arXiv subjects

Jiming Chen

Publications and source records attributed to Jiming Chen.

At least 19 recordsLinked to original sources

arXiv2026

On the Convergence Rate Lower Bound of Biochemical Computational Modules

Biochemical reaction networks have become a central theoretical framework for implementing molecular computation. A key challenge is finite time computational accuracy, as computation outputs are encoded in limiting steady states (LSSs) of species concentrations while practical implementations operate for only finite time. This work proposes a concise characterization of convergence rate for biochemical computational modules with multiple output species, and rigorously establishes it as being bounded by the eigenvalue with largest (least negative) real part of the Jacobian matrix. Two numerical examples illustrate how the theoretical lower bound shapes the convergence rate range and reveals its dependence on reaction rate constants. This formulation enables systematic evaluation of biochemical computation speed and provides a practical design measure for constructing high-accuracy and error-controlled biochemical computational modules.

math.OC↗

arXiv2026

ROEVO: Robust Organized Edge Feature-based Visual Odometry Using RGB-D Cameras

This work presents a visual odometry (VO) system that leverages image edge features. Edges are spatially expressive cues commonly present across diverse environments, offering rich textural and structural information. However, existing edge-based VO methods often fail to fully exploit this potential. To this end, we introduce a novel feature representation termed \textit{organized edges}, which transforms disjoint edge pixels into sequentialized clusters, enabling more effective retention and utilization of the underlying textural and structural information. Another nice property of this formulation is that organized edges can perform edge-level association across multiple frames, enabling the establishment of a co-visibility graph. To achieve precise and efficient pose estimation, we propose a range of particularly designed tracking and joint optimization methods based on the characteristics of organized edges. For tracking, we formulate edge-wise rather than pixel-wise residuals to achieve robust and accurate inter-frame registration. For joint optimization, we introduce a novel shape-preserving edge-fitting method and an organized edge-based Bundle Adjustment (BA) approach, which decomposes the traditional BA problem into fitting and registration to preserve the structural integrity. Based on these novel techniques, we develop a complete VO system that exclusively employs organized edge features, achieving efficient tracking and precise local mapping. Extensive experiments demonstrate its accuracy and robustness in indoor environments, outperforming or achieving comparable performance to state-of-the-art methods. The source code is publicly available at https://github.com/liumingrui814/ROEVO

cs.RO↗

arXiv2026

When Eavesdroppers Reason: Agentic Eavesdropping Attacks on Semantic Communication

Semantic communication (SemCom) has emerged as a promising paradigm for next-generation networks. However, its typical end-to-end joint source--channel coding (JSCC) architecture also raises serious privacy concerns. To guide future secure SemCom design, it is important to understand how serious such leakage can be. Nevertheless, existing eavesdropping attacks mainly rely on fixed-configuration solvers and often require instantaneous wiretap channel state information (CSI) to achieve effective privacy inference. This may lead future secure SemCom designs to overlook potentially severe risks. To address this, we propose a large language model (LLM)-orchestrated agentic eavesdropper. Specifically, the proposed eavesdropper forms a closed-loop workflow with three functional agents. The optimization agent adaptively performs joint semantic-and-channel inversion to recover private information from the intercepted signal without requiring wiretap CSI. The perception agent evaluates the effectiveness of the optimization agent and assesses whether the recovered private semantics are reasonable, providing feedback to the optimization agent. The refinement agent further analyzes the recovered content and uses a generative prior to refine promising candidates into more realistic and complete private reconstructions while preserving consistency with the intercepted signal. Simulation results over a MIMO Rayleigh fading channel show that the proposed eavesdropper achieves more than $75\%$ eavesdropping success rate at $\mathrm{SNR}\geq 5$~dB even without wiretap CSI, highlighting a severe privacy threat that future secure SemCom systems must address.

eess.SP↗

arXiv2026

Improving Local Feature Matching by Entropy-inspired Scale Adaptability and Flow-endowed Local Consistency

