arXiv subjects

Zhiqiang Zuo

Publications and source records attributed to Zhiqiang Zuo.

18 recordsLinked to original sources

arXiv2026

Boundary-Aligned Contribution Routing for Robust Optical--SAR Object Detection

Optical imagery provides rich appearance cues, whereas synthetic aperture radar (SAR) offers observations that are less sensitive to illumination and weather, making optical--SAR fusion attractive for remote-sensing object detection. However, the presence of multiple modalities does not guarantee beneficial fusion: imperfect spatial, temporal, and semantic correspondence can make an otherwise intact stream conditionally harmful and induce negative cross-modal transfer. We handle this issue through a model-specific task-utility perspective and learn task-conditioned contribution routing using detection supervision alone. The proposed fusion-boundary-aligned routing regulates each modality's contribution before the first learned cross-modal feature-value mixing operation. For architectures with frequent shallow interaction, a Feature Router performs cross-conditioned, group-addressable modulation near the input; for dual-backbone architectures, a Dual-Statistic Semantic Router predicts stream-level contribution weights from modality-specific average and maximum statistics before late semantic fusion. The routers require no explicit utility supervision, quality labels, reconstruction, or distillation. Experiments on M4-SAR and SpaceNet6-OTD cover nominal full inputs, controlled correspondence shifts, missing modalities, and four nonzero modality-corruption scenarios. Across the reported clean-training controls, routing improves full-input $\text{mAP}_{50}$ by 0.5--5.9 points. Relative to the corresponding modality-dropout baselines, it raises missing-modality $\text{mAP}_{50}$ by 7.6--41.6 points and reduces the negative-transfer rate by up to 12.7 percentage points. Spearman correlations between the learned routing weights and model-specific leave-one-modality-out utility range from 0.45 to 0.66, supporting the task-utility interpretation of the routing coefficients.

cs.CV↗

arXiv2026

DeGuNet: Depth-Guided Ultra-Compact Backbones for Efficient LiDAR-Camera 3D Detection

In autonomous driving perception, the fusion of LiDAR and camera modalities has become the dominant paradigm for 3D object detection. However, current multi-modal frameworks heavily rely on massive visual backbones pretrained on 2D semantic tasks. This reliance introduces substantial parameter redundancy and a structural misalignment, as 2D priors are ill-equipped to handle the extreme sparsity of LiDAR projections required for Bird's-Eye-View geometry. To address this, we present DeGuNet, an ultra-compact and plug-and-play image backbone explicitly designed for depth-guided representation learning. By incorporating sparsity-aware feature extraction mechanisms, DeGuNet effectively aligns multi-view images with unstructured LiDAR depth while strictly preventing invalid-region contamination. Extensive experiments on the nuScenes dataset demonstrate DeGuNet's broad plug-and-play applicability and superior efficiency. When integrated into established baselines, it fundamentally eliminates architectural redundancy, reducing GPU memory consumption by up to 66.5% and achieving a 1.16x inference speedup. Concurrently, DeGuNet delivers up to a 6.20 absolute mAP gain, establishing a new paradigm for parameter-efficient multi-modal 3D perception.

cs.CV↗

arXiv2026

Can BEV Perception Gracefully Degrade under Sensor Failures?

Despite the remarkable success of multi-modal bird's-eye view (BEV) perception in autonomous driving, current systems exhibit a critical vulnerability: existing fusion mechanisms are highly brittle to sensor corruptions, often causing catastrophic performance degradation. This vulnerability largely stems from the fact that standard fusion frameworks typically integrate multi-modal representations in a static manner, leading to a precipitous performance collapse under missing or corrupted modalities. In contrast, we show that graceful degradation is achievable through active modality reliability assessment. To this end, we present Grace-BEV, a lightweight and plug-and-play framework that enforces active reliability awareness during multi-modal fusion. Instead of relying on computationally expensive cross-modal interactions, Grace-BEV leverages the aligned BEV space to explicitly assess modality trustworthiness via a TrustGate Router and dynamically recalibrate feature integration using the FailSafe Fusion Block. Furthermore, we devise a Three-Phase Training strategy with Modality Dropout to prevent modality dominance and encourage balanced cross-modal learning under unreliable inputs. Extensive experiments on nuScenes-R and nuScenes-C show that Grace-BEV maintains robust performance across diverse corruption settings. Notably, under catastrophic LiDAR failures where standard baselines collapse to 0.0% mean Average Precision (mAP), Grace-BEV restores performance to as high as 34.7% mAP. Moreover, it improves clean accuracy by up to 1.4%, achieving a strong trade-off between robustness and efficiency.

