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arXiv 2025年

作者： Yue, Wenzhen Liu, Yong Li, Haoxuan Wang, Hao Ying, Xianghua Guo, Ruohao Xing, Bowei Shi, Ji State Key Laboratory of General AI Peking University Beijing100871 China Center for Data Science Peking University Beijing100871 China School of Software BNRist Tsinghua University Beijing100084 China Department of Control Science and Engineering Zhejiang University Hangzhou310058 China

This paper presents OLinear, a linear-based multivariate time series forecasting model that operates in an orthogonally transformed domain. Recent forecasting models typically adopt the temporal forecast (TF) paradigm, which directly encode and decode time series in the time domain. However, the entangled stepwise dependencies in series data can hinder the performance of TF. To address this, some forecasters conduct encoding and decoding in the transformed domain using fixed, dataset-independent bases (e.g., sine and cosine signals in the Fourier transform). In contrast, we utilize OrthoTrans, a data-adaptive transformation based on an orthogonal matrix that diagonalizes the series’ temporal Pearson correlation matrix. This approach enables more effective encoding and decoding in the decorrelated feature domain and can serve as a plug-in module to enhance existing forecasters. To enhance the representation learning for multivariate time series, we introduce a customized linear layer, NormLin, which employs a normalized weight matrix to capture multivariate dependencies. Empirically, the NormLin module shows a surprising performance advantage over multi-head self-attention, while requiring nearly half the FLOPs. Extensive experiments on 24 benchmarks and 140 forecasting tasks demonstrate that OLinear consistently achieves state-of-the-art performance with high efficiency. Notably, as a plug-in replacement for self-attention, the NormLin module consistently enhances Transformer-based forecasters. The code and datasets are available at https://***/r/OLinear. © 2025, CC BY.

关键词： Fourier series

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Feature-wise and Sample-wise Adaptive Transfer Learning for High-dimensional Linear Regression

arXiv

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arXiv 2024年

作者： He, Zelin Sun, Ying Liu, Jingyuan Li, Runze Department of Statistics Pennsylvania State University United States School of Electrical Engineering and Computer Science Pennsylvania State University United States MOE Key Laboratory of Econometrics Department of Statistics and Data Science School of Economics Wang Yanan Institute for Studies in Economics Fujian Key Lab of Statistics Xiamen University China

We consider the transfer learning problem in the high dimensional linear regression setting, where the feature dimension is larger than the sample size. To learn transferable information, which may vary across features or the source samples, we propose an adaptive transfer learning method that can detect and aggregate the feature-wise (F-AdaTrans) or sample-wise (S-AdaTrans) transferable structures. We achieve this by employing a fused-penalty, coupled with weights that can adapt according to the transferable structure. To choose the weight, we propose a theoretically informed, data-driven procedure, enabling F-AdaTrans to selectively fuse the transferable signals with the target while filtering out non-transferable signals, and S-AdaTrans to obtain the optimal combination of information transferred from each source sample. We show that, with appropriately chosen weights, F-AdaTrans achieves a convergence rate close to that of an oracle estimator with known transferable structure, and S-AdaTrans recovers existing near-minimax optimal rates as a special case. The effectiveness of the proposed method is validated using both synthetic and real data, demonstrating favorable performance compared to the existing methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Transfer learning

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Integrating Appearance and Motion Cues for Visual Tracking via Spatio-Temporal Prompt

SSRN

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SSRN 2025年

作者： Jia, Xianxin Hou, ZhiQiang Wang, Yunlong Yue, Hao Hu, Shuai Ma, Sugang Yang, Xiaobao Pu, Lei School of Communications and Information Engineering School of Artificial Intelligence Xi’ an University of Posts and Telecommunications Xi’an710121 China Key Laboratory of Network Data Analysis and Intelligent Processing of Shaanxi Province Xi’an710121 China School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China Combat Support College Rocket Force University of Engineering Xi’an710025 China