Recent semi-dense image matching methods have achieved remarkable success, but two long-standing issues still impair their performance. At the coarse stage, the over-exclusion issue of their mutual nearest neighbor (MNN) matching layer makes them struggle to handle cases with scale difference between images. To this end, we comprehensively revisit the matching mechanism and make a key observation that the hint concealed in the score matrix can be exploited to indicate the scale ratio. Based on this, we propose a scale-aware matching module which is exceptionally effective but introduces negligible overhead. At the fine stage, we point out that existing methods neglect the local consistency of final matches, which undermines their robustness. To this end, rather than independently predicting the correspondence for each source pixel, we reformulate the fine stage as a cascaded flow refinement problem and introduce a novel gradient loss to encourage local consistency of the flow field. Extensive experiments demonstrate that our novel matching pipeline, with these proposed modifications, achieves robust and accurate matching performance on downstream tasks.

cs.CV↗

arXiv2026

DexRepNet++: Learning Dexterous Robotic Manipulation with Geometric and Spatial Hand-Object Representations

Robotic dexterous manipulation is a challenging problem due to high degrees of freedom (DoFs) and complex contacts of multi-fingered robotic hands. Many existing deep reinforcement learning (DRL) based methods aim at improving sample efficiency in high-dimensional output action spaces. However, existing works often overlook the role of representations in achieving generalization of a manipulation policy in the complex input space during the hand-object interaction. In this paper, we propose DexRep, a novel hand-object interaction representation to capture object surface features and spatial relations between hands and objects for dexterous manipulation skill learning. Based on DexRep, policies are learned for three dexterous manipulation tasks, i.e. grasping, in-hand reorientation, bimanual handover, and extensive experiments are conducted to verify the effectiveness. In simulation, for grasping, the policy learned with 40 objects achieves a success rate of 87.9% on more than 5000 unseen objects of diverse categories, significantly surpassing existing work trained with thousands of objects; for the in-hand reorientation and handover tasks, the policies also boost the success rates and other metrics of existing hand-object representations by 20% to 40%. The grasp policies with DexRep are deployed to the real world under multi-camera and single-camera setups and demonstrate a small sim-to-real gap.

cs.RO↗

arXiv2026

Strategy-Supervised Autonomous Laparoscopic Camera Control via Event-Driven Graph Mining

Autonomous laparoscopic camera control must maintain a stable and safe surgical view under rapid tool-tissue interactions while remaining interpretable to surgeons. We present a strategy-grounded framework that couples high-level vision-language inference with low-level closed-loop control. Offline, raw surgical videos are parsed into camera-relevant temporal events (e.g., interaction, working-distance deviation, and view-quality degradation) and structured as attributed event graphs. Mining these graphs yields a compact set of reusable camera-handling strategy primitives, which provide structured supervision for learning. Online, a fine-tuned Vision-Language Model (VLM) processes the live laparoscopic view to predict the dominant strategy and discrete image-based motion commands, executed by an IBVS-RCM controller under strict safety constraints; optional speech input enables intuitive human-in-the-loop conditioning. On a surgeon-annotated dataset, event parsing achieves reliable temporal localization (F1-score 0.86), and the mined strategies show strong semantic alignment with expert interpretation (cluster purity 0.81). Extensive ex vivo experiments on silicone phantoms and porcine tissues demonstrate that the proposed system outperforms junior surgeons in standardized camera-handling evaluations, reducing field-of-view centering error by 35.26% and image shaking by 62.33%, while preserving smooth motion and stable working-distance regulation.

