检索结果-内蒙古大学图书馆

On the Existence and Asymptotic Stability of Hybrid Stochastic Systems with Neutral Term and Non-Differentiable Time Delay

学校读者我要写书评

暂无评论

SSRN

SSRN 2023年

作者： Mao, Wei Chen, Bo Wu, Fuke Mao, Xuerong School of Mathematical Science Jiangsu Province Engineering Research Center of the Elementary Eduction and Big Data Jiangsu Second Normal University Nanjing210013 China School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan430074 China Department of Mathematics and Statistics University of Strathclyde GlasgowG1 1XH United Kingdom

This paper focuses on a class of hybrid stochastic delay systems with neutral term (HSDSwNT), where the time delay is non-differentiable and the coefficients do not satisfy the linear growth condition. Under the local Lipschitz condition and the Hasminskii-type condition, the existence and uniqueness as well as boundedness of the solution to HSDSwNT are established. By virtue of the Lyapunov function and M-matrix theory, sufficient conditions on the asymptotic stability and general decay stability are also established for HSDSwNT. Finally, two examples are given to show the effectiveness of our results. © 2023, The Authors. All rights reserved.

关键词： Time delay

TV-SAM: Increasing Zero-Shot Segmentation Performance on Multimodal Medical Images Using GPT-4 Generated Descriptive Prompts Without Human Annotation

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Jiang, Zekun Cheng, Dongjie Qin, Ziyuan Gao, Jun Lao, Qicheng Ismoilovich, Abdullaev Bakhrom Gayrat, Urazboev Elyorbek, Yuldashov Habibullo, Bekchanov Tang, Defu Wei, LinJing Li, Kang Zhang, Le The College of Computer Science Sichuan University Sichuan Chengdu China The West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu China Case Western Reserve University Cleveland United States School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China The Urgench State University Urgench Uzbekistan College of Animal Science and Technology Gansu Agricultural University Lanzhou China College of Information Science and Technology Gansu Agricultural University Lanzhou China

This study presents a novel multimodal medical image zero-shot segmentation algorithm named the text-visual-prompt segment anything model (TV-SAM) without any manual annotations. The TV-SAM incorporates and integrates the large language model GPT-4, the vision language model GLIP, and the SAM to autonomously generate descriptive text prompts and visual bounding box prompts from medical images, thereby enhancing the SAM's capability for zero-shot segmentation. Comprehensive evaluations are implemented on seven public datasets encompassing eight imaging modalities to demonstrate that TV-SAM can effectively segment unseen targets across various modalities without additional training. TV-SAM significantly outperforms SAM AUTO (P Copyright © 2024, The Authors. All rights reserved.

关键词： Image segmentation

Graph Adaptive Semantic Transfer for Cross-domain Sentiment Classification

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Zhang, Kai Zhang, Kun Liu, Qi Huang, Zhenya Zhang, Mengdi Wu, Wei Cheng, Mingyue Chen, Enhong Anhui Province Key Lab. of Big Data Analysis and Application University of S&T of China State Key Laboratory of Cognitive Intelligence Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Meituan Beijing China Anhui Province Key Lab. of Big Data Analysis and Application University of S&T of China Hefei China

Cross-domain sentiment classification (CDSC) aims to use the transferable semantics learned from the source domain to predict the sentiment of reviews in the unlabeled target domain. Existing studies in this task attach more attention to the sequence modeling of sentences while largely ignoring the rich domain-invariant semantics embedded in graph structures (i.e., the part-of-speech tags and dependency relations). As an important aspect of exploring characteristics of language comprehension, adaptive graph representations have played an essential role in recent years. To this end, in the paper, we aim to explore the possibility of learning invariant semantic features from graph-like structures in CDSC. Specifically, we present Graph Adaptive Semantic Transfer (GAST) model, an adaptive syntactic graph embedding method that is able to learn domain-invariant semantics from both word sequences and syntactic graphs. More specifically, we first raise a POS-Transformer module to extract sequential semantic features from the word sequences as well as the part-of-speech tags. Then, we design a Hybrid Graph Attention (HGAT) module to generate syntax-based semantic features by considering the transferable dependency relations. Finally, we devise an Integrated aDaptive Strategy (IDS) to guide the joint learning process of both modules. Extensive experiments on four public datasets indicate that GAST achieves comparable effectiveness to a range of state-of-the-art models. © 2022, CC BY.