Rich appearance cues in the template frame and dynamic motion cues between consecutive frames are essential for achieving robust visual tracking. However, most existing tracking methods exhibit limitations in their architectural design, often relying on a single cue while neglecting the other. This lack of integration hinders the effective interaction between different cues, thereby restricting the model’s ability to comprehensively interpret the target characteristics. To address this challenge, we propose a unified spatio-temporal prompt learning framework, ICSPTrack, which systematically captures target appearance and motion information across consecutive ***,theAppearancecueEncoderfirstextractsappearance-relatedfeatures, followed by the Motion Cue Encoder, which derives motion-related representations. These cues are subsequently integrated within the Spatio-Temporal Prompt Decoder to facilitate future reasoning by unifying different information sources. The proposed tracker was trained in an end-to-end manner and rigorously evaluated on seven benchmark datasets, including LaSOT, TNL2K, and TrackingNet. The framework achieved success rates of 72.0%, 58.5%, and 84.4% on LaSOT, TNL2K, and TrackingNet, respectively, while attaining an average overlap rate of 74.7% on GOT-10K. Despite its low computational complexity of only 28.8 GFLOPs, the model demonstrated efficient inference at 56 FPS. Experimental results indicate that the proposed method effectively handles complex tracking scenarios and achieves excellent tracking performance. © 2025, The Authors. All rights reserved.

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Adaptive CFAR Detectors for Colocated MIMO Radar in Compound Gaussian Clutter

Adaptive CFAR Detectors for Colocated MIMO Radar in Compound...

引用

International Conference on Communication Technology (ICCT)

作者： Mengjiao Tang Yao Rong Sanfeng Hu Xiaolin Du Jianbo Li Yunnan Key Laboratory of Statistical Modeling and Data Analysis Yunnan University China School of Computer and Control Engineering Yantai University China School of Communication and Information Engineering Chongqing University of Posts and Telecommunications China

This paper studies adaptive target detection in compound-Gaussian clutter for a colocated multi-input multi-output (MIMO) radar. A new one-step generalized likelihood ratio test (GLRT) is devised with all the involved unknown parameters estimated iteratively. Besides, a minor revision is made to the already existing two-step GLRT for this problem. Numerical examples show that both the two detectors are effective. For the considered simulation setup, the newly proposed detector can achieve higher detection rates than the revised one and competitors designed in Gaussian clutter scenarios.

关键词： Adaptation models MIMO radar Detectors Object detection Communications technology Numerical models Compounds

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Effect of Bi3+ and Alkali Metal Ions Doping on Luminescence Properties of Ba3Ga2O6 Phosphor

SSRN

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SSRN 2024年

作者： Deng, Chaoyong Pi, Yewei Cui, Ruirui Gong, Xinyong College of Physics Guizhou University Guizhou Guiyang550025 China Key Laboratory of Functional Composite Materials of Guizhou Province College of Big Data and Information Engineering Guizhou University Guizhou Guiyang550025 China Guizhou Academy of Sciences Guizhou Guiyang550005 China

A novel Bi3+ doped Ba3Ga2O6 phosphor was successfully synthesized by high-temperature solid-state method in this work. The results of X-ray diffraction (XRD) characterization, scanning electron microscope (SEM) characterization and energy dispersive spectrometer (EDS) characterization indicate that Bi3+ ions were doped successfully into the lattice. The phosphor has a broad emission band covering the 350 to 580 nm spectral region peaked at 416 nm under the excitation at 340 nm, due to the 3P1 → 1S0 transition of Bi3+ ions. Furthermore, when Na+ ions and K+ ions were co-doped, the emission intensity increased by 1.53 times and 1.55 times, respectively. By combining Ba3Ga2O6: Bi3+, K+, the commercial green phosphor (Ba, Sr)2SiO4: Eu2+ and the commercial red phosphor CaAlSiN3: Eu2+ with a 340 nm UV chip, a pc-white LED was fabricated successfully. And the chromaticity coordinates of the WLED is (0.3647, 0.3329), the value of Correlated Color Temperature (CCT) is 4136 K and the Color Rendering Index (CRI) = 88.4. All results indicate that Ba3Ga2O6: Bi3+ phosphor can make a great contribution to the field of NUV-based WLEDs. © 2024, The Authors. All rights reserved.