cs.RO↗

arXiv2026

LOONG: Online Time-Optimal Autonomous Flight for MAVs in Cluttered Environments

Autonomous flight of micro air vehicles (MAVs) in unknown, cluttered environments remains challenging for time-critical missions due to conservative maneuvering strategies. This article presents an integrated planning and control framework for high-speed, time-optimal autonomous flight of MAVs in cluttered environments. In each replanning cycle (100 Hz), a time-optimal trajectory under polynomial presentation is generated as a reference, with the time-allocation process accelerated by imitation learning. Subsequently, a time-optimal model predictive contouring control (MPCC) incorporates safe flight corridor (SFC) constraints at variable horizon steps to enable aggressive yet safe maneuvering, while fully exploiting the MAV's dynamics. We validate the proposed framework extensively on a custom-built LiDAR-based MAV platform. Simulation results demonstrate superior aggressiveness compared to the state of the art, while real-world experiments achieve a peak speed of 18 m/s in a cluttered environment and succeed in 10 consecutive trials from diverse start points. The video is available at the following link: https://youtu.be/vexXXhv99oQ.

cs.RO↗

arXiv2026

Rethinking Secure Semantic Communications in the Age of Generative and Agentic AI: Threats and Opportunities

Semantic communication (SemCom) improves communication efficiency by transmitting task-relevant information instead of raw bits and is expected to be a key technology for 6G networks. Recent advances in generative AI (GenAI) further enhance SemCom by enabling robust semantic encoding and decoding under limited channel conditions. However, these efficiency gains also introduce new security and privacy vulnerabilities. Due to the broadcast nature of wireless channels, eavesdroppers can also use powerful GenAI-based semantic decoders to recover private information from intercepted signals. Moreover, rapid advances in agentic AI enable eavesdroppers to perform long-term and adaptive inference through the integration of memory, external knowledge, and reasoning capabilities. This allows eavesdroppers to further infer user private behavior and intent beyond the transmitted content. Motivated by these emerging challenges, this paper comprehensively rethinks the security and privacy of SemCom systems in the age of generative and agentic AI. We first present a systematic taxonomy of eavesdropping threat models in SemCom systems. Then, we provide insights into how GenAI and agentic AI can enhance eavesdropping threats. Meanwhile, we also highlight potential opportunities for leveraging GenAI and agentic AI to design privacy-preserving SemCom systems.

eess.SP↗

arXiv2025

DeepGuard: Defending Deep Joint Source-Channel Coding Against Eavesdropping at Physical-Layer

Deep joint source-channel coding (DeepJSCC) has emerged as a promising paradigm for efficient and robust information transmission. However, its intrinsic characteristics also pose new security challenges, notably an increased vulnerability to eavesdropping attacks. Existing studies on defending against eavesdropping attacks in DeepJSCC, while demonstrating certain effectiveness, often incur considerable computational overhead or introduce performance trade-offs that may adversely affect legitimate users. In this paper, we present DeepGuard, to the best of our knowledge, the first physical-layer defense framework for DeepJSCC against eavesdropping attacks, validated through over-the-air experiments using software-defined radios (SDRs). Considering that existing eavesdropping attacks against DeepJSCC are limited to simulation under ideal channels, we take a step further by identifying and implementing four representative types of attacks under various configurations in orthogonal frequency-division multiplexing systems. These attacks are evaluated over-the-air under diverse scenarios, allowing us to comprehensively characterize the real-world threat landscape. To mitigate these threats, DeepGuard introduces a novel preamble perturbation mechanism that modifies the preamble shared only between legitimate transceivers. To realize it, we first conduct a theoretical analysis of the perturbation's impact on the signals intercepted by the eavesdropper. Building upon this, we develop an end-to-end perturbation optimization algorithm that significantly degrades eavesdropping performance while preserving reliable communication for legitimate users. We prototype DeepGuard using SDRs and conduct extensive over-the-air experiments in practical scenarios. Extensive experiments demonstrate that DeepGuard effectively mitigates eavesdropping threats.