关键词： Sentiment analysis

Limiting the deviation incentives in resource sharing networks 19

学校读者我要写书评

暂无评论

Limiting the deviation incentives in resource sharing networ...

19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020

作者： Cheng, Yukun Deng, Xiaotie Li, Yuhao Suzhou Key Laboratory for Big Data and Information Service School of Business Suzhou University of Science and Technology China Center of Frontier of Computer Studies Department of Computer Science and Technology Peking University China

ISBN: (纸本)9781450375184

Crowdsourcing, Peer-to-Peer (P2P) resource networks and sharing economies have created a sequence of new waves [11, 13, 16], to explore the power to match the needs of users and supplies from providers through the Internet and mobile networks [7, 10, 15]. To motivate sharing, [12] pioneered the use of incentive techniques to drive cooperation and to promote voluntary contributions by participating agents. Arbiguably, the BitTorrent protocol, created by Cohen [9], has been well recoganized, and then the BitTorrent network becomes one of few Internet wide successful P2P systems [17]. © 2020 International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). All rights reserved.

关键词： Peer to peer networks

A spectrum-enhanced attention model for semantic segmentation of remote sensing images

学校读者我要写书评

暂无评论

A spectrum-enhanced attention model for semantic segmentatio...

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

作者： Xin Li Feng Xu Feifei Tao Yao Tong Xin Lyu Jianyi Zhong André Kaup College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China School of Computer Engineering Jiangsu Ocean University Lianyungang China School of Artificial Intelligence and Information Technology Nanjing University of Chinese Medicine Nanjing China Chair of Multimedia Communications and Signal Processing Friedrich-Alexander University Erlangen-Nuremberg Erlangen Germany

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Semantic segmentation of remote sensing images (RSIs) is essential for applications such as environmental monitoring, urban planning, and disaster management. Convolutional Neural Networks (CNNs) and their variants struggle to capture comprehensive spectral context for learning discriminative representations. In this paper, we propose a Spectrum-Enhanced Network (SPENet) that leverages the Frequency Transformer Block (FTB) to capture rich spectral context. FTB integrates Spectrum-Enhanced Attention (SEA) with Multi-Head Frequency Self-Attention (MH-FSA), incorporating more informative contextual cues. Specifically, SEA aggregates spectral statistics through covariance matrix normalization before applying channel-wise attention. By projecting feature maps onto the frequency domain, MH-FSA provides the network with a broader context, extending beyond the low-frequency focus of standard self-attention mechanisms. Extensive experiments on the ISPRS Potsdam and LoveDA datasets show that SPENet significantly outperforms state-of-the-art methods. Besides, the proposed SEA module notably rises average F1-score/overall accuracy/mean insert over union wiht more than 2.5/2.6%/2.3%, as demonstrated by ablation study.

关键词： Semantic segmentation Frequency-domain analysis Urban planning Semantics Speech enhancement Signal processing Transformers Sensors Remote sensing Standards

Neuromodulated Meta-Learning

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Wang, Jingyao Guo, Huijie Qiang, Wenwen Li, Jiangmeng Zheng, Changwen Xiong, Hui Hua, Gang University of Chinese Academy of Sciences Beijing China National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Thrust of Artificial Intelligence Hong Kong University of Science and Technology Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Wormpex AI Research LLC BellevueWA98004 United States

