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arXiv 2025年

作者： Hu, Guyue Song, Siyuan Kang, Yukun Yin, Zhu Zhao, Gangming Li, Chenglong Tang, Jin Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui Provincial Key Laboratory of Security Artificial Intelligence School of Artificial Intelligence Anhui University Hefei230601 China School of Internet Anhui University Hefei230601 China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China Department of Computer Science The University of Hong Kong Hong Kong

Medical image segmentation in X-ray images is beneficial for computer-aided diagnosis and lesion localization. Existing methods mainly fall into a centralized learning paradigm, which is inapplicable in the practical medical scenario that only has access to distributed data islands. Federated Learning has the potential to offer a distributed solution but struggles with heavy training instability due to client-wise domain heterogeneity (including distribution diversity and class imbalance). In this paper, we propose a novel Federated Client-tailored Adapter (FCA) framework for medical image segmentation, which achieves stable and client-tailored adaptive segmentation without sharing sensitive local data. Specifically, the federated adapter stirs universal knowledge in off-the-shelf medical foundation models to stabilize the federated training process. In addition, we develop two client-tailored federated updating strategies that adaptively decompose the adapter into common and individual components, then globally and independently update the parameter groups associated with common client-invariant and individual client-specific units, respectively. They further stabilize the heterogeneous federated learning process and realize optimal client-tailored instead of sub-optimal global-compromised segmentation models. Extensive experiments on three large-scale datasets demonstrate the effectiveness and superiority of the proposed FCA framework for federated medical segmentation. © 2025, CC BY.

关键词： Federated learning

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Adversarial Testing: A Novel On-Line Testing Method for Deep Learning Processors

Adversarial Testing: A Novel On-Line Testing Method for Deep...

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Asian Test Symposium (ATS)

作者： Wen Li Ying Wang Kaiwei Zou Huawei Li Xiaowei Li School of Computer and Information Technology Shanxi University Taiyuan Shanxi China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Taiyuan Shanxi China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Department of Electronic Engineering Tsinghua University Beijing China

Deep neural networks have shown outstanding performance on complex tasks. Recently, various researches have been developed to pursue fast and energy-efficient deep learning accelerators. However, devices may suffer from hard defects and hardware variability during its lifetime, which poses severe challenges to deep learning accelerators. To protect edge deep learning accelerators from fault-induced failures, we leverage the adversarial deep learning technique to tailor a lightweight online fault detection method for neural network accelerator chips. The proposed Adversarial Testing scheme (AT) is a function-level testing method outcompeting conventional test in several ways: negligible run-time overhead, super sensitivity to subtle hardware variations, which reduces chip over-kills and also the unnecessary diagnosis operations. The evaluation results show that AT can accurately detect fault occurrence and ensure the normal use of deep learning accelerator during its lifetime.

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RGBT Tracking via Progressive Fusion Transformer with Dynamically Guided Learning

arXiv

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arXiv 2023年

作者： Zhu, Yabin Li, Chenglong Wang, Xiao Tang, Jin Huang, Zhixiang Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Key Laboratory of Electromagnetic Environmental Sensing of Anhui Higher Education Institutes School of Electronic and Information Engineering Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Artificial Intelligence Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China

Existing Transformer-based RGBT tracking methods either use cross-attention to fuse the two modalities, or use self-attention and cross-attention to model both modality-specific and modality-sharing information. However, the significant appearance gap between modalities limits the feature representation ability of certain modalities during the fusion process. To address this problem, we propose a novel Progressive Fusion Transformer called ProFormer, which progressively integrates single-modality information into the multimodal representation for robust RGBT tracking. In particular, ProFormer first uses a self-attention module to collaboratively extract the multimodal representation, and then uses two cross-attention modules to interact it with the features of the dual modalities respectively. In this way, the modality-specific information can well be activated in the multimodal representation. Finally, a feed-forward network is used to fuse two interacted multimodal representations for the further enhancement of the final multimodal representation. In addition, existing learning methods of RGBT trackers either fuse multimodal features into one for final classification, or exploit the relationship between unimodal branches and fused branch through a competitive learning strategy. However, they either ignore the learning of single-modality branches or result in one branch failing to be well optimized. To solve these problems, we propose a dynamically guided learning algorithm that adaptively uses well-performing branches to guide the learning of other branches, for enhancing the representation ability of each branch. Extensive experiments demonstrate that our proposed ProFormer sets a new state-of-the-art performance on RGBT210, RGBT234, LasHeR, and VTUAV datasets. In particular, the improvements of ProFormer on the LasHeR dataset are up to +6.2%/+ 4.1% in PR/SR compared with existing best RGBT tracker. Copyright © 2023, The Authors. All rights reserved.

关键词： Learning algorithms

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Embedding Dynamic Attributed Networks by Modeling the Evolution Processes 28

Embedding Dynamic Attributed Networks by Modeling the Evolut...

