检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Jiang, Feibo Tang, Chuanguo Dong, Li Wang, Kezhi Yang, Kun Pan, Cunhua Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China School of Information Science and Engineering Hunan Normal University Changsha China Changsha Social Laboratory of Artificial Intelligence Hunan University of Technology and Business Changsha China The Department of Computer Science Brunel University London United Kingdom The School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom Changchun Institute of Technology China The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China

Semantic Communication (SC) has emerged as a novel communication paradigm in recent years, successfully transcending the Shannon physical capacity limits through innovative semantic transmission concepts. Nevertheless, extant Image Semantic Communication (ISC) systems face several challenges in dynamic environments, including low semantic density, catastrophic forgetting, and uncertain Signal-to-Noise Ratio (SNR). To address these challenges, we propose a novel Vision-Language Model-based Cross-modal Semantic Communication (VLM-CSC) system. The VLM-CSC comprises three novel components: (1) Cross-modal Knowledge Base (CKB) is used to extract high-density textual semantics from the semantically sparse image at the transmitter and reconstruct the original image based on textual semantics at the receiver. The transmission of high-density semantics contributes to alleviating bandwidth pressure. (2) Memory-assisted Encoder and Decoder (MED) employ a hybrid long/short-term memory mechanism, enabling the semantic encoder and decoder to overcome catastrophic forgetting in dynamic environments when there is a drift in the distribution of semantic features. (3) Noise Attention Module (NAM) employs attention mechanisms to adaptively adjust the semantic coding and the channel coding based on SNR, ensuring the robustness of the CSC system. The experimental simulations validate the effectiveness, adaptability, and robustness of the CSC system. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Multiscale fast correlation filtering tracking algorithm based on a feature fusion model

Multiscale fast correlation filtering tracking algorithm bas...

引用

作者： null null null null null null null School of Computer and Communication Engineering and Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Changsha China School of Information Science and Engineering Fujian University of Technology Fujian China Hunan Valin Lianyuan Iron & Steel Corporation Loudi China Electronics & Information School Yangtze University Jingzhou China Hunan Institute of Scientific and Technical Information Changsha China Technical Quality Department Hunan ZOOMLION Heavy Industry Intelligent Technology Corporation Limited Changsha China

关键词： Object tracking

来源：评论

学校读者我要写书评

暂无评论

The Vlasov-Yukawa-Boltzmann system without angular cutoff near vacuum

引用

Journal of Differential Equations 2025年 443卷

作者： Xianshen Hu Linjie Xiong Wenzheng Zhu School of Mathematics Hunan University Changsha 410082 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Applied Mathematics Hunan University Changsha 410082 China

We investigate the global nonlinear stability of the non-cutoff Vlasov-Yukawa-Boltzmann (VYB) system with moderately soft potentials γ + 2 s ∈ ( 0 , 2 ) and all physically relevant singularity parameter s ∈ ( 0 , 1 ) . Building upon the foundational framework established in Chaturvedi's work [Ann. PDE, 7(2021), no.2, Paper No. 15, 104 pp.], we demonstrate the global nonlinear stability of the VYB system near vacuum by leveraging space-time weights and employing commuting vector fields to achieve integrable time decay for the nonlinear terms. This extends the existence result in Choi and Ha's work [Acta Math. Sci. Ser. B (Engl. Ed.) 35 (2015), no. 4, 887–905.] to angular non-cutoff cases.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Transfer learning-based few-shot sample deep learning-assisted template attacks

引用

Physical Communication 2025年 72卷

作者： Zhiying Feng Lang Li Yu Ou Lianrui Deng College of Computer Science and Technology Hengyang Normal University Hengyang 421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal University Hengyang 421002 China

Deep learning-assisted template attack (DLATA) is a novel side-channel attack (SCA) method proposed by Lichao Wu at CHES2022. It utilizes a triplet network to assist template attacks (TA), avoiding the redundant training and hyperparameter tuning required in traditional DL-based SCA methods. However, the training of the triplet network requires a large number of power samples due to its unique structure. We propose a new optimization scheme, in which the transfer learning (TL) technology is used to train multiple models on several similar datasets with fewer power traces, to mitigation the problem. The approach allows us to leverage pre-trained models to product a new mode on the another target dataset by fine-tuning weights so that significantly reduce the training cost for the triplet network while maintaining attack effectiveness. We remould the structure and dimensionality of similar datasets so that the models trained on them can perform effective transfer learning for training on the target dataset. Concretely, some of parameters and features obtained from pretraining can be used directly for the target task, while the rest only require fine-tuning. Evaluation and experimental validation on the public ASCAD dataset demonstrate that our method achieves or even surpasses the performance of the original method with a 90% reduction in the training set. These findings highlight the effectiveness of the proposed TL strategy in achieving robust attack performance in low-sample training environments.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Higher order log-concavity of the overpartition function and its consequences

arXiv

引用

arXiv 2022年

作者： Mukherjee, Gargi Zhang, Helen W.J. Zhong, Ying Institute for Algebra Johannes Kepler University Altenberger Straße 69 LinzA-4040 Austria School of Mathematics Hunan University Changsha410082 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Applied Mathematics Changsha410082 China