eess.SP↗

arXiv2025

VICTOR: Dataset Copyright Auditing in Video Recognition Systems

Video recognition systems are increasingly being deployed in daily life, such as content recommendation and security monitoring. To enhance video recognition development, many institutions have released high-quality public datasets with open-source licenses for training advanced models. At the same time, these datasets are also susceptible to misuse and infringement. Dataset copyright auditing is an effective solution to identify such unauthorized use. However, existing dataset copyright solutions primarily focus on the image domain; the complex nature of video data leaves dataset copyright auditing in the video domain unexplored. Specifically, video data introduces an additional temporal dimension, which poses significant challenges to the effectiveness and stealthiness of existing methods. In this paper, we propose VICTOR, the first dataset copyright auditing approach for video recognition systems. We develop a general and stealthy sample modification strategy that enhances the output discrepancy of the target model. By modifying only a small proportion of samples (e.g., 1%), VICTOR amplifies the impact of published modified samples on the prediction behavior of the target models. Then, the difference in the model's behavior for published modified and unpublished original samples can serve as a key basis for dataset auditing. Extensive experiments on multiple models and datasets highlight the superiority of VICTOR. Finally, we show that VICTOR is robust in the presence of several perturbation mechanisms to the training videos or the target models.

cs.CR↗

arXiv2025

PrivATE: Differentially Private Average Treatment Effect Estimation for Observational Data

Causal inference plays a crucial role in scientific research across multiple disciplines. Estimating causal effects, particularly the average treatment effect (ATE), from observational data has garnered significant attention. However, computing the ATE from real-world observational data poses substantial privacy risks to users. Differential privacy, which offers strict theoretical guarantees, has emerged as a standard approach for privacy-preserving data analysis. However, existing differentially private ATE estimation works rely on specific assumptions, provide limited privacy protection, or fail to offer comprehensive information protection. To this end, we introduce PrivATE, a practical ATE estimation framework that ensures differential privacy. In fact, various scenarios require varying levels of privacy protection. For example, only test scores are generally sensitive information in education evaluation, while all types of medical record data are usually private. To accommodate different privacy requirements, we design two levels (i.e., label-level and sample-level) of privacy protection in PrivATE. By deriving an adaptive matching limit, PrivATE effectively balances noise-induced error and matching error, leading to a more accurate estimate of ATE. Our evaluation validates the effectiveness of PrivATE. PrivATE outperforms the baselines on all datasets and privacy budgets.

cs.CR↗

arXiv2025

Beyond Scaffold: A Unified Spatio-Temporal Gradient Tracking Method

In distributed and federated learning algorithms, communication overhead is often reduced by performing multiple local updates between communication rounds. However, due to data heterogeneity across nodes and the local gradient noise within each node, this strategy can lead to the drift of local models away from the global optimum. To address this issue, we revisit the well-known federated learning method Scaffold (Karimireddy et al., 2020) under a gradient tracking perspective, and propose a unified spatio-temporal gradient tracking algorithm, termed ST-GT, for distributed stochastic optimization over time-varying graphs. ST-GT tracks the global gradient across neighboring nodes to mitigate data heterogeneity, while maintaining a running average of local gradients to substantially suppress noise, with slightly more storage overhead. Without assuming bounded data heterogeneity, we prove that ST-GT attains a linear convergence rate for strongly convex problems and a sublinear rate for nonconvex cases. Notably, ST-GT achieves the first linear speed-up in communication complexity with respect to the number of local updates per round $\tau$ for the strongly-convex setting. Compared to traditional gradient tracking methods, ST-GT reduces the topology-dependent noise term from $\sigma^2$ to $\sigma^2/\tau$, where $\sigma^2$ denotes the noise level, thereby improving communication efficiency.

cs.LG↗

arXiv2025

SenseRay-3D: Generalizable and Physics-Informed Framework for End-to-End Indoor Propagation Modeling

