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arXiv 2024年

作者： Dong, Li Jiang, Feibo Wang, Minjie Peng, Yubo Li, Xiaolong School of Computer Science Hunan University of Technology and Business Changsha China Xiangjiang Laboratory Changsha China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China College of Information Science and Engineering Hunan Normal University Changsha China

The intelligent reflection surface (IRS) and unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is widely used in temporary and emergency scenarios. Our goal is to minimize the energy consumption of the MEC system by jointly optimizing UAV locations, IRS phase shift, task offloading, and resource allocation with a variable number of UAVs. To this end, we propose a Flexible REsource Scheduling (FRES) framework by employing a novel deep progressive reinforcement learning which includes the following innovations: Firstly, a novel multi-task agent is presented to deal with the mixed integer nonlinear programming (MINLP) problem. The multi-task agent has two output heads designed for different tasks, in which a classified head is employed to make offloading decisions with integer variables while a fitting head is applied to solve resource allocation with continuous variables. Secondly, a progressive scheduler is introduced to adapt the agent to the varying number of UAVs by progressively adjusting a part of neurons in the agent. This structure can naturally accumulate experiences and be immune to catastrophic forgetting. Finally, a light taboo search (LTS) is introduced to enhance the global search of the FRES. The numerical results demonstrate the superiority of the FRES framework which can make real-time and optimal resource scheduling even in dynamic MEC systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Logical Relation Modeling and Mining in Hyperbolic Space for Recommendation 40

Logical Relation Modeling and Mining in Hyperbolic Space for...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Tan, Yanchao Lv, Hang Zhou, Zihao Guo, Wenzhong Xiong, Bo Liu, Weiming Chen, Chaochao Wang, Shiping Yang, Carl College of Computer and Data Science Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Institute for Artificial Intelligence University of Stuttgart Stuttgart Germany College of Computer Science Zhejiang University Hangzhou China Emory University Department of Computer Science Atlanta United States

ISBN: (纸本)9798350317152

The sparse interactions between users and items have aggravated the difficulty of their representations in recommender systems. Existing methods leverage tags to alleviate the sparsity problem but ignore prevalent logical relations among items and tags (e.g., membership, hierarchy, and exclusion), which can be leveraged to enhance the accuracy of modeling user preferences and conducting recommendations. To this end, we propose to extract logical relations among item tags from existing tag taxonomies and exploit the individual strengths of the Poincaré and the Lorentz models in hyperbolic space for logical relation modeling towards enhanced recommendations. Moreover, we find that the logical relations directly extracted from existing tag taxonomies can be inaccurate and coarse. Therefore, we further devise innovative consistency-based and granularity-based weighting mechanisms based on user behavior patterns for data-driven logical relation mining that can be jointly optimized along with recommendations in an end-to-end fashion. Extensive experiments on four real-world benchmark datasets show drastic performance gains brought by our proposed framework, which constantly achieves an average of 8.25% improvement over state-of-the-art competitors regarding both Recall and NDCG metrics. Insightful case studies further demonstrate that our automatically refined logical relations are highly accurate and interpretable. © 2024 IEEE.

关键词： Recommender systems

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Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning 24

Towards High-resolution 3D Anomaly Detection via Group-Level...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9798400706868

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3D-AD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. © 2024 ACM.

关键词： Contrastive Learning

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Medical Image Segmentation Based on 3d Pdc with Swin Transformer

SSRN

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SSRN 2025年

作者： Fan, Lin Ding, Xiaojia Wang, Zhongmin Wang, Hai Zhang, Rong School of Computer Science and Technology Xi’an University of Posts and Telecommunications Shaanxi Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi’an710121 China Xi’an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi’an710121 China The School of Information Science and Technology Northwest University Shaanxi Xi’an710121 China

3D medical image segmentation is vital for disease diagnosis and effective treatment strategies. Despite the advancements in Convolutional Neural Networks (CNN), their fixed receptive fields constrain global context modeling, leading to suboptimal performance, particularly with complex shapes and multi-scale variations. Transformer enhances the global modeling capability through self-attention mechanism. However, challenges persist in capturing fine-grained local features and extracting edge features. To overcome these issues, In this study, a dual-path encoder is proposed, which consists of a 3D PDC and a Swin Transformer and is combined with a CNN for the decoder. The 3D PDC module extracts local features by calculating the differences between neighboring pixels. To improve the ability of capturing edge structures, three 3D PDC variants (3D CPDC, 3D APDC, and 3D RPDC) are proposed to optimize the processing ability of complex edges, multi-directional edges, and multi-scale structures, respectively. After performance evaluation, the selected CARV combinations (3D CPDC, 3D APDC, 3D RPDC, and 3D ordinary convolution) achieve a compromise between high segmentation accuracy and low computational cost. The Swin Transformer encoder captures global context using a hierarchical shift-window mechanism, while the CNN decoder fuses features progressively to produce precise pixel-level segmentation. The proposed method exceeds the performance of existing state-of-the-art techniques, as shown by experiments conducted on the BTCV, FLARE21, and AMOS22 datasets. © 2025, The Authors. All rights reserved.