Let p(n) denote the overpartition function. In this paper, we study the asymptotic higher order log-concavity property of the overpatition function in a similar framework done by Hou and Zhang for the partition function. This will enable us to move on further in order to prove log-concavity of overpartitions, explicitly by studying the asymptotic expansion of the quotient (Equation presented) upto a certain order so that one can finally ends up with the phenomena of 2-log-concavity and higher order Turán property of p(n) by following a sort of unified *** Codes 05A20, 11N37, 65G99 © 2022, CC BY.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An Enhanced Flower Pollination Algorithm for Solving Optimal Power Flow Problem

SSRN

引用

SSRN 2022年

作者： Zhong, keyu Xiao, Fen Gao, Xieping Key Laboratory of Computing and Stochastic Mathematics of Ministry of Education Hunan Normal University Changsha410081 China School of Mathematics and Statistics Hunan Normal University Changsha410081 China School of Computer Science Xiangtan University Hunan Xiangtan411105 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China

The OPF system for renewable energy power has presented characteristics such as extremely high security constraints and more obvious multimodal properties, its solution is a challenging task and requires more efficient methods. In this paper, an enhanced flower pollination algorithm, EFPA-SF is proposed to solve the OPF problem. In EFPA-SF, there are four improvements: firstly, a random step size in the process of cross-pollination is proposed to improve the global search ability of EFPA-SF. Secondly, in the stage of self-pollination, the global optimal pollen is introduced to improve the ability of exploitation–exploration balance of the algorithm. In addition, if pollen individual crosses boundaries, boundary constraint is carried out by the previous position and boundary position of pollen, which is instrumental to escape from local optimum. Finally, the superiority of feasible solutions (SF) constraints handling method is integrated into the algorithm to satisfy the feasibility of the solutions. To verify the effectiveness of the proposed algorithm, the optimization capability of EFPA is first investigated and analyzed by using the most recent IEEE CEC2021 benchmark functions. Then, EFPA-SF is used to solve three engineering design problems and the performance is compared with the state-of-the-art algorithms, the validation results reveal that the proposed algorithm outperforms state-of-the-art algorithms, and demonstrate that it can be used for constrained optimization as well as for unconstrained optimization problems with good generality. Finally, EFA-SF is applied to the OPF problem on a modified IEEE-30 bus system that incorporates stochastic wind, solar, and small hydro power in addition to conventional thermal power generation. The comprehensive investigation reveals that EFA-SF can provide competitive solutions to the OPF problem compared to popular algorithms. © 2022, The Authors. All rights reserved.

关键词： Constraint handling

来源：评论

学校读者我要写书评

暂无评论

CA-MLIF: Cross-Attention and Multimodal Low-Rank Interaction Fusion Framework for Tumor Prognostic Prediction 39

CA-MLIF: Cross-Attention and Multimodal Low-Rank Interaction...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： An, Yajun Chen, Jiale Lin, Huan Liu, Zhenbing Feng, Siyang Zhang, Hualong Lan, Rushi Liu, Zaiyi Pan, Xipeng School of Computer Science and Information Security Guilin University of Electronic Technology Guilin 541004 China School of Artificial Intelligence and Big Data Chongqing College of Finance and Economics Chongqing 402160 China Department of Radiology Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences) Southern Medical University Guangzhou 510080 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin 541004 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou 510080 China

ISBN: (纸本)157735897X

Cancer is a leading cause of death worldwide due to its aggressive nature and complex variability. Accurate prognosis is therefore challenging but essential for guiding personalized treatment and follow-up. Previous research often relied on single data sources, missing the opportunity to combine various types of patient information for more comprehensive survival predictions. To address these challenges, we propose a two-stage fusion method named Cross-Attention and Multimodal Low-Rank Interaction Fusion Framework (CA-MLIF). In the first stage, we propose a CA mechanism for real-time feature updates and cross-modal mutual learning to capture rich semantic information. In the second stage, we design a novel multimodal low-rank interaction fusion method for survival prediction. Specifically, we present modal attention mechanism (MAM) for feature filtration, low-rank multimodal fusion (LMF) for model complexity reduction, and optimal weight concatenation (OWC) for maximizing feature integration. Extensive experiments on two public datasets TCGA-GBMLGG and TCGA-KIRC, as well as a multi-center in-house lung adenocarcinoma (LUAD) dataset validate the effectiveness of CA-MLIF, which demonstrate that our method outperforms existing approaches in survival prediction under both pathology-gene fusion and CT-pathology fusion scenarios. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Learning to rank

