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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yulin Chen Qian Huang Zhijian Wang Ziyang Yin Meng Geng Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Nucleus segmentation plays a vital role in medical image analysis. However, existing segmentation methods frequently encounter hurdles, such as the loss of crucial image details during downsampling and issues like noise and spatial displacement. In this study, we propose NucleiFormer, where Haar wavelet transforms are employed in the encoder to replace conventional downsampling techniques. Additionally, we utilize an adaptive feature calibration module to align and calibrate features of different scales, reduce feature redundancy, suppress noise, and enhance the spatial awareness of the model. Extensive experiments validate our model’s superior segmentation performance in accurately delineating nucleus boundaries and minimizing errors.

关键词： Wavelet transforms Adaptation models Image segmentation Image analysis Noise Termination of employment Calibration Speech processing Optimization Biomedical imaging

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Super-Resolution of Diffusion-Weighted Images via TDI-Conditioned Diffusion Model 15th

Super-Resolution of Diffusion-Weighted Images via TDI-Cond...

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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024

作者： Ma, Jiquan Teng, Yujun Chen, Geng Jiang, Haotian Zhang, Kai Liu, Feihong Rekik, Islem Shen, Dinggang School of Computer Science and Technology Heilongjiang University Harbin China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai China School of Information Science and Technology Northwest University Xi’an China BASIRA Lab Imperial-X and Department of Computing Imperial College London London United Kingdom

ISBN: (纸本)9783031869198

Diffusion-Weighted Imaging (DWI) is a significant technique for studying white matter. However, it suffers from low-resolution obstacles in clinical settings. Post-acquisition Super-Resolution (SR) can enhance the resolution of DWIs and has gained increasing research interest in recent years. An advanced generative model, the Diffusion Model (DM), exhibits particularly promising performance in image SR. However, effective conditions are required to bootstrap the DM for DWI SR. To this end, we proposed the first DM-based DWI SR model with two effective conditions based on low-solution DWIs and Track Density Imaging (TDI) maps, which possess rich high-resolution prior knowledge Additionally, we consider another condition based on features from low-resolution DWIs. These two conditions are integrated into our model, which comprises three components: DWI Resolution Enhancer (DRE), DWI Feature Extractor (DFE), and TDI Feature Extractor (TFE). DRE combines low-resolution DWI features from DFE with TDI features from TFE to progressively generate high-resolution DWIs. We performed extensive experiments on DWIs of normal subjects from human connectome projects and patients with Parkinson’s disease. The results demonstrate that our model outperforms existing DWI SR models, both qualitatively and quantitatively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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TraSculptor: Visual Analytics for Enhanced Decision-Making in Road Traffic Planning

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IEEE Transactions on Visualization and Computer Graphics 2025年 PP卷 PP页

作者： Deng, Zikun Liu, Yuanbang Zhu, Mingrui Xiang, Da Yu, Haiyue Su, Zicheng Lu, Qinglong Schreck, Tobias Cai, Yi South China University of Technology School of Software Engineering China South China University of Technology Ministry of Education Key Laboratory of Big Data and Intelligent Robot China Tongji University Key Laboratory of Road and Traffic Engineering of the Ministry of Education China National University of Singapore Department of Civil and Environmental Engineering Singapore Graz University of Technology Austria

The design of urban road networks significantly influences traffic conditions, underscoring the importance of informed traffic planning. Traffic planning experts rely on specialized platforms to simulate traffic systems, assessing the efficacy of the road network across various states of modifications. Nevertheless, a prevailing issue persists: many existing traffic planning platforms exhibit inefficiencies in flexibly interacting with the road network's structure and attributes and intuitively comparing multiple states during the iterative planning process. This paper introduces TraSculptor, an interactive planning decision-making system. To develop TraSculptor, we identify and address two challenges: interactive modification of road networks and intuitive comparison of multiple network states. For the first challenge, we establish flexible interactions to enable experts to easily and directly modify the road network on the map. For the second challenge, we design a comparison view with a history tree of multiple states and a road-state matrix to facilitate intuitive comparison of road network states. To evaluate TraSculptor, we provided a usage scenario where the Braess's paradox was showcased, invited experts to perform a case study on the Sioux Falls network, and collected expert feedback through interviews. © 1995-2012 IEEE.

