle, strong data processing ability and prediction ability, has a significant effect on the processing of a large number of electrical signals generated by TENG, and the combination with TENG sensors will promote the rapid development of intelligent sensor networks in the future. Therefore, this paper is based on the intelligent sound monitoring and recognition system of TENG, which has good sound recognition capability, and aims to evaluate the feasibility of the sound perception module architecture in ubiquitous sensor networks.

eess.SP↗