来源：评论

学校读者我要写书评

暂无评论

Improved Bohr inequality for harmonic mappings

arXiv

引用

arXiv 2021年

作者： Liu, Gang Ponnusamy, Saminathan College of Mathematics and Statistics Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal University HengyangHunan421002 China Department of Mathematics Indian Institute of Technology Madras Chennai600 036 India

Based on improving the classical Bohr inequality, we get in this paper some refined versions for a quasi-subordination family of functions, one of which is key to build our results. By means of these investigations, for a family of harmonic mappings defined in the unit disk D, we establish an improved Bohr inequality with refined Bohr radius under particular conditions. Along the line of extremal problems concerning the refined Bohr radius, we derive a series of results. Here the family of harmonic mappings have the form f = h + g, where g(0) = 0, the analytic part h is bounded by 1 and that |g′(z)| ≤ k|h′(z)| in D and for some k ∈ [0, 1].MSC Codes Primary: 30A10, 30B10, 30H05, 31A05, 30C62, 30C80, Secondary: 30C35, 30C45 Copyright © 2021, The Authors. All rights reserved.

关键词： Harmonic analysis

来源：评论

学校读者我要写书评

暂无评论

BGRA-Net: Boundary-Guided and Region-Aware Convolutional Neural Network for the Segmentation of Breast Ultrasound Images

BGRA-Net: Boundary-Guided and Region-Aware Convolutional Neu...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiang Zhang Xuanya Li Kai Hu Xieping Gao Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan China Baidu Inc. Beijing China Key Laboratory of Medical Imaging and Artifical Intelligence of Hunan Province Xiangnan University Chenzhou China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China

ISBN: (纸本)9781665429825

In this paper, we propose a novel convolutional neural network based on boundary-guided and region-aware (BGRA-Net) for breast tumor segmentation in ultrasound images. In particular, in the encoding stage, we propose a boundary-guided module (BGM) to guide the learning of boundary features in the decoding stage by explicitly strengthening the extracted boundary information. Meanwhile, in the decoding stage, we propose a region-aware module (RAM) to integrate different levels of detailed and semantic features to improve the comprehensive representation of tumor regional features. Besides, a scale-adaptive module (SAM) is further proposed to capture the characteristics of tumors with different sizes between the encoding and decoding stages. To evaluate the effectiveness of our BGRA-Net, we conduct extensive experiments on the UDIAT dataset and compare it with eight state-of-the-art methods. The experimental results show that our BGRA-Net outperforms the state-of-the-art methods and can achieve accurate segmentation of breast tumors with ambiguous boundaries.

关键词： Deep learning Image segmentation Breast tumors Ultrasonic imaging Semantics Feature extraction Visual effects

来源：评论

学校读者我要写书评

暂无评论

Efficiently Retrieving Differences between Remote Sets using Counting Bloom Filter

引用

International Journal of Performability Engineering 2019年第7期15卷 1947-1954页

作者： Tian, Xiaomei Zhao, Huihuang Sun, Yaqi Liang, Xiaoman College of Computer Science and Technology Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China

Retrieving differences between remote sets is widely used in set reconciliation and data deduplication. Set reconciliation and data deduplication between two nodes are widely used in various network applications. The basic idea of the difference retrieving problem is that each member of a node pair has an object set and seeks to find all differences between the two remote sets. There are many methods for retrieving difference sets, such as the standard Bloom filter (SBF), counting Bloom filter (CBF), and invertible Bloom filter (IBF). In these methods, based on the standard Bloom filter or its variants, each node represents its objects using a standard Bloom filter or other Bloom filter, which is then exchanged. A receiving node retrieves different objects between the two sets according to the received SBF, CBF, or IBF. We propose a new algorithm for retrieving differences that finds differences between remote sets using counting Bloom filters' deletion operation. The theoretical analyses and experimental results show that the differences can be retrieved efficiently. Only a very small number of differences are missing in the retrieving process, and this false negative rate can be decreased to 0% by adjusting the counting Bloom filter's parameters. © 2019 Totem Publisher, Inc. All rights reserved.

关键词： Data structures

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：