关键词： Visual analytics

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An Encoder-Decoder Model Based On Spiking Neural Networks For Address Event Representation Object Recognition

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Du, Sichun Zhu, Haodi Zhang, Yang Hong, Qinghui Hunan University College of Computer Science and Electronic Engineering Changsha418002 China Shenzhen University Computer Vision Institute School of Computer Science and Software Engineering National Engineering Laboratory for Big Data System Computing Technology Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

Address event representation (AER) object recognition task has attracted extensive attention in neuromorphic vision processing. The spike-based and event-driven computation inherent in the spiking neural network (SNN) provides an energy-saving solution for AER object recognition. However, SNN with spike timing dependent plasticity (STDP) learning rule has not achieved satisfying AER object recognition performance. This work proposes an SNN-based encoder-decoder model to improve the recognition performance of AER objects. An STDP-based locally connected spiking neural network (LC-SNN) is proposed as an encoder to extract rich spatiotemporal features from AER event flows more flexibly. After the encoder extracts and learns primary features, we propose a fully connected spiking neural network (FC-SNN) based on the reward-modulated spike-timing-dependent plasticity (R-STDP) learning rule as a decoder to learn higher-level features for classification. In addition, we improved the winner-take-all (WTA) mechanisms and R-STDP learning rule in the decoder based on the reward and punish decision, enabling the network to perform better. The experiments are performed on the N-MNIST, MNIST-DVS, and the DVS Gesture datasets, improving the accuracy of the best existing plasticity-based SNN by 0.19%, 0.27% and 1.35%, respectively. © 2016 IEEE.

关键词： Decoding

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Electrocardiogram-to-Pair (E2P): A Secure Group Pairing Protocol for WBAN Devices

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IEEE Internet of Things Journal 2025年

作者： Zhao, Guichuan Tian, Youliang Jiang, Qi Ma, Jianfeng Guizhou University College of Computer Science and Technology Guiyang 550000 China Guizhou University State Key Laboratory of Public Big Data College of Big Data and Information Engineering Guiyang 550000 China Xidian University School of Cyber Engineering Xi'an 710071 China

In a wireless body area network (WBAN), group pairing among multiple wearable devices enables efficient and secure broadcasting group messages. Existing pairing methods that rely on trusted concentrators, active participation of users, or homogeneous environments are vulnerable to single point of failure and have restricted practicality. In this article, we propose Electrocardiogram-to-Pair (E2P), an electrocardiogram (ECG)-based secure group pairing protocol for WBAN devices that allows the establishment of a shared group key among multiple devices without requiring the user involvement or the central device. First, all wearable devices worn by the same user simultaneously collect ECG signals, which are then pre-processed through filtering and alignment to minimize the effect of noise on the pairing. Next, an adaptive quantization method is designed to reliably quantize the pre-processed ECG signals to generate initial keys. This method is ingenious as it relies on feature transformation and dynamic thresholds generation in the quantization process, which effectively ensures the security of biometrics and increases the key generation rate. Then, the group key is established among devices with initial keys through an improved Cascade method. The simulation results demonstrate that E2P achieves an effective balance the reliability and efficiency in group pairing, with outstanding performance in both security and key generation rate. © 2014 IEEE.

关键词： Electrocardiogram group pairing key establishment wireless body area network

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Deep Learning Based Proactive Anomaly Detection for 5G Core Control Plane Network Function Interactions

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Tan, Yawen Liu, Jiajia Li, Yuanhao Wang, Jiadai Xidian University State Key Laboratory of Integrated Services Networks School of Cyber Engineering Xi'an710071 China Northwestern Polytechnical University National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Cybersecurity Shaanxi Xi'an710072 China

