检索结果-内蒙古大学图书馆

Anomalous Hall effects in magnetic weak topological insulator films

Physical Review B 2025年第4期111卷 045409-045409页

作者： Rui Chen Xiao-Xia Yi Bin Zhou Dong-Hui Xu Department of Physics Hubei University Wuhan 430062 China Key Laboratory of Intelligent Sensing System and Security of Ministry of Education Hubei University Wuhan 430062 China Department of Physics and Chongqing Key Laboratory for Strongly Coupled Physics Chongqing University Chongqing 400044 China Center of Quantum Materials and Devices Chongqing University Chongqing 400044 China

The interplay between magnetism and strong topological insulator gives rise to distinct new topological phases and various intriguing phenomena, attracting significant attention in recent years. However, magnetic effects in weak topological insulators remain largely unexplored. In this work, we systematically investigate the magnetic effect on thin films of weak topological insulators. We focus on ferromagnetic and antiferromagnetic effects, which have been extensively studied in strong topological insulators, as well as the recently highlighted altermagnetic effect. We reveal that the interplay between magnetism and weak topological insulators leads to a variety of Hall effects in the absence of an external magnetic field, including the metallic quantum anomalous Hall effect without chiral edge states, the quantum anomalous Hall effect with a higher Hall conductance plateau, the quantized layer Hall effect, the metallic half-quantized valleylike Hall effect, and a quantized valleylike Hall effect. This work provides valuable insights for exploring magnetic effect on weak topological insulators.

关键词： Topological insulators

来源：评论

学校读者我要写书评

暂无评论

Federated Hybrid-Supervised Learning for Universal Medical Image Segmentation

Federated Hybrid-Supervised Learning for Universal Medical I...

引用

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

作者： Shenhai Zheng Sian Wen Congyu Li Qing Chen Laquan Li College of Computer Science and Technology Chongqing University of Posts and Telecommunications China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education China School of Science Chongqing University of Posts and Telecommunications China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Federated Learning (FL) is an advanced technology that tackles the challenge of blocked data arising from privacy concerns, enabling the training of deep learning models without the need for data sharing. However, FL faces difficulties with heterogeneous data and limited annotations in medical image segmentation. Motivated by this discovery, this study proposes a novel hybrid-supervised federated learning method (FedSLAG) that explores various types of annotations in medical imaging. To focus more on weakly-supervised and unsupervised scenarios within hybrid-supervised learning, a federated Gaussian enhancement module was proposed for heterogeneous sparse annotations (points and scribbles). The feature extraction module combines the features of multiple weakly-supervised clients and establishes the correlation of similar pixels, thus making up for the deficiency of scarce annotations and the insufficient feature extraction capability of a single machine. Then, a two-stage broadcast mechanism based on supervision sparsity was proposed to alleviate optimization deviation in local models. Experiments on breast tumor and skin lesion segmentation tasks demonstrate significant efficiency gains as well as highly competitive segmentation accuracy in many hybrid-supervised situations. Our codes are available at: https://***/TrivenDev/FedSLAG.

关键词： Training Image segmentation Breast tumors Annotations Federated learning Feature extraction Skin Lesions Speech processing Biomedical imaging

来源：评论

学校读者我要写书评

暂无评论

Mamba Adapter: Efficient Multi-Modal Fusion for Vision-Language Tracking

引用

IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Shi, Liangtao Zhong, Bineng Liang, Qihua Hu, Xiantao Mo, Zhiyi Song, Shuxiang Ministry of Education Guangxi Normal University Key Laboratory of Education Blockchain and Intelligent Technology Guilin541004 China Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining and Security Guilin541004 China Wuzhou University School of Data Science and Software Engineering Wuzhou543002 China

Utilizing the high-level semantic information of language to compensate for the limitations of vision information is a highly regarded approach in single-object tracking. However, most existing vision-language (VL) trackers employ full-parameter fine-tuning, which can easily lead to catastrophic forgetting. Therefore, they fail to fully exploit the prior knowledge of pre-trained models from upstream tasks, resulting in unsatisfactory tracking performance. To alleviate the above problem, we propose a simple yet effective Vision-Language Tracking pipeline based on Mamba Adapter, named MAVLT, which adopts the idea of parameter-efficient fine-tuning (PEFT) to realize the interaction between vision-language modalities. This novel approach offers the following advantages: (1)The knowledge of the upstream pre-trained model is efficiently inherited by freezing its parameters. This ensures that the VL tracking framework only learns the modules for vision and language interaction, with a focus on the fusion between modalities. (2)The modal interaction between language and vision encoders is flexibly bridged in each encoder layer via proposed mamba adapter, enabling efficient interaction of visual and language information at multiple levels. Extensive experiments on five popular vision-language tracking benchmarks validate the effectiveness of the proposed MAVLT. Particularly, the MAVLT achieves 73.4% AUC score on the LaSOT benchmarks with only 0.18%(0.32M) of the total parameters updates. Code and models are available at https://***/GXNU-ZhongLab/MAVLT. © 1991-2012 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Correlation effects on topological structure in complex network evolution