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28th International Conference on Computational Linguistics, COLING 2020

作者： Xu, Zenan Ou, Zijing Su, Qinliang Yu, Jianxing Quan, Xiaojun Lin, Zhenkun School of Data and Computer Science Sun Yat-Sen University China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Key Lab. of Machine Intelligence and Advanced Computing Ministry of Education China

ISBN: (纸本)9781952148279

Network embedding has recently emerged as a promising technique to embed nodes of a network into low-dimensional vectors. While fairly successful, most existing works focus on the embedding techniques for static networks. But in practice, there are many networks that are evolving over time and hence are dynamic, e.g., the social networks. To address this issue, a high-order spatio-temporal embedding model is developed to track the evolutions of dynamic networks. Specifically, an activeness-aware neighborhood embedding method is first proposed to extract the high-order neighborhood information at each given timestamp. Then, an embedding prediction framework is further developed to capture the temporal correlations, in which the attention mechanism is employed instead of recurrent neural networks (RNNs) for its efficiency in computing and flexibility in modeling. Extensive experiments are conducted on four real-world datasets from three different areas. It is shown that the proposed method outperforms all the baselines by a substantial margin for the tasks of dynamic link prediction and node classification, which demonstrates the effectiveness of the proposed methods on tracking the evolutions of dynamic networks. © 2020 COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference. All rights reserved.

关键词： Recurrent neural networks

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Cross-Modal Object Tracking via Modality-Aware Fusion Network and A Large-Scale Dataset

arXiv

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arXiv 2023年

作者： Liu, Lei Zhang, Mengya Li, Cheng Li, Chenglong Tang, Jin The Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Artificial Intelligence Anhui University Hefei230601 China The Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Laboratory of Intelligent Computing and Signal Processing The Ministry of Education Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China The School of Computer Science and Technology Anhui University Hefei230601 China

Visual tracking often faces challenges such as invalid targets and decreased performance in low-light conditions when relying solely on RGB image sequences. While incorporating additional modalities like depth and infrared data has proven effective, existing multi-modal imaging platforms are complex and lack real-world applicability. In contrast, near-infrared (NIR) imaging, commonly used in surveillance cameras, can switch between RGB and NIR based on light intensity. However, tracking objects across these heterogeneous modalities poses significant challenges, particularly due to the absence of modality switch signals during tracking. To address these challenges, we propose an adaptive cross-modal object tracking algorithm called Modality-Aware Fusion Network (MAFNet). MAFNet efficiently integrates information from both RGB and NIR modalities using an adaptive weighting mechanism, effectively bridging the appearance gap and enabling a modality-aware target representation. It consists of two key components: an adaptive weighting module and a modality-specific representation module. The adaptive weighting module predicts fusion weights to dynamically adjust the contribution of each modality, while the modality-specific representation module captures discriminative features specific to RGB and NIR modalities. MAFNet offers great flexibility as it can effortlessly integrate into diverse tracking frameworks. With its simplicity, effectiveness, and efficiency, MAFNet outperforms state-of-the-art methods in cross-modal object tracking. To validate the effectiveness of our algorithm and overcome the scarcity of data in this field, we introduce CMOTB, a comprehensive and extensive benchmark dataset for cross-modal object tracking. CMOTB consists of 61 categories and 1000 video sequences, comprising a total of over 799K frames. We believe that our proposed method and dataset offer a strong foundation for advancing cross-modal object tracking research. The dataset, toolkit, a

关键词： Large dataset

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An intelligent self-sustained RAN slicing framework for diverse service provisioning in 5G-beyond and 6G networks

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Intelligent and Converged Networks 2020年第3期1卷 281-294页

作者： Jie Mei Xianbin Wang Kan Zheng Electrical and Computer Engineering Western UniversityLondonON N6A 5B9Canada Intelligent Computing and Communication(IC2)Lab Wireless Signal Processing and Network(WSPN)LabKey Laboratory of Universal Wireless CommunicationMinistry of EducationBeijing University of Posts and Telecommunications(BUPT)Beijing 100876China

Network slicing is a key technology to support the concurrent provisioning of heterogeneous Quality of Service(QoS)in the 5th Generation(5G)-beyond and the 6th Generation(6G)***,effective slicing of Radio Access Network(RAN)is very challenging due to the diverse QoS requirements and dynamic conditions in the 6G *** this paper,we propose a self-sustained RAN slicing framework,which integrates the self-management of network resources with multiple granularities,the self-optimization of slicing control performance,and self-learning together to achieve an adaptive control strategy under unforeseen network *** proposed RAN slicing framework is hierarchically structured,which decomposes the RAN slicing control into three levels,i.e.,network-level slicing,next generation NodeB(gNodeB)-level slicing,and packet scheduling level *** the network level,network resources are assigned to each gNodeB at a large timescale with coarse resource *** the gNodeB-level,each gNodeB adjusts the configuration of each slice in the cell at the large *** the packet scheduling level,each gNodeB allocates radio resource allocation among users in each network slice at a small ***,we utilize the transfer learning approach to enable the transition from a model-based control to an autonomic and self-learning RAN slicing *** the proposed RAN slicing framework,the QoS performance of emerging services is expected to be dramatically enhanced.