cs.CV↗

arXiv2026

Dual-Interaction-Aware Cooperative Control Strategy for Alleviating Mixed Traffic Congestion

As Intelligent Transportation System (ITS) develops, Connected and Automated Vehicles (CAVs) are expected to significantly reduce traffic congestion through cooperative strategies, such as in bottleneck areas. However, the uncertainty and diversity in the behaviors of Human-Driven Vehicles (HDVs) in mixed traffic environments present major challenges for CAV cooperation. This paper proposes a Dual-Interaction-Aware Cooperative Control (DIACC) strategy that enhances both local and global interaction perception within the Multi-Agent Reinforcement Learning (MARL) framework for Connected and Automated Vehicles (CAVs) in mixed traffic bottleneck scenarios. The DIACC strategy consists of three key innovations: 1) A Decentralized Interaction-Adaptive Decision-Making (D-IADM) module that enhances actor's local interaction perception by distinguishing CAV-CAV cooperative interactions from CAV-HDV observational interactions. 2) A Centralized Interaction-Enhanced Critic (C-IEC) that improves critic's global traffic understanding through interaction-aware value estimation, providing more accurate guidance for policy updates. 3) A reward design that employs softmin aggregation with temperature annealing to prioritize interaction-intensive scenarios in mixed traffic. Additionally, a lightweight Proactive Safety-based Action Refinement (PSAR) module applies rule-based corrections to accelerate training convergence. Experimental results demonstrate that DIACC significantly improves traffic efficiency and adaptability compared to rule-based and benchmark MARL models.

eess.SY↗

arXiv2026

Boosting Maximum Entropy Reinforcement Learning via One-Step Flow Matching

Diffusion policies are expressive yet incur high inference latency. Flow Matching (FM) enables one-step generation, but integrating it into Maximum Entropy Reinforcement Learning (MaxEnt RL) is challenging: the optimal policy is an intractable energy-based distribution, and the efficient log-likelihood estimation required to balance exploration and exploitation suffers from severe discretization bias. We propose \textbf{F}low-based \textbf{L}og-likelihood-\textbf{A}ware \textbf{M}aximum \textbf{E}ntropy RL (\textbf{FLAME}), a principled framework that addresses these challenges. First, we derive a Q-Reweighted FM objective that bypasses partition function estimation via importance reweighting. Second, we design a decoupled entropy estimator that rigorously corrects bias, which enables efficient exploration and brings the policy closer to the optimal MaxEnt policy. Third, we integrate the MeanFlow formulation to achieve expressive and efficient one-step control. Empirical results on MuJoCo show that FLAME outperforms Gaussian baselines and matches multi-step diffusion policies with significantly lower inference cost. Code is available at https://github.com/lzqw/FLAME.

cs.LG↗

arXiv2025

Active Secure Neighbor Selection in Multi-Agent Systems with Byzantine Attacks

This paper investigates the problem of resilient control for multi-agent systems in the presence of Byzantine adversaries via an active secure neighbor selection framework. A pre-discriminative graph is first constructed to characterize the admissible set of candidate neighbors for each agent. Based on this graph, a dynamic in-neighbor selection strategy is proposed, wherein each agent actively selects a subset of its pre-discriminative neighbors. The number of selected neighbors is adjustable, allowing for a trade-off between communication overhead and robustness, with the minimal case requiring only a single in-neighbor. The proposed strategy facilitates the reconstruction of a directed spanning tree among normal agents following the detection and isolation of Byzantine agents. It achieves resilient consensus without imposing any assumptions on the initial connectivity among normal agents. Moreover, the approach significantly reduces communication burden while maintaining resilience to adversarial behavior. A numerical example is provided to illustrate the effectiveness of the proposed method.