引用

Physica A: Statistical Mechanics and its Applications 2025年 671卷

作者： Chen, Xiaojie Qiao, Weile Pan, Guijun School of physics Hubei University Hubei Wuhan430062 China Key Laboratory of Intelligent Sensing System and Security Hubei University Ministry of Education Hubei Wuhan430062 China

Network growth model is an important and popular research topic aimed at uncovering the mechanisms of network formation and evolution. Most growth models focus on two key mechanisms: growth and preferential attachment without directly accounting for the correlation effects, existing in many realistic network systems. Here, we propose a preferential correlation growth model by introducing a proximity parameter that controls the proximity between nodes connected to newly added nodes. The smaller the proximity parameter, the shorter the distance between the selected nodes, and the stronger the correlation between them. When the proximity parameter approaches infinity, our model reduces to the BA model. By comparing simulations with different proximity parameter, we find that a high degree of correlation significantly enhances the robustness, both local and global efficiency of the network. Besides, our model demonstrates high clustering coefficient, short average path length, and richer cyclic structures, which are closer to real-world networks. Furthermore, our network model effectively addresses the low clustering coefficient issue of the BA model and provides a better understanding of the cyclic structures in real-world networks. Specially, we find that on the mesoscopic time scale, the correlation effects can significantly improve thermodynamic efficiency from a statistical perspective. Our work shows that correlation should be another key mechanism of network formation in addition to the preferential attachment, deserved to be considered for better understanding of the structure and function of the network. © 2025 Elsevier B.V.

关键词： Growth temperature

来源：评论

学校读者我要写书评

暂无评论

Cryptanalysis of BAKSHEESH Block Cipher 20th

Cryptanalysis of BAKSHEESH Block Cipher

引用

20th International Conference on Information security and Cryptology, Inscrypt 2024

作者： Xu, Shengyuan Chen, Siwei Feng, Xiutao Xiang, Zejun Zeng, Xiangyong Department of Fundamental Courses Shandong University of Science and Technology Shandong Taian China School of Cyber Science and Technology Hubei University Hubei Wuhan China Key Laboratory of Mathematics Mechanization Academy of Mathematics and Systems Science Beijing China Faculty of Mathematics and Statistics Hubei Key Laboratory of Applied Mathematics Hubei University Hubei Wuhan China Key Laboratory of Intelligent Sensing System and Security Ministry of Education Hubei University Hubei Wuhan China

ISBN: (纸本)9789819647330

BAKSHEESH is a lightweight block cipher following up the well-known cipher GIFT-128, which uses a 4-bit SBox that has a non-trivial Linear Structure (LS). Also, the Sbox requires a low number of AND gates that makes BAKSHEESH stronger to resist the side channel attacks compared to GIFT-128. In this paper, we give the first third-party security analysis of BAKSHEESH from the traditional attacks perspective: integral, differential and linear attacks. Firstly, we propose a framework for integral attacks based on the properties of BAKSHEESH ’s Sbox and its inverse. By this, we achieve the 9- and 10-round practical key-recovery attacks, and give a 15-round theoretical attack. Secondly, we re-evaluate the security bound against differential cryptanalysis, correcting two errors from the original paper and presenting a key-recovery attack for 19 rounds. At last, for linear cryptanalysis, we develop an automated model for key-recovery attacks and then demonstrate a key-recovery attack for 21 rounds. We stress that our attacks cannot threaten the full-round BAKSHEESH, but give a deep understanding on its security. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： BAKSHEESH key-recovery Attacks Lightweight Block Cipher security Evaluation

来源：评论

学校读者我要写书评

暂无评论

Efficiency Boosting of Secure Cross-Platform Recommender systems Over Sparse data

引用

IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2146-2160页

作者： Ren, Hao Xu, Guowen Zhang, Tianwei Ning, Jianting Huang, Xinyi Li, Hongwei Lu, Rongxing School of Cyber Science and Engineering Sichuan University Chengdu610017 China State Key Laboratory of Cryptology Beijing100878 China Key Laboratory of Data Protection and Intelligent Management Sichuan University Ministry of Education Chengdu610017 China Cyber Science Research Institute Sichuan University Chengdu610017 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Cyber Science and Engineering Wuhan University Wuhan430072 China Faculty of Data Science City University of Macau 999078 China Colledge of Cyber Security Jinan University Guangzhou510632 China School of Computer Science University of New Brunswick New BrunswickE3B 5A3 Canada