关键词： Radio Access Network(RAN) network slicing network resource management intelligent network

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A High Isolation UWB MIMO Antenna based on Angle Diversity

A High Isolation UWB MIMO Antenna based on Angle Diversity

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IEEE International Wireless Symposium (IWS)

作者： Qin Li Yufa Sun Key Lab of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China

ISBN: (数字)9781728167039

ISBN: (纸本)9781728167046

A UWB MIMO antenna based on angle diversity technology is proposed in this paper. This antenna integrates Vivaldi antenna and UWB slot antenna with a compact size of 26×26mm 2 , the difference of radiation pattern can realize lower coupling between different antenna units. Less than -19 dB isolation between different ports can be obtained through the whole UWB band (3.1-10.6GHz). In addition, the combination of one C-Shaped structure and two L-Shaped structures (CLL) avoids the interference of WLAN (5.125-5.825GHz) band. The proposed antenna has good performance, which is suitable for UWB communication system.

关键词： Wireless LAN Slot antennas Vivaldi antennas Interference Ultra wideband antennas MIMO communication Antenna radiation patterns

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A Low SAR, Small size, High gain, Circularly polarized Antenna for Wearable Application 2

A Low SAR, Small size, High gain, Circularly polarized Anten...

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2020 2nd International Conference on Artificial intelligence Technologies and Application, ICAITA 2020

作者： Wang, Chao Zhang, Liang Cheng, Yi Li, XueMei Wu, Xianliang Key Lab of Ministry of Education of Intelligent Computing and Signal Processing Anhui University Hefei Anhui230601 China AnHui Province Key Laboratory of Simulation and Design for Electronic Information System Hefei Normal University Hefei Anhui230601 China

A planar, small size, high gain, low specific absorption rate (SAR) and circularly-polarized (CP) wearable antenna is proposed in this paper. The antenna is fabricated on a substrate with a dielectric constant of 3.5 with a thickness of 0.5 mm and sized at 42×42 mm only. Both the simulation results show that the overlap bandwidth of impedance and axial ratio of the proposed antenna is 13.7% (5.15-5.91 GHz). To isolate the antenna against the influence of the human body, a artificial magnetic conductor (AMC) plane is added on the reverse side of the radiator. The AMC structure effectively isolates the human body from the radiation of the antenna and reduces the specific absorption rate (SAR) of it by more than 96.1% at 5.5 GHz, which improves the antenna gain and the peak gain reaches 5.09 dBi at 5.5 GHz. The wearable performance of the antenna was analyzed, and it is observed that it can sustain good performance even under human body loading. Beside, the antenna has a small size, which makes it ideal for wearable applications. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Wearable antennas

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A Novel EBG Power Plane Structure for Ultrawideband Suppression of SSN in High Speed Circuits

A Novel EBG Power Plane Structure for Ultrawideband Suppress...

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Cross Strait Quad-Regional Radio Science and Wireless Technology Conference (CSQRWC)

作者： Yali Zhao Haoran Zhu Key Lab of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China

ISBN: (数字)9781728181813

ISBN: (纸本)9781728181820

A novel structure based on the coplanar electromagnetic band-gap (EBG) power plane is proposed to realize the ultrawideband suppression of simultaneous switching noise (SSN). The proposed EBG structure is realized with a combination of L-shaped Bridge and slit EBG structure which is inserted by a complementary split ring resonator (CSRR). From the simulated results of the proposed EBG structure, a wideband suppression of SSN ranges from 340 MHz to 20 GHz is achieved with a high mitigation level of -40 dB. Compared with the earlier published structures, the suppression effect is greatly improved.

关键词： Wireless communication Split ring resonators Switches Metamaterials Wideband Periodic structures Ultra wideband technology

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Understanding the Robustness of 3D Object Detection with Bird'View Representations in Autonomous Driving

Understanding the Robustness of 3D Object Detection with Bir...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zijian Zhu Yichi Zhang Hai Chen Yinpeng Dong Shu Zhao Wenbo Ding Jiachen Zhong Shibao Zheng Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Dept. of Comp. Sci. and Tech. THBI Lab Institute for AI Tsinghua University BNRist Center Zhongguancun Laboratory Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Information Materials and Intelligent Sensing Laboratory of Anhui Province School of Computer Science and Technology Anhui University SAIC Motor AI Lab

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird's-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations; 2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features; 3) Camera-LiDARfusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models.

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