The Service-Based Architecture (SBA) introduced by 3GPP allows the control plane of 5G Core Network (CN) to function through a set of interconnected Network Functions (NFs), which offers significant benefits such as improved scalability, simplified operations, and efficient resource utilization. However, such a new transformation could also make critical NFs in 5G CN become more susceptible to attacks due to both internal vulnerabilities within the software-implemented NFs or the expanded interfaces brought by the SBA. Existing studies targeting 5G network security pay little attention to the detection of abnormal behaviors from control plane NFs. Therefore, in this paper, we propose 5GCGuard, an anomaly detection scheme aiming at identifying abnormal NF interactions in 5G CN. It employs a deep learning-based sequence model to learn normal interaction patterns and detects anomalies based on deviations from the learned pattern. Special designs including attention mechanism, multi-task learning, probabilistic labeling and automatic threshold decision are made to enhance its detection capabilities. We establish a cloud-native 5G testbed to evaluate the effectiveness of 5GCGuard. Extensive evaluation results show its superiority over comparison schemes. Case studies also demonstrate its ability to proactively detect various NF interaction anomalies, enabling swift action to block attacks and prevent further damage. © 2015 IEEE.

关键词： 5G mobile communication systems

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Zero-Shot Cross-Domain Code Search without Fine-Tuning

arXiv

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arXiv 2025年

作者： Liang, keyu Liu, Zhongxin Liu, Chao Wan, Zhiyuan Lo, David Yang, Xiaohu The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China School of Big Data and Software Engineering Chongqing University Chongqing China School of Computing and Information Systems Singapore Management University Singapore Singapore

Code search is a crucial task in software engineering, aiming to retrieve code snippets that are semantically relevant to a natural language query. Recently, Pre-trained Language Models (PLMs) have shown remarkable success and are widely adopted for code search tasks. However, PLM-based methods often struggle in cross-domain scenarios. When applied to a new domain, they typically require extensive fine-tuning with substantial data. Even worse, the data scarcity problem in new domains often forces these methods to operate in a zero-shot setting, resulting in a significant decline in performance. RAPID, which generates synthetic data for model fine-tuning, is currently the only effective method for zero-shot cross-domain code search. Despite its effectiveness, RAPID demands substantial computational resources for fine-tuning and needs to maintain specialized models for each domain, underscoring the need for a zero-shot, fine-tuning-free approach for cross-domain code search. The key to tackling zero-shot cross-domain code search lies in bridging the gaps among domains. In this work, we propose to break the query-code matching process of code search into two simpler tasks: query-comment matching and code-code matching. We first conduct an empirical study to investigate the effectiveness of these two matching schemas in zero-shot cross-domain code search. Our findings highlight the strong complementarity among the three matching schemas, i.e., query-code, query-comment, and code-code matching. Based on the findings, we propose CodeBridge, a zero-shot, fine-tuning-free approach for cross-domain code search. Specifically, CodeBridge first employs zero-shot prompting to guide Large Language Models (LLMs) to generate a comment for each code snippet in the codebase and produce a code for each query. Subsequently, it encodes queries, code snippets, comments, and the generated code using PLMs and assesses similarities through three matching schemas: query-code, query-comment,

关键词： Search engines

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Using Depth-Enhanced Spatial Transformation for Student Gaze Target Estimation in Dual-View Classroom Images

Using Depth-Enhanced Spatial Transformation for Student Gaze...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Haonan Miao Peizheng Zhao Yuqi Sun Fang Nan Xiaolong Zhang Yaqiang Wu Feng Tian School of Computer Science and Technology Xi’an Jiaotong University Xi’an China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Dual-view gaze target estimation in classroom environments has not been thoroughly explored. Existing methods lack consideration of depth information, primarily focusing on 2D image information and neglecting the latent 3D spatial context, which could lead to suboptimal transformation and cause the gaze cone to intersect with an incorrect object. This paper introduces a novel dual-view gaze target estimation method tailored for classroom settings, leveraging depth-enhanced spatial transformations. By formulating a depth-enhanced 2D space, our method uses depth-enhanced spatial transformation to accurately project students’ gaze cones to the teacher-oriented image. Additionally, we collected a dataset named DVSGE, specifically for student gaze target estimation in dual-view classroom images. Experimental results demonstrate significant performance improvements of 9.8% in AUC and 19.9% in L2-Distance for our method, surpassing existing methods.

