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Researcher profile

Mu Zhou

Mu Zhou contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Computer Visioneess.IVMachine LearningArtificial IntelligenceCryptography and Securityeess.SP

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Published work

7 published item(s)

preprint2026arXiv

A Privacy-Preserving Localization Scheme with Node Selection in Mobile Networks

Localization in mobile networks has been widely applied in many scenarios. However, an entity responsible for location estimation exposes both the target and anchors to potential location leakage at any time, creating serious security risks. Although existing studies have proposed privacy-preserving localization algorithms, they still face challenges of insufficient positioning accuracy and excessive communication overhead. In this article, we propose a privacy-preserving localization scheme, named PPLZN. PPLZN protects protects the location privacy of both the target and anchor nodes in crowdsourced localization. Simulation results validate the effectiveness of PPLZN. Evidently, it can achieve accurate position estimation without location leakage and outperform state-of-the-art approaches in both positioning accuracy and communication overhead. In addition, PPLZN significantly reduces computational and communication overhead in large-scale deployments, making it well-fitted for practical privacy-preserving localization in resource-constrained networks.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Evidence Over Plans: Online Trajectory Verification for Skill Distillation

Agent skills can remarkably improve task success rates by using human-written procedural documents, but their quality is difficult to assess without environment-grounded verification. Existing skill generation methods heavily rely on preference logs rather than direct environment interaction, often yielding negligible or even degraded gains. We identify that it is a fundamental timing bottleneck: robust skills should be posterior-based, distilled from empirical environment interaction rather than prior plans. In this study, we introduce the Posterior Distillation Index (PDI), a trajectory-level metric that quantifies how well a distilled skill is grounded in the task-environment evidence. To operationalize PDI, we present SPARK (Structured Pipelines for Autonomous Runnable tasKs and sKill generation) for preserving task execution evidence towards full trajectory-level analysis. SPARK generates environment-verified trajectories used to compute PDI, and it applies PDI as an online diagnostic and intervention signal to ensure posterior skill formation. Across 86 runnable tasks, SPARK-generated skills consistently surpass no-skill baselines and outperform human-written skills on student models (inference cost up to 1,000x cheaper than teacher models). These findings show that PDI-guided distillation produces efficient and transferable skills grounded in the task-environment interaction. We release our code at https://github.com/EtaYang10th/spark-skills .

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Acceleration Estimation of Signal Propagation Path Length Changes for Wireless Sensing

As indoor applications grow in diversity, wireless sensing, vital in areas like localization and activity recognition, is attracting renewed interest. Indoor wireless sensing relies on signal processing, particularly channel state information (CSI) based signal parameter estimation. Nonetheless, regarding reflected signals induced by dynamic human targets, no satisfactory algorithm yet exists for estimating the acceleration of dynamic path length change (DPLC), which is crucial for various sensing tasks in this context. Hence, this paper proposes DP-AcE, a CSI-based DPLC acceleration estimation algorithm. We first model the relationship between the phase difference of adjacent CSI measurements and the DPLC's acceleration. Unlike existing works assuming constant velocity, DP-AcE considers both velocity and acceleration, yielding a more accurate and objective representation. Using this relationship, an algorithm combining scaling with Fourier transform is proposed to realize acceleration estimation. We evaluate DP-AcE via the acceleration estimation and acceleration-based fall detection with the collected CSI. Experimental results reveal that, using distance as the metric, DP-AcE achieves a median acceleration estimation percentage error of 4.38%. Furthermore, in multi-target scenarios, the fall detection achieves an average true positive rate of 89.56% and a false positive rate of 11.78%, demonstrating its importance in enhancing indoor wireless sensing capabilities.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

DeepRecon: Joint 2D Cardiac Segmentation and 3D Volume Reconstruction via A Structure-Specific Generative Method