Fueled by its successful commercialization, the recommender system (RS) has gained widespread attention. However, as the training data fed into the RS models are often highly sensitive, it ultimately leads to severe privacy concerns, especially when data are shared among different platforms. In this paper, we follow the tune of existing works to investigate the problem of secure sparse matrix multiplication for cross-platform RSs. Two fundamental and critical issues are addressed: preserving the training data privacy and breaking the data silo problem. Specifically, we propose two concrete constructions with significantly boosted efficiency. They are designed for the sparse location insensitive case and location sensitive case, respectively. State-of-the-art cryptography building blocks including homomorphic encryption (HE) and private information retrieval (PIR) are fused into our protocols with non-trivial optimizations. As a result, our schemes can enjoy the HE acceleration technique without privacy trade-offs. We give formal security proofs for the proposed schemes and conduct extensive experiments on both real and large-scale simulated datasets. Compared with state-of-the-art works, our two schemes compress the running time roughly by 10×10× and 2.8×2.8×. They also attain up to 15×15× and 2.3×2.3× communication reduction without accuracy loss. © 2024 IEEE.

关键词： Differential privacy

来源：评论

学校读者我要写书评

暂无评论

A Cross-Scene Few-Shot Learning Based on Intra–Inter Domain Contrastive Alignment for Hyperspectral Image Change Detection

引用

IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Hou, Wenhui Peng, Jiangtao Yang, Bing Wu, Lanxin Sun, Weiwei Hubei University Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and Statistics Wuhan430062 China Hubei University Key Laboratory of Intelligent Sensing System and Security Ministry of Education Wuhan430062 China China Jiliang University College of Sciences Hangzhou310018 China Ningbo University Ningbo Key Laboratory of Remote Sensing and Ecological Security of Coastal Zone Department of Geography and Spatial Information Techniques Ningbo315211 China

Recently, deep neural networks have demonstrated outstanding performance in hyperspectral image (HSI) change detection (CD), especially when there are sufficient labeled samples. However, the labels of HSI are difficult to obtain, and acquiring enough labels to train deep network is a great challenge in practice. Therefore, to mitigate the effect of insufficient labels in detection results, this article proposes a cross-scene few-shot learning (FSL) network based on intra-inter domain contrastive alignment (CAFSL) for HSI-CD, which combines contrastive learning (CL) and FSL into a unified framework, aiming to achieve better detection results using only a few labeled samples. Specifically, we perform cross-scene FSL using a pair of dual-phase images from a very high-resolution image (VHRI) as the source domain and a pair of HSI as the target domain. Then, an intra-domain supervised CL (INSCL) module is designed to enhance the compactness within classes and widen the discrimination between classes by maximizing the feature similarity of intra-class and minimizing the feature similarity of inter-class. Finally, a cross-domain contrastive alignment (CRCA) module is proposed to align the features of source and target domains, which mitigates the effect of domain migration problems caused by different data types. Experiments on three HSI benchmark datasets reveal that the CAFSL algorithm outperforms current advanced algorithms based on deep learning (DL) and FSL while with limited labeled samples. © 1980-2012 IEEE.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

DMSACNN: Deep Multiscale Attentional Convolutional Neural Network for EEG-Based Motor Decoding

引用

IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Liu, Ke Xing, Xin Yang, Tao Yu, Zhuliang Xiao, Bin Wang, Guoyin Wu, Wei Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing400065 China Chongqing University of Posts and Telecommunications Key Laboratory of Big Data Intelligent Computing China Chongqing University of Posts and Telecommunications Key Laboratory of Cyberspace Big Data Intelligent Security of Ministry of Education China South China University of Technology College of Automation Science and Engineering Guangzhou510641 China Chongqing Normal University National Center for Applied Mathematics in Chongqing Chongqing401331 China Shanghai Jiao Tong University School of Medicine Songjiang Hospital Songjiang Research Institute Shanghai Key Laboratory of Emotions and Affective Disorders Shanghai201600 China