关键词： Three-dimensional displays Estimation Focusing Signal processing Acoustics Speech processing

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Health risks of rare earth elements exposure:Impact on mitochondrial DNA copy number and micronucleus frequency

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Journal of Environmental Sciences 2025年第5期151卷 150-160页

作者： Shiyi Hong Yali Zhang Qiaojian Zhang Zekang Su Guiping Hu Li Wang Shanfa Yu Xiaojun Zhu Guang Jia Department of Occupational and Environmental Health Sciences School of Public HealthPeking UniversityBeijing 100083China School of Engineering Medicine and Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang UniversityBeijing 100083China Department of Toxicology School of Public HealthBaotou Medical CollegeBaotouInner Mongolia 014040China Henan Institute for Occupational Medicine Zhengzhou 450052China National Center for Occupational Safety and Health Beijing 102308China Beijing Key Laboratory of Toxicological Research and Risk Assessment for Food Safety School of Public HealthPeking UniversityBeijing 100083China

Toxic effects in terms of mitochondria and hereditary substances have been characterized in vitro for individual rare earth elements,while,the joint effects of mixed elements exposure in the population remain *** on the Occupational Chromate Exposure Dynamic Cohort of China,this study investigated the relationship between 15 blood rare earth elements(cerium,dysprosium,erbium,europium,gadolinium,holmium,lanthanum,lutetium,neodymium,praseodymium,samarium,terbium,thulium,yttrium,and ytterbium)and mitochondrial DNA copy number(MtDNACN)as well as peripheral blood lymphocyte micronucleus frequency(MNF).The elastic net was used to select elements highly correlated with effect indicators,whose dose-response relationships were further illustrated by restricted cubic *** kernel regressionwas employed to explore the combined effects of elements and the contributions of single *** results showed that most rare earth elementswere positively correlatedwith effect indicators,with yttrium showing the strongest association(β(95%CI):0.139(0.1089–0.189)for MtDNACN,0.937(0.345–1.684)for MNF).In the mixed exposure model,with the exposure level fixed at the 50th percentile as the reference,the effect estimates on MtDNACN and MNF increased by 0.228 and 0.598 units,respectively,at the 75th *** single effect analysis implied that yttrium,lanthanum and terbium contributed the most to the elevation of MtDNACN,while yttrium posed the highest risk for genetic damage,accordingly,we provided recommendations to prioritize these elements of *** addition,we observed a chief mediating effect of MtDNACN on the elevation of MNF caused by lanthanum,whereas further mechanistic exploration is required to confirm this finding.

关键词： Rare earth elements Mitochondrial DNA copy number Micronucleus frequency Mixed effect

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FMDPgram: An Improvement of FMD for Rotating Machinery Fault Diagnosis

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IEEE Transactions on Industrial Informatics 2025年

作者： Li, Hua Wang, Tianyang Zhang, Feibin Chu, Fulei Guizhou University State Key Laboratory of Public Big Data Guiyang 550025 China Tsinghua University Department of Mechanical Engineering Beijing 100084 China

Recently, feature mode decomposition (FMD) has been proposed and has demonstrated robust performance in the application of rotating machinery fault diagnosis. However, FMD does not have parameter adaptability, and its influencing parameters (i.e., segment number, mode number and filter length) need to be defined in advance. Inspired by the decomposition mode of wavelet packet transform, this article cleverly proposes a binary decomposition structure of FMD to form the concept of FMD packet (FMDP). Furthermore, the tiling structure of FMDP is constructed, thus forming the FMDPgram. In FMDPgram, the frequency band of the raw signal is evenly divided into 2 segments, and each FMD generates two submode components (SMCs), thus simplifying the hyperparameter optimization into a single-parameter optimization. Additionally, an indicator called narrowband peak-to-average ratio (NPAR) is proposed for optimal SMC positioning, and which is displayed in the FMDPgram tile map. First, performing FMDPgram with NPAR on the raw signal. Then, the optimal SMC is located based on the NPAR in the FMDPgram tile map. Finally, the envelope power spectrum of the optimal SMC is calculated to characterize the fault characteristic information. FMDPgram is tested on simulated and experimental data, and compared with FMD, spectral kurtosis, VMD and improved Kurtogram to evaluate its performance in rolling bearing diagnosis under low signal-to-noise ratio and non-Gaussian interference. © 2005-2012 IEEE.

关键词： Fault diagnosis feature mode decomposition (FMD) FMD packet (FMDP) FMDPgram main submode component narrowband peak-to-average ratio (NPAR)

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