关键词： Phosphors

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The Security and Privacy of Mobile Edge Computing: An Artificial Intelligence Perspective

arXiv

引用

arXiv 2024年

作者： Wang, Cheng Yuan, Zenghui Zhou, Pan Xu, Zichuan Li, Ruixuan Wu, Dapeng Oliver Hubei Engineering Research Center on Big Data Security Key Laboratory of Distributed System Security of Hubei Province School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Software The Key Laboratory for Ubiquitous Network Service Software of Liaoning Province Dalian University of Technology Dalian116024 China Intelligent and Distributed Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science City University of Hong Kong Kowloon999077 Hong Kong

Mobile Edge Computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network’s edge. By shifting the load of cloud computing to individual local servers, MEC helps meet the requirements of ultralow latency, localized data processing, and extends the potential of Internet of Things (IoT) for end-users. However, the crosscutting nature of MEC and the multidisciplinary components necessary for its deployment have presented additional security and privacy concerns. Fortunately, Artificial Intelligence (AI) algorithms can cope with excessively unpredictable and complex data, which offers a distinct advantage in dealing with sophisticated and developing adversaries in the security industry. Hence, in this paper we comprehensively provide a survey of security and privacy in MEC from the perspective of AI. On the one hand, we use European Telecommunications Standards Institute (ETSI) MEC reference architecture as our based framework while merging the Software Defined Network (SDN) and Network Function Virtualization (NFV) to better illustrate a serviceable platform of MEC. On the other hand, we focus on new security and privacy issues, as well as potential solutions from the viewpoints of AI. Finally, we comprehensively discuss the opportunities and challenges associated with applying AI to MEC security and privacy as possible future research directions. Copyright © 2024, The Authors. All rights reserved.

关键词： Network security

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Consistency label-activated region generating network for weakly supervised medical image segmentation

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Computers in Biology and Medicine 2024年 173卷 108380-108380页

作者： Du, Wei Huo, Yongkang Zhou, Rixin Sun, Yu Tang, Shiyi Zhao, Xuan Li, Ying Li, Gaoyang Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun130012 China Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine Shanghai East Hospital Shanghai200092 China Bioinformatics Department School of Life Sciences and Technology Tongji University Shanghai200092 China

The current methods of auto-segmenting medical images are limited due to insufficient and ambiguous pathonmorphological labeling. In clinical practice, rough classification labels (such as disease or normal) are more commonly used than precise segmentation masks. However, there is still much to be explored regarding utilizing these weak clinical labels to accurately determine the lesion mask and guide medical image segmentation. In this paper, we proposed a weakly supervised medical image segmentation model to directly generate the lesion mask through a class activation map (CAM) guided cycle-consistency label-activated region transferring network. Cycle-consistency enforces that the mappings between the two domains should be reversible, which ensures that the original image can be reconstructed from the translated image. We developed a complementary branches fusion module to address the issue of blurry boundaries in CAM-guided segmentation. The complementary branch preserves the original semantic information of the non-lesion region and perfectly fuses the transferred feature of the lesion region with a complementary mask-constrained fake image generation process to clear the boundary of the lesion and non-lesion regions. This module allows the class transformation to focus solely on the label-activated region, resulting in more explicit segmentation. This model can accurately identify different region of medical images at the pixel-level while preserving the overall semantic structure semantion. It organizes disease labels and corresponding regions during image synthesis. Our method utilizes a joint discrimination strategy that significantly enhances the precision of the produced lesion mask. Extensive experiments of the proposed method on BraTs, ISIC and COVID-19 datasets demonstrate superior performance over existing state-of-the-art methods. The code and datasets are available at: https://***/mlcb-jlu/MedImgSeg. © 2024 Elsevier Ltd