关键词： Image segmentation

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Non-autoregressive Machine Translation with Probabilistic Context-free Grammar

arXiv

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arXiv 2023年

作者： Gui, Shangtong Shao, Chenze Ma, Zhengrui Zhang, Xishan Chen, Yunji Feng, Yang State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Cambricon Technologies China

Non-autoregressive Transformer(NAT) significantly accelerates the inference of neural machine translation. However, conventional NAT models suffer from limited expression power and performance degradation compared to autoregressive (AT) models due to the assumption of conditional independence among target tokens. To address these limitations, we propose a novel approach called PCFG-NAT, which leverages a specially designed Probabilistic Context-Free Grammar (PCFG) to enhance the ability of NAT models to capture complex dependencies among output tokens. Experimental results on major machine translation benchmarks demonstrate that PCFG-NAT further narrows the gap in translation quality between NAT and AT models. Moreover, PCFG-NAT facilitates a deeper understanding of the generated sentences, addressing the lack of satisfactory explainability in neural machine translation.2 Copyright © 2023, The Authors. All rights reserved.

关键词： Computer aided language translation

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Automated Segmentation of Lateral Ventricle in MR Images Using Multi-scale Feature Fusion Convolutional Neural Network 25th

Automated Segmentation of Lateral Ventricle in MR Images Usi...

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25th International Conference on Pattern Recognition Workshops, ICPR 2020

作者： Ye, Fei Wang, Zhiqiang Hu, Kai Zhu, Sheng Gao, Xieping Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan411105 China College of Medical Imaging and Inspection Xiangnan University Chenzhou423000 China

ISBN: (纸本)9783030687793

Studies have shown that the expansion of the lateral ventricle is closely related to many neurodegenerative diseases, so the segmentation of the lateral ventricle plays an important role in the diagnosis of related diseases. However, traditional segmentation methods are subjective, laborious, and time-consuming. Furthermore, due to the uneven magnetic field, irregular, small, and discontinuous shape of every single slice, the segmentation of the lateral ventricle is still a great challenge. In this paper, we propose an efficient and automatic lateral ventricle segmentation method in magnetic resonance (MR) images using a multi-scale feature fusion convolutional neural network (MFF-Net). First, we create a multi-center clinical dataset with a total of 117 patient MR scans. This dataset comes from two different hospitals and the images have different sampling intervals, different ages, and distinct image dimensions. Second, we present a new multi-scale feature fusion module (MSM) to capture different levels of feature information of lateral ventricles through various receptive fields. In particular, MSM can also extract the multi-scale lateral ventricle region feature information to solve the problem of insufficient feature extraction of small object regions with the deepening of network structure. Finally, extensive experiments have been conducted to evaluate the performance of the proposed MFF-Net. In addition, to verify the performance of the proposed method, we compare MFF-Net with seven state-of-the-art segmentation models. Both quantitative results and visual effects show that our MFF-Net outperforms other models and can achieve more accurate segmentation performance. The results also indicate that our model can be applied in clinical practice and is a feasible method for lateral ventricle segmentation. © 2021, Springer Nature Switzerland AG.

关键词： Magnetic resonance

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Unsupervised Multivariate Time Series Anomaly Detection by Feature Decoupling in Federated Learning Scenarios

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： He, Yifan Ding, Xi Tang, Yateng Guan, Jihong Zhou, Shuigeng Fudan University School of Computer Science Shanghai200433 China Shanghai Key Laboratory of Intelligent Information Processing Shanghai200433 China Tencent Weixin Group Guangzhou510308 China Tongji University Department of Computer Science and Technology Shanghai201804 China Ministry of Education Key Laboratory of Embedded System and Service Computing Tongji University Shanghai200092 China

Anomalies are usually regarded as data errors or novel patterns previously unseen, which are quite different from most observed data. Accurate detection of anomalies is crucial in various application scenarios. This paper focuses on unsupervised anomaly detection from Multivariate Time Series (MTS), as real-world data collected from sources such as wearable devices, medical equipment and industrial machines typically manifest as MTS and are often unlabeled. Anomaly detection in MTS represents a data-driven challenge that traditionally requires substantial centralized data for training models. However, in practice, data are frequently distributed among multiple institutions, with privacy concerns restricting unrestricted access. To address these issues, we introduce Feature Decoupling Federated Learning (FDFL), an approach designed to collaboratively train a representation learning network over multiple clients for unsupervised anomaly detection in MTS. Unlike previous methods that simply integrate MTS anomaly detection algorithms with federated learning strategies, FDFL specifically addresses heterogeneity among clients by decoupling the representation network into shared and private branches through a contrastive learning mechanism. This method aggregates shared parameters during each federated round while maintaining client-specific private parameters locally. Additionally, we develop a self-attention block that integrates the representations derived from both shared and private parameters to reconstruct MTS and identify anomalies based on reconstruction errors. Extensive experiments conducted on three publicly available datasets demonstrate that FDFL outperforms existing algorithms in most cases, highlighting the effectiveness and superiority of our proposed method in MTS anomaly detection. © 2020 IEEE.