eess.SY↗

arXiv2025

Beyond the Expiry Date: Uncovering Hidden Value in Functional Drink Waste for a Circular Future

Expired functional drinks have great valorisation potential due to the high concentration of organic molecules present. However, detailed information of the resources in these expired functional drinks is limited, hindering the rational design of a recovery system. To address this gap, we present here a study that comprehensively characterises the chemical composition of functional drinks and discus their potential use as feedstocks for biomethane production. The example functional drinks were abundant in sugars, organic acids, and amino acids, and were especially rich in glucose, fructose, and alanine. Our studies revealed that functional drinks with high COD values that corresponded to high proportions of sugar and organic acid and low proportions of sorbitol and amino acids could realise profitable recovery through anaerobic digestion, with a minimum biomethane yield of 11.72 mL CH4 / mL drink. To assess utility further we also examined the dynamic composition of functional drinks up to 16 weeks (at 4 °C) after expiration to capture the shift in resources during deterioration. In doing so, we identified 4 distinct periods of carbon resource variation: 1) chemically stable period, 2) sorbitol degradation period, 3) sugar degradation period, and 4) acidification period. Based on the time-course biomethane production experiments for expired functional drinks, the optimal operating time window for biomethane production from drinks without ascorbic acid would be after sorbitol degradation period in terms of its economic performance through convenient natural deterioration. Therefore, this comprehensive study on dynamic chemical composition in expired functional drinks and their biomethane production potential could facilitate a rational design of resource recovery system for soft drink field.

eess.SY↗

arXiv2025

Resilient Event-Triggered Control of Vehicle Platoon Under DoS Attacks and Parameter Uncertainty

This paper investigates the problem of dynamic event-triggered platoon control for intelligent vehicles (IVs) under denial of service (DoS) attacks and parameter uncertainty. DoS attacks disrupt vehicle-to-vehicle (V2V) communications, leading to the destabilization of vehicle formations. To alleviate the burden of the V2V communication network and enhance the tracking performance in the presence of DoS attacks and parameter uncertainty, a resilient and dynamic event-triggered mechanism is proposed. In contrast to the static event-triggering mechanism (STEM), this approach leverages the internal dynamic variable to further save communication resources. Subsequently, a method is developed for designing the desired triggering mechanism. Following this, a co-design framework is constructed to guarantee robust and resilient control against DoS attacks, with the analysis of eliminating Zeno behavior. Lastly, extensive simulations are presented to show the superiority of the proposed method in terms of enhancing platoon resilience and robustness and improving communication efficiency.

eess.SY↗

arXiv2025

Resilient Control for Networked Switched Systems With/Without ACK: An Active Quantized Framework

This paper deals with the quantized control problem for switched systems under denial-of-service (DoS) attack. Considering the system's defensive capability and the computational resources of quantizers and controllers, four control strategies are proposed. These strategies incorporate different combinations of controllers (active and passive), quantizers (centered on the origin or custom-designed), and network configurations (with or without ACK signals). For each strategy, specific update laws for the encoder and decoder are designed to avoid quantization saturation. Furthermore, the uniformity of encoder and decoder operations is maintained by transmitting additional information to the decoder. To achieve asymptotic stability, sufficient conditions concerning the switching signal and DoS attack constraints are derived by taking into account the asynchronous behaviors. The proposed active quantization strategy with the ACK signal leverages the system model information to compute the control signal in real-time, allowing for possible convergence of the system state despite DoS attack. Additionally, a well-designed switching signal is suggested to further mitigate the impact of DoS attack. A passive quantization strategy with ACK signal is also developed as a simplified version of the active quantized control strategy, providing the foundation for a strategy without ACK signal. Inspired by time-triggered and event-triggered mechanisms, the passive quantization strategy without ACK signal is investigated, with two feasible update laws for the quantizer. Finally, two simulations are conducted to validate the effectiveness of the proposed strategies.

eess.SY↗

arXiv2025

A Unified Attack Detection Strategy for Multi-Agent Systems over Transient and Steady Stages