Objective: Accurate decoding of electroencephalogram (EEG) signals has become more significant for the brain-computer interface (BCI). Specifically, motor imagery and motor execution (MI/ME) tasks enable the control of external devices by decoding EEG signals during imagined or real movements. However, accurately decoding MI/ME signals remains a challenge due to the limited utilization of temporal information and ineffective feature selection methods. Methods: This paper introduces DMSACNN, an end-to-end deep multiscale attention convolutional neural network for MI/ME-EEG decoding. DMSACNN incorporates a deep multiscale temporal feature extraction module to capture temporal features at various levels. These features are then processed by a spatial convolutional module to extract spatial features. Finally, a local and global feature fusion attention module is utilized to combine local and global information and extract the most discriminative spatiotemporal features. Main results: DMSACNN achieves impressive accuracies of 78.20%, 96.34% and 70.90% for hold-out analysis on the BCI-IV-2a, High Gamma and OpenBMI datasets, respectively, outperforming most of the state-of-the-art methods. Conclusion and significance: These results highlight the potential of DMSACNN in robust BCI applications. Our proposed method provides a valuable solution to improve the accuracy of the MI/ME-EEG decoding, which can pave the way for more efficient and reliable BCI systems. The source code for DMSACNN is available at https://***/xingxin-99/***. © 2025 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

SFma-Unet: A Mamba-Based Spatial-Frequency Fusion Network for Medical Image Segmentation

SFma-Unet: A Mamba-Based Spatial-Frequency Fusion Network fo...

引用

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

作者： Zhanpeng Liu Yuqiang Zhang Bin Wang Yang Yang Lin Cai Institute of Artificial Intelligence Xiamen University Xiamen China Key Laboratory of Intelligent Sensing System and Security (Ministry of Education) School of Artificial Intelligence Hubei University Wuhan China School of Life Sciences Hubei University Wuhan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recently, Mamba-based methods have gained popularity in medical image segmentation due to their ability to model long-range dependencies with linear computational complexity. However, current segmentation methods often face challenges such as low contrast, blurred boundaries, and unclear backgrounds in medical images. Considering that perceived objects and features exhibit greater discriminative power in the frequency domain, we propose a novel mamba-based spatial and frequency domain feature fusion network, SFMa-Unet, to address these challenges. Specifically, we designed the Spatial-Frequency Interaction (SFI) module, which leverages the powerful modeling capabilities of Mamba to fuse spatial and frequency domain features, enhancing feature representation. Additionally, we developed a Mamba-based multi-scale feature channel fusion (MFCF) bridge to capture local and global dependencies across different feature scales, further improving the model’s representational capacity. We conduct comprehensive experiments on the ISIC17 and ISIC18 public datasets. Experimental results demonstrate the effectiveness and robustness of SFMa-Unet. Codes are available at https://***/RainCh-zyq/SFma-Unet.

关键词： Bridges Image segmentation Solid modeling Three-dimensional displays Frequency-domain analysis Semisupervised learning Signal processing Robustness Speech processing Biomedical imaging

来源：评论

学校读者我要写书评

暂无评论

T³Planner: Multi-Phase Planning Across Structure-Constrained Optical, IP, and Routing Topologies

引用

IEEE Journal on Selected Areas in Communications 2025年第5期43卷 1823-1839页

作者： Yijun Hao Shusen Yang Fang Li Yifan Zhang Cong Zhao Xuebin Ren Peng Zhao Chenren Xu Shibo Wang National Engineering Laboratory for Big Data Analytics Xi’an Jiaotong University Xi’an China National Engineering Laboratory for Big Data Analytics and the Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China School of Computer Science Peking University Beijing China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China National Engineering Laboratory for Big Data Analytics XiÃan Jiaotong University XiÃan China

Network topology planning is an essential multi-phase process to build and jointly optimize the multi-layer network topologies in wide-area networks (WANs). Most existing practices target single-phase/layer planning, and are incapable of satisfying all rigorous topological structure constraints (e.g., dual-homing rings) defined by network standards and operators, especially in large-scale networks. These significantly limit their usability and performance in production networks. We consider a general topology planning problem with typical structure constraints over three essential phases (greenfield, reconfiguration, and site expansion) and topological layers (optical, IP, and routing topologies). We present, T3Planner, a novel practical solver to this problem in production. Specifically, we develop a structure-driven encoder based on graph neural network (GNN) for concise structure encoding, and design a new learning framework with optical-centric layer compression/reconstruction and rule-aided reinforcement learning (RL) for fast convergence and high performance. Extensive experiments on nine real topologies demonstrate that T3Planner scales to large optical networks with hundreds of sites, saves 46.6% cost, and supports $3.12\times $ more demand when compared to related existing approaches.

关键词： Topology Network topology Planning IP networks Optical fiber networks Routing Optical fibers Optical feedback Production Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：