Modeling indoor radio propagation is crucial for wireless network planning and optimization. However, existing approaches often rely on labor-intensive manual modeling of geometry and material properties, resulting in limited scalability and efficiency. To overcome these challenges, this paper presents SenseRay-3D, a generalizable and physics-informed end-to-end framework that predicts three-dimensional (3D) path-loss heatmaps directly from RGB-D scans, thereby eliminating the need for explicit geometry reconstruction or material annotation. The proposed framework builds a sensing-driven voxelized scene representation that jointly encodes occupancy, electromagnetic material characteristics, and transmitter-receiver geometry, which is processed by a SwinUNETR-based neural network to infer environmental path-loss relative to free-space path-loss. A comprehensive synthetic indoor propagation dataset is further developed to validate the framework and to serve as a standardized benchmark for future research. Experimental results show that SenseRay-3D achieves a mean absolute error of 4.27 dB on unseen environments and supports real-time inference at 217 ms per sample, demonstrating its scalability, efficiency, and physical consistency. SenseRay-3D paves a new path for sense-driven, generalizable, and physics-consistent modeling of indoor propagation, marking a major leap beyond our pioneering EM DeepRay framework.

cs.LG↗

arXiv2025

TokenCLIP: Token-wise Prompt Learning for Zero-shot Anomaly Detection

Adapting CLIP for anomaly detection on unseen objects has shown strong potential in a zero-shot manner. However, existing methods typically rely on a single textual space to align with visual semantics across diverse objects and domains. The indiscriminate alignment hinders the model from accurately capturing varied anomaly semantics. We propose TokenCLIP, a token-wise adaptation framework that enables dynamic alignment between visual and learnable textual spaces for fine-grained anomaly learning. Rather than mapping all visual tokens to a single, token-agnostic textual space, TokenCLIP aligns each token with a customized textual subspace that represents its visual characteristics. Explicitly assigning a unique learnable textual space to each token is computationally intractable and prone to insufficient optimization. We instead expand the token-agnostic textual space into a set of orthogonal subspaces, and then dynamically assign each token to a subspace combination guided by semantic affinity, which jointly supports customized and efficient token-wise adaptation. To this end, we formulate dynamic alignment as an optimal transport problem, where all visual tokens in an image are transported to textual subspaces based on semantic similarity. The transport constraints of OT ensure sufficient optimization across subspaces and encourage them to focus on different semantics. Solving the problem yields a transport plan that adaptively assigns each token to semantically relevant subspaces. A top-k masking is then applied to sparsify the plan and specialize subspaces for distinct visual regions. Extensive experiments demonstrate the superiority of TokenCLIP.

cs.CV↗

arXiv2025

Cerberus: Real-Time Video Anomaly Detection via Cascaded Vision-Language Models

Video anomaly detection (VAD) has rapidly advanced by recent development of Vision-Language Models (VLMs). While these models offer superior zero-shot detection capabilities, their immense computational cost and unstable visual grounding performance hinder real-time deployment. To overcome these challenges, we introduce Cerberus, a two-stage cascaded system designed for efficient yet accurate real-time VAD. Cerberus learns normal behavioral rules offline, and combines lightweight filtering with fine-grained VLM reasoning during online inference. The performance gains of Cerberus come from two key innovations: motion mask prompting and rule-based deviation detection. The former directs the VLM's attention to regions relevant to motion, while the latter identifies anomalies as deviations from learned norms rather than enumerating possible anomalies. Extensive evaluations on four datasets show that Cerberus on average achieves 57.68 fps on an NVIDIA L40S GPU, a 151.79$\times$ speedup, and 97.2\% accuracy comparable to the state-of-the-art VLM-based VAD methods, establishing it as a practical solution for real-time video analytics.

cs.CV↗

arXiv2025

Orientation Learning and Adaptation towards Simultaneous Incorporation of Multiple Local Constraints

Orientation learning plays a pivotal role in many tasks. However, the rotation group SO(3) is a Riemannian manifold. As a result, the distortion caused by non-Euclidean geometric nature introduces difficulties to the incorporation of local constraints, especially for the simultaneous incorporation of multiple local constraints. To address this issue, we propose the Angle-Axis Space-based orientation representation method to solve several orientation learning problems, including orientation adaptation and minimization of angular acceleration. Specifically, we propose a weighted average mechanism in SO(3) based on the angle-axis representation method. Our main idea is to generate multiple trajectories by considering different local constraints at different basepoints. Then these multiple trajectories are fused to generate a smooth trajectory by our proposed weighted average mechanism, achieving the goal to incorporate multiple local constraints simultaneously. Compared with existing solution, ours can address the distortion issue and make the off-theshelf Euclidean learning algorithm be re-applicable in non-Euclidean space. Simulation and Experimental evaluations validate that our solution can not only adapt orientations towards arbitrary desired via-points and cope with angular acceleration constraints, but also incorporate multiple local constraints simultaneously to achieve extra benefits, e.g., achieving smaller acceleration costs.