This paper proposes a unified detection strategy against three kinds of attacks for multi-agent systems (MASs) which is applicable to both transient and steady stages. For attacks on the communication layer, a watermarking-based detection scheme with KullbackLeibler (KL) divergence is designed. Different from traditional communication schemes, each agent transmits a message set containing two state values with different types of watermarking. It is found that the detection performance is determined by the relevant parameters of the watermarking signal. Unlike the existing detection manoeuvres, such a scheme is capable of transient and steady stages. For attacks on the agent layer, a convergence rate related detection approach is put forward. It is shown that the resilience of the considered system is characterized by the coefficient and offset of the envelope. For hybrid attacks, based on the above detection mechanisms, a general framework resorting to trusted agents is presented, which requires weaker graph conditions and less information transmission. Finally, an example associated with the platooning of connected vehicles is given to support the theoretical results.

eess.SY↗

arXiv2024

HardRace: A Dynamic Data Race Monitor for Production Use

Data races are critical issues in multithreaded program, leading to unpredictable, catastrophic and difficult-to-diagnose problems. Despite the extensive in-house testing, data races often escape to deployed software and manifest in production runs. Existing approaches suffer from either prohibitively high runtime overhead or incomplete detection capability. In this paper, we introduce HardRace, a data race monitor to detect races on-the-fly while with sufficiently low runtime overhead and high detection capability. HardRace firstly employs sound static analysis to determine a minimal set of essential memory accesses relevant to data races. It then leverages hardware trace instruction, i.e., Intel PTWRITE, to selectively record only these memory accesses and thread synchronization events during execution with negligible runtime overhead. Given the tracing data, HardRace performs standard data race detection algorithms to timely report potential races occurred in production runs. The experimental evaluations show that HardRace outperforms state-of-the-art tools like ProRace and Kard in terms of both runtime overhead and detection capability -- HardRace can detect all kinds of data races in read-world applications while maintaining a negligible overhead, less than 2% on average.

cs.SE↗

arXiv2024

Scaling Inter-procedural Dataflow Analysis on the Cloud

Apart from forming the backbone of compiler optimization, static dataflow analysis has been widely applied in a vast variety of applications, such as bug detection, privacy analysis, program comprehension, etc. Despite its importance, performing interprocedural dataflow analysis on large-scale programs is well known to be challenging. In this paper, we propose a novel distributed analysis framework supporting the general interprocedural dataflow analysis. Inspired by large-scale graph processing, we devise dedicated distributed worklist algorithms for both whole-program analysis and incremental analysis. We implement these algorithms and develop a distributed framework called BigDataflow running on a large-scale cluster. The experimental results validate the promising performance of BigDataflow -- BigDataflow can finish analyzing the program of millions lines of code in minutes. Compared with the state-of-the-art, BigDataflow achieves much more analysis efficiency.

cs.PL↗

arXiv2024

Resilient control of networked switched systems subject to deception attack and DoS attack

In this paper, the resilient control for switched systems in the presence of deception attack and denial-of-service (DoS) attack is addressed. Due to the interaction of two kinds of attacks and the asynchronous phenomenon of controller mode and subsystem mode, the system dynamics becomes much more complex. A criterion is derived to ensure the mean square security level of the closed-loop system. This in turn reveals the balance of system resilience and control performance. Furthermore, a mixed-switching control strategy is put forward to make the system globally asymptotically stable. It is shown that the system will still converge to the equilibrium even if the deception attack occurs. Finally, simulations are carried out to verify the effectiveness of the theoretical results.

eess.SY↗

arXiv2024

HardTaint: Production-Run Dynamic Taint Analysis via Selective Hardware Tracing

Dynamic taint analysis (DTA), as a fundamental analysis technique, is widely used in security, privacy, and diagnosis, etc. As DTA demands to collect and analyze massive taint data online, it suffers extremely high runtime overhead. Over the past decades, numerous attempts have been made to lower the overhead of DTA. Unfortunately, the reductions they achieved are marginal, causing DTA only applicable to the debugging/testing scenarios. In this paper, we propose and implement HardTaint, a system that can realize production-run dynamic taint tracking. HardTaint adopts a hybrid and systematic design which combines static analysis, selective hardware tracing and parallel graph processing techniques. The comprehensive evaluations demonstrate that HardTaint introduces only around 9% runtime overhead which is an order of magnitude lower than the state-of-the-arts, while without sacrificing any taint detection capability.