Humans excel at adapting perceptions and actions to diverse environments, enabling efficient interaction with the external world. This adaptive capability relies on the biological nervous system (BNS), which activates different brain regions for distinct tasks. Meta-learning similarly trains machines to handle multiple tasks but relies on a fixed network structure, not as flexible as BNS. To investigate the role of flexible network structure (FNS) in meta-learning, we conduct extensive empirical and theoretical analyses, finding that model performance is tied to structure, with no universally optimal pattern across tasks. This reveals the crucial role of FNS in meta-learning, ensuring meta-learning to generate the optimal structure for each task, thereby maximizing the performance and learning efficiency of meta-learning. Motivated by this insight, we propose to define, measure, and model FNS in meta-learning. First, we define that an effective FNS should possess frugality, plasticity, and sensitivity. Then, to quantify FNS in practice, we present three measurements for these properties, collectively forming the structure constraint with theoretical supports. Building on this, we finally propose Neuromodulated Meta-Learning (NeuronML) to model FNS in meta-learning. It utilizes bi-level optimization to update both weights and structure with the structure constraint. Extensive theoretical and empirical evaluations demonstrate the effectiveness of NeuronML on various tasks. Code is publicly available at https://***/WangJingyao07/NeuronML. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

Design of fuzzy control based elevator group control algorithm

学校读者我要写书评

暂无评论

Design of fuzzy control based elevator group control algorit...

第34届中国控制与决策会议

作者： Xu Mengbin Wei Dong Zhao Ke School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing Municipal Science and Technology Commission Key Laboratory of Intelligent Processing for Building Big Data Beijing Xicheng District Special Equipment Inspection Institute

In view of the problem that it is dithcult to establish the mathematical model of elevator group control system and the optimization effect of traditional control method is not ideal,a group control model based on fuzzy control is ***,we analyze the basic principle of elevator group control system,then we elaborate the algorithm flow of elevator group control system scheduling optimization,and finally,we verify the algorithm in the simulation platform based on Visual Studio,and the simulation results show that fuzzy control can effectively solve the scheduling optimization problem of elevator group control system.

关键词： Elevator group control system Fuzzy control Scheduling optimization evaluation function

Experimental Analysis of Selective Imitation for Multifactorial Differential Evolution 14th

学校读者我要写书评

暂无评论

Experimental Analysis of Selective Imitation for Multifactor...

14th International Conference on Bio-inspired Computing: Theories and Applications, BIC-TA 2019

作者： Peng, Deming Cai, Yiqiao Fu, Shunkai Luo, Wei College of Computer Science and Technology Huaqiao University Quanzhou China Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao University Xiamen361000 China

ISBN: (纸本)9789811534249

Recently, evolutionary multitasking optimization (EMTO) is proposed as a new emerging optimization paradigm to simultaneously solve multiple optimization tasks in a cooperative manner. In EMTO, the knowledge transfer between tasks is mainly carried out through the assortative mating and selective imitation operators. However, in the literature of EMTO, little study on the selective imitation operator has yet been done to provide a deeper insight in the knowledge transfer across different tasks. Based on this consideration, we firstly study the influence of the inheritance probability (IP) of the selective imitation on an EMTO algorithm, multifactorial differential evolution (MFDE), through the experimental analysis. Then, an adaptive inheritance mechanism (AIM) is introduced into the selective imitation operator of MFDE to automatically adjust the IP value for different tasks at different evolutionary stages. The experimental results on a suite of single-objective multitasking benchmark problems have demonstrated the effectiveness of AIM in enhancing the performance of MFDE. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Multitasking

A novel weighted approach for time series forecasting based on visibility graph

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Zhan, Tianxiang Xiao, Fuyuan School of Computer and Information Science Southwest University Chongqing400715 China School of Big Data and Software Engineering Chongqing University Chongqing401331 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology China

Time series has attracted a lot of attention in many fields today. Time series forecasting algorithm based on complex network analysis is a research hotspot. How to use time series information to achieve more accurate forecasting is a problem. To solve this problem, this paper proposes a weighted network forecasting method to improve the forecasting accuracy. Firstly, the time series will be transformed into a complex network, and the similarity between nodes will be found. Then, the similarity will be used as a weight to make weighted forecasting on the predicted values produced by different nodes. Compared with the previous method, the proposed method is more accurate. In order to verify the effect of the proposed method, the experimental part is tested on M1, M3 datasets and Construction Cost Index (CCI) dataset, which shows that the proposed method has more accurate forecasting performance. Copyright © 2021, The Authors. All rights reserved.

关键词： Time series