关键词： Federated learning

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Nonreciprocity with Structured Light Using Optical Pumping in Hot Atoms

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Physical Review Applied 2022年第2期18卷 024027-024027页

作者： Fei Song Zhiping Wang Enze Li Benli Yu Zhixiang Huang Key Laboratory of Opto-Electronic Information Acquisition and Manipulation Ministry of Education Anhui University Hefei 230601 China and Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Hefei 230601 China Key Laboratory of Quantum Information University of Science and Technology of China Hefei Anhui 230026 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei 230039 China

Nonreciprocal transmission of structured light has possible applications in high-capacity optical communication and high-dimensional signal processing. Here, we demonstrate the magnet-free and cavity-free optical nonreciprocity of an orbital-angular-momentum (OAM) beam using optical-pumping technology with the assistance of the Doppler effect in hot atoms. By using optical pumping to induce a direction-dependent population transfer of the ground-state atoms, we realize optical nonreciprocity for the signal laser that is carrying OAM. Interestingly, an isolation ratio close to 30 dB and maximum transmission over 0.86 can be achieved simultaneously without the use of an optical cavity. Due to the unique amplitude and helical phase profile of the structured light, our proposal may open an avenue for exploring the singularity characteristics of optical nonreciprocity using structured light.

关键词： Angular momentum of light Light-matter interaction Non-reciprocal transmission Optical pumping Alkali metals Atomic gases Communication networks Devices

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A Lumped Elements Integrated Miniaturization Super-Wideband Antenna

A Lumped Elements Integrated Miniaturization Super-Wideband ...

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International Workshop on Antenna Technology: Small Antennas and Novel Metamaterials, iWAT

作者： Yinfeng Xia Yingsong Li Wei Xue Luyu Zhao Eugene Sinkevich Vladimir Mordachev College of Information and Communication Engineering Harbin Engineering University Harbin China The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China EMC R&D Laboratory Belarusian State University of Informatics and Radioelectronics Minsk Belarus

ISBN: (数字)9798350314755

ISBN: (纸本)9798350314762

A lumped elements integrated miniaturization super-wideband antenna is proposed, which is fed by coplanar waveguide (CPW) that consists of feeding line, extended ground plane, three S-shaped-slot rectangle radiation patches and several integrated lumped elements. The three S-shaped-slot rectangle radiation patches are electromagnetically coupled by the balanced CPW feeding line. To enhance the bandwidth of the designed antenna, several lumped elements are integrated into the antenna. The designed antenna is modeled in CST. By co-simulation and optimizing using CST and ADS. A -10 dB fractional bandwidth of 169.5% can be achieved which ranges from 0.8 GHz-9.7 GHz. Moreover, the proposed antenna has good omnidirectional radiation patterns in the operating band.

关键词： Conferences Rectangular waveguides Bandwidth Antenna feeds Distance measurement Coplanar waveguides Planar waveguides

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A Passive Component Incorporated Miniaturization Super-Wideband Antenna for EMC Measurement

A Passive Component Incorporated Miniaturization Super-Wideb...

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Antennas and Propagation & USNC/URSI National Radio Science Meeting

作者： Yinfeng Xia Yingsong Li Wei Xue Luyu Zhao Eugene Sinkevich Vladimir Mordachev College of Information and Communication Engineering Harbin Engineering University Harbin China The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China EMC R&D laboratory Belarusian State University of Informatics and Radioelectronics Minsk Belarus

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

A passive component incorporated miniaturization super-wideband (PCIM-SWB) antenna is proposed for EMC measurement, which has a feeding line with passive components, circle-shaped radiating patch, defected ground. The proposed PCIM-SWB antenna is created from a simple monopole, where its defected ground is to enhance its bandwidth. The two passive components are integrated into the feeding to further to improve its bandwidth to cover lower frequency band. By co-simulation and optimization using HFSS and ADS, a -10 dB fractional bandwidth of 163.2% is gotten for the PCIM-SWB ranging from 0.48 GHz to 4.74 GHz with omnidirectional radiating pattern, which can be employed for Wi-Fi and many military applications.

关键词： Antenna measurements Bandwidth Electromagnetic compatibility Distance measurement Broadband antennas Antenna accessories Wireless fidelity

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