Joint 2D cardiac segmentation and 3D volume reconstruction are fundamental to building statistical cardiac anatomy models and understanding functional mechanisms from motion patterns. However, due to the low through-plane resolution of cine MR and high inter-subject variance, accurately segmenting cardiac images and reconstructing the 3D volume are challenging. In this study, we propose an end-to-end latent-space-based framework, DeepRecon, that generates multiple clinically essential outcomes, including accurate image segmentation, synthetic high-resolution 3D image, and 3D reconstructed volume. Our method identifies the optimal latent representation of the cine image that contains accurate semantic information for cardiac structures. In particular, our model jointly generates synthetic images with accurate semantic information and segmentation of the cardiac structures using the optimal latent representation. We further explore downstream applications of 3D shape reconstruction and 4D motion pattern adaptation by the different latent-space manipulation strategies.The simultaneously generated high-resolution images present a high interpretable value to assess the cardiac shape and motion.Experimental results demonstrate the effectiveness of our approach on multiple fronts including 2D segmentation, 3D reconstruction, downstream 4D motion pattern adaption performance.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Graph Convolutional Networks for Multi-modality Medical Imaging: Methods, Architectures, and Clinical Applications

Image-based characterization and disease understanding involve integrative analysis of morphological, spatial, and topological information across biological scales. The development of graph convolutional networks (GCNs) has created the opportunity to address this information complexity via graph-driven architectures, since GCNs can perform feature aggregation, interaction, and reasoning with remarkable flexibility and efficiency. These GCNs capabilities have spawned a new wave of research in medical imaging analysis with the overarching goal of improving quantitative disease understanding, monitoring, and diagnosis. Yet daunting challenges remain for designing the important image-to-graph transformation for multi-modality medical imaging and gaining insights into model interpretation and enhanced clinical decision support. In this review, we present recent GCNs developments in the context of medical image analysis including imaging data from radiology and histopathology. We discuss the fast-growing use of graph network architectures in medical image analysis to improve disease diagnosis and patient outcomes in clinical practice. To foster cross-disciplinary research, we present GCNs technical advancements, emerging medical applications, identify common challenges in the use of image-based GCNs and their extensions in model interpretation, large-scale benchmarks that promise to transform the scope of medical image studies and related graph-driven medical research.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

TransFusion: Multi-view Divergent Fusion for Medical Image Segmentation with Transformers

Combining information from multi-view images is crucial to improve the performance and robustness of automated methods for disease diagnosis. However, due to the non-alignment characteristics of multi-view images, building correlation and data fusion across views largely remain an open problem. In this study, we present TransFusion, a Transformer-based architecture to merge divergent multi-view imaging information using convolutional layers and powerful attention mechanisms. In particular, the Divergent Fusion Attention (DiFA) module is proposed for rich cross-view context modeling and semantic dependency mining, addressing the critical issue of capturing long-range correlations between unaligned data from different image views. We further propose the Multi-Scale Attention (MSA) to collect global correspondence of multi-scale feature representations. We evaluate TransFusion on the Multi-Disease, Multi-View \& Multi-Center Right Ventricular Segmentation in Cardiac MRI (M\&Ms-2) challenge cohort. TransFusion demonstrates leading performance against the state-of-the-art methods and opens up new perspectives for multi-view imaging integration towards robust medical image segmentation.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Light-weight Calibrator: a Separable Component for Unsupervised Domain Adaptation

Existing domain adaptation methods aim at learning features that can be generalized among domains. These methods commonly require to update source classifier to adapt to the target domain and do not properly handle the trade off between the source domain and the target domain. In this work, instead of training a classifier to adapt to the target domain, we use a separable component called data calibrator to help the fixed source classifier recover discrimination power in the target domain, while preserving the source domain's performance. When the difference between two domains is small, the source classifier's representation is sufficient to perform well in the target domain and outperforms GAN-based methods in digits. Otherwise, the proposed method can leverage synthetic images generated by GANs to boost performance and achieve state-of-the-art performance in digits datasets and driving scene semantic segmentation. Our method empirically reveals that certain intriguing hints, which can be mitigated by adversarial attack to domain discriminators, are one of the sources for performance degradation under the domain shift.

0 citations0 reviewsNo code linkedOpen access