cs.CR↗

arXiv2022

Robust output regulation of linear system subject to modeled and unmodeled uncertainty

In this paper, a novel robust output regulation control framework is proposed for the system subject to noise, modeled disturbance and unmodeled disturbance to seek tracking performance and robustness simultaneously. The output regulation scheme is utilized in the framework to track the reference in the presence of modeled disturbance, and the effect of unmodeled disturbance is reduced by an $\mathcal{H}_\infty$ compensator. The Kalman filter can be also introduced in the stabilization loop to deal with the white noise. Furthermore, the tracking error in the presence/absence of noise and disturbance is estimated. The effectiveness and performance of our proposed control framework is verified in the numerical example by applying in the Furuta Inverted Pendulum system.

eess.SY↗

arXiv2022

RAPO: An Adaptive Ranking Paradigm for Bilingual Lexicon Induction

Bilingual lexicon induction induces the word translations by aligning independently trained word embeddings in two languages. Existing approaches generally focus on minimizing the distances between words in the aligned pairs, while suffering from low discriminative capability to distinguish the relative orders between positive and negative candidates. In addition, the mapping function is globally shared by all words, whose performance might be hindered by the deviations in the distributions of different languages. In this work, we propose a novel ranking-oriented induction model RAPO to learn personalized mapping function for each word. RAPO is capable of enjoying the merits from the unique characteristics of a single word and the cross-language isomorphism simultaneously. Extensive experimental results on public datasets including both rich-resource and low-resource languages demonstrate the superiority of our proposal. Our code is publicly available in \url{https://github.com/Jlfj345wf/RAPO}.

cs.CL↗

arXiv2021

A Two-Functional-Network Framework of Opinion Dynamics

A common trait involving the opinion dynamics in social networks is an anchor on interacting network to characterize the opinion formation process among participating social actors, such as information flow, cooperative and antagonistic influence, etc. Nevertheless, interacting networks are generally public for social groups, as well as other individuals who may be interested in. This blocks a more precise interpretation of the opinion formation process since social actors always have complex feeling, motivation and behavior, even beliefs that are personally private. In this paper, we formulate a general configuration on describing how individual's opinion evolves in a distinct fashion. It consists of two functional networks: interacting network and appraisal network. Interacting network inherits the operational properties as DeGroot iterative opinion pooling scheme while appraisal network, forming a belief system, quantifies certain cognitive orientation to interested individuals' beliefs, over which the adhered attitudes may have the potential to be antagonistic. We explicitly show that cooperative appraisal network always leads to consensus in opinions. Antagonistic appraisal network, however, causes opinion cluster. It is verified that antagonistic appraisal network affords to guarantee consensus by imposing some extra restrictions. They hence bridge a gap between the consensus and the clusters in opinion dynamics. We further attain a gauge on the appraisal network by means of the random convex optimization approach. Moreover, we extend our results to the case of mutually interdependent issues.

eess.SY↗

arXiv2020

SpecuSym: Speculative Symbolic Execution for Cache Timing Leak Detection

CPU cache is a limited but crucial storage component in modern processors, whereas the cache timing side-channel may inadvertently leak information through the physically measurable timing variance. Speculative execution, an essential processor optimization, and a source of such variances, can cause severe detriment on deliberate branch mispredictions. Despite static analysis could qualitatively verify the timing-leakage-free property under speculative execution, it is incapable of producing endorsements including inputs and speculated flows to diagnose leaks in depth. This work proposes a new symbolic execution based method, SpecuSym, for precisely detecting cache timing leaks introduced by speculative execution. Given a program (leakage-free in non-speculative execution), SpecuSymsystematically explores the program state space, models speculative behavior at conditional branches, and accumulates the cache side effects along with subsequent path explorations. During the dynamic execution, SpecuSymconstructs leak predicates for memory visits according to the specified cache model and conducts a constraint-solving based cache behavior analysis to inspect the new cache behaviors. We have implementedSpecuSymatop KLEE and evaluated it against 15 open-source benchmarks. Experimental results show thatSpecuSymsuccessfully detected from 2 to 61 leaks in 6 programs under 3 different cache settings and identified false positives in 2 programs reported by recent work.

cs.CR↗