关键词： Image segmentation

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ECLIPSE: Expunging Clean-label Indiscriminate Poisons via Sparse Diffusion Purification

arXiv

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arXiv 2024年

作者： Wang, Xianlong Hu, Shengshan Zhang, Yechao Zhou, Ziqi Zhang, Leo Yu Xu, Peng Wan, Wei Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Information and Communication Technology Griffith University SouthportQLD4215 Australia

Clean-label indiscriminate poisoning attacks add invisible perturbations to correctly labeled training images, thus dramatically reducing the generalization capability of the victim models. Recently, defense mechanisms such as adversarial training, image transformation techniques, and image purification have been proposed. However, these schemes are either susceptible to adaptive attacks, built on unrealistic assumptions, or only effective against specific poison types, limiting their universal applicability. In this research, we propose a more universally effective, practical, and robust defense scheme called ECLIPSE. We first investigate the impact of Gaussian noise on the poisons and theoretically prove that any kind of poison will be largely assimilated when imposing sufficient random noise. In light of this, we assume the victim has access to an extremely limited number of clean images (a more practical scene) and subsequently enlarge this sparse set for training a denoising probabilistic model (a universal denoising tool). We then introduce Gaussian noise to absorb the poisons and apply the model for denoising, resulting in a roughly purified dataset. Finally, to address the trade-off of the inconsistency in the assimilation sensitivity of different poisons by Gaussian noise, we propose a lightweight corruption compensation module to effectively eliminate residual poisons, providing a more universal defense approach. Extensive experiments demonstrate that our defense approach outperforms 10 state-of-the-art defenses. We also propose an adaptive attack against ECLIPSE and verify the robustness of our defense scheme. Our code is available at https://***/CGCL-codes/ECLIPSE. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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Deep Generative Network for Image Inpainting with Gradient Semantics and Spatial-Smooth Attention

SSRN

引用

SSRN 2023年

作者： Sheng, Ziqi Xu, Wenbo Lin, Cong Lu, Wei School of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Guangzhou510006 China Applied Laboratory of Dig Data and Education Statistics School of Statistics and Mathematics Guangdong University of Finance and Economics Guangzhou510006 China

As a powerful means of tampering in image content security area, image inpainting based on deep generative networks can yield visually appealing outputs but often produces ambiguous artifacts, particularly in boundary and high semantic areas. To address this issue, a novel end-to-end network with gradient semantics and spatial-smooth attention (GS-SSA) is proposed, which combines a gradient learning network and an image inpainting network. The gradient learning network is meant to properly anticipate the gradient semantics in the hole region and get a complete gradient semantic map. The image inpainting network utilizes the complete gradient semantic map to better repair the missing pixels and obtain the final inpainting results. Moreover, spatial-smooth attention is introduced into the image inpainting network, considering the spatial structural relations and the surrounding information in the hole region respectively. © 2023, The Authors. All rights reserved.

关键词： Semantics

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A Survivable Routing Algorithm with Hybrid Protection in Elastic Optical Networks

A Survivable Routing Algorithm with Hybrid Protection in Ela...

引用

Mobile Internet, Cloud Computing and Information Security (MICCIS), International Conference on

作者： Hankun Zeng Xu Zhang Chuan Feng Xiaoxue Gong School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China Institute of Intelligent Communication and Network Security Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350389906

ISBN: (纸本)9798350389913

Spectral efficiency is a crucial factor in the survivability of multi-class traffic in elastic optical networks (EONs). In this paper, we propose a spectrum-efficient hybrid protection scheme, namely dedicated path protection (DPP) and shared backup path protection (SBPP), to address the survivability issue in EONs. Meanwhile, we design a survivable routing, modulation level, and spectrum assignment (RMLSA) algorithm to achieve hybrid protection in EONs. The simulation results demonstrate that the proposed algorithm has a lower blocking rate and Spectrum occupation ratio.

关键词： Time-frequency analysis Spectral efficiency Simulation Modulation Information security Optical fiber networks Routing

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