cs.RO↗

arXiv2025

Pareto-optimal Trade-offs Between Communication and Computation with Flexible Gradient Tracking

This paper addresses distributed stochastic optimization problems under non-i.i.d. data, focusing on the inherent trade-offs between communication and computational efficiency. To this end, we propose FlexGT, a flexible snapshot gradient tracking method that enables tunable numbers of local updates and neighbor communications per round, thereby adapting efficiently to diverse system resource conditions. Leveraging a unified convergence analysis framework, we derive tight communication and computational complexity for FlexGT with explicit dependence on objective properties and certain tunable parameters. Moreover, we introduce an accelerated variant, termed Acc-FlexGT, and prove that, with prior knowledge of the graph, it achieves Pareto-optimal trade-offs between communication and computation. Particularly, in the nonconvex case, Acc-FlexGT achieves the optimal iteration complexity of $\tilde{\mathcal{O}}\left( \left( L\sigma ^2 \right) /\left( n\epsilon ^2 \right) +L/\left( \epsilon \sqrt{1-\sqrt{\rho _W}} \right) \right) $ and optimal communication complexity of $\tilde{\mathcal{O}}\left( L/\left( \epsilon \sqrt{1-\sqrt{\rho _W}} \right) \right)$ for appropriately chosen numbers of local updates, matching existing lower bounds up to logarithmic factors. And, it improves the existing results for the strongly convex case by a factor of $\tilde{\mathcal{O}} \left( 1/\sqrt{\epsilon} \right)$, where $\epsilon$ is the targeted accuracy, $n$ the number of nodes, $L$ the Lipschitz constant, $\rho_W$ the connectivity of the graph, and $\sigma$ the stochastic gradient variance. Numerical experiments corroborate the theoretical results and demonstrate the effectiveness of the proposed methods.

math.OC↗

arXiv2025

DeepStream: Prototyping Deep Joint Source-Channel Coding for Real-Time Multimedia Transmissions

Deep learning-based joint source-channel coding (DeepJSCC) has emerged as a promising technique in 6G for enhancing the efficiency and reliability of data transmission across diverse modalities, particularly in low signal-to-noise ratio (SNR) environments. This advantage is realized by leveraging powerful neural networks to learn an optimal end-to-end mapping from the source data directly to the transmit symbol sequence, eliminating the need for separate source coding, channel coding, and modulation. Although numerous efforts have been made towards efficient DeepJSCC, they have largely stayed at numerical simulations that can be far from practice, leaving the real-world viability of DeepJSCC largely unverified. To this end, we prototype DeepStream upon orthogonal frequency division multiplexing (OFDM) technology to offer efficient and robust DeepJSCC for multimedia transmission. In conforming to OFDM, we develop both a feature-to-symbol mapping method and a cross-subcarrier precoding method to improve the subcarrier independence and reduce peak-to-average power ratio. To reduce system complexity and enable flexibility in accommodating varying quality of service requirements, we further propose a progressive coding strategy that adjusts the compression ratio based on latency with minimal performance loss. We implement DeepStream for real-time image transmission and video streaming using software-defined radio. Extensive evaluations verify that DeepStream outperforms both the standard scheme and the direct deployment scheme. Particularly, at an SNR of 10 dB, DeepStream achieves a PSNR of 35 dB for image transmission and an MS-SSIM of 20 dB for video streaming, whereas the standard scheme fails to recover meaningful information.

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