检索结果-内蒙古大学图书馆

International Workshop on Advanced Computational Intelligence (IWACI)

作者： Wei Guo Hangjun Che Man-Fai Leung Nankun Mu Xiangguang Dai Yuming Feng School of Electronic and Information Engineering Southwest University Chongqing China School of Electronic Information Engineering Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Southwest University Chongqing China School of Computing and Information Science Faculty of Science and Engineering Anglia Ruskin University Cambridge UK Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education College of Computer Science Chongqing University Chongqing China School of Computer Science and Engineering Chongqing Three Gorges University Chongqing China Key Laboratory of Intelligent Information Processing and Control Chongqing Three Gorges University Chongqing China

We propose a latent multi-view subspace clustering model based on Laplacian regularized representation. To emphasize the information of the representation matrix at the local level and reflect the grouping effect of clustering, a Laplacian regularization is imposed on the representation matrix. Additionally, To boost the efficiency o f c lustering, we apply nonnegative constraints on the representation matrix. A unified framework is designed to solve the proposed model by using ALM and LADMAP methods. The proposed model has excellent performance, as demonstrated by a number of experimental findings.

关键词：

来源：评论

学校读者我要写书评

暂无评论

CSCL-DTI: predicting drug-target interaction through cross-view and self-supervised contrastive learning

CSCL-DTI: predicting drug-target interaction through cross-v...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xuan Lin Xi Zhang Zu-Guo Yu Yahui Long Xiangxiang Zeng Philip S. Yu College of Computer Science Xiangtan University China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University China Singapore Immunology Network (SIgN) Agency for Science Technology and Research (A*STAR) Singapore College of Information Science and Engineering Hunan University China Department of Computer Science University of Illinois Chicago USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Accurately predicting drug-target interactions (DTI) is a critical step in drug discovery. Existing methods of DTI prediction primarily employ Simplified Molecular-Input Line-Entry System (SMILES) sequences or molecular graphs to learn drug representations. However, the features learned by such single-view approach is prone to incomplete. While some multiview methods that consider the views of both SMILES sequences and molecular graphs have been developed, these methods often fall in short in capturing potential interactions between views. In this work, we propose a novel dual contrastive learning framework CSCL-DTI for DTI prediction. First, we design a contrastive-enhanced cross-view representation learning (CVRL) to learn representations for drugs. In this module, Transformer-based and graph convolutional network (GCN)-based encoders are separately adopted to learn view-specific representations, followed by contrastive learning to enrich the representations by accounting for the potential interplay between local chemical context and topological structure. Second, we combine Transformer with self-supervised contrastive learning (SSCL) to learn representations for targets by modelling protein amino acids sequences. The scheme allows to effectively preserve the intrinsic characteristics of the sequences. Finally, we introduce a bilinear attention network to obtain an integrated representation by adaptively incorporating drug and target representations. Benchmarking experiments on two datasets demonstrated that CSCL-DTI 1 outperforms six state-of-the-art methods.

关键词： Drugs Proteins Representation learning Protein engineering Adaptation models Contrastive learning Predictive models Transformers Amino acids Diffusion tensor imaging

来源：评论

学校读者我要写书评

暂无评论

NOBGP: A Novel Optimized Balanced Graph Partitioning Algorithm 19th

NOBGP: A Novel Optimized Balanced Graph Partitioning Algorit...

引用

19th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2024

作者： Chen, Jiebin Hu, Ziqiang Ye, Renjie Zhang, Qishan Guo, Kun College of Computer and Data Science Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China Xianda College of Economics and Humanities Shanghai International Studies University Shanghai China

ISBN: (纸本)9789819623723

Large-scale graphs have become prevalent with the advent of the big data era. Distributed graph computing systems are commonly used for processing and analyzing large-scale graphs, with graph partitioning being a key prerequisite for their efficient computation. Graph partitioning aims to balance the load across partitions while minimizing the number of cut-edges. Moreover, it should achieve high efficiency and scalability. However, the existing popular graph partitioning algorithms do not fully take into account the internal topology of real-world graphs, which affects the final partition quality and convergence. Meanwhile, they easily fall into the local optimum due to partition load constraints. This paper introduces a Novel Optimized Balanced Graph Partitioning algorithm (NOBGP). First, we propose an initialization strategy based on label propagation of core vertices to achieve initial partitions with good locality and accelerate convergence. Second, we optimize the label propagation process to ensure balanced partitions and propose a probability-based disruption strategy to avoid the local optimum. We implement NOBGP on the distributed graph computing framework GraphX. Extensive experimental results on real-world graphs show that the proposed algorithm is scalable and performs better than the existing algorithms. We also run PageRank and Louvain applications using the graph partitioning results to demonstrate the efficiency of our algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Graph algorithms

来源：评论

学校读者我要写书评

暂无评论

Ultra-wideband MIMO Antenna Design Based on EBG Structure

Ultra-wideband MIMO Antenna Design Based on EBG Structure

引用

Progress in Electromagnetic Research Symposium (PIERS)

作者： Zhipeng Chen Shijie Huang Zhongxiang Zhang Xianliang Wu Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Heifei China Anhui Province Key Laboratory of Simulation and Design for Electronic Information System Hefei Normal University Heifei China

ISBN: (纸本)9781665409889

This paper proposes a low-coupling planar compact four-element ultra-wideband antenna based on the EBG structure for multiple-input multiple-output (MIMO) systems. The MIMO antenna is composed of four miniaturized monopole antenna elements based on the half-cut method and a periodic electromagnetic band gap (EBG) structure. The traditional mushroom-shaped EBG structure is cut into squares and a pair of harpoon-shaped microstrip lines are introduced. Structure to reduce its resonant frequency and increase the band gap width. By loading the improved EBG structure in the middle of the MIMO antenna, the electromagnetic coupling between the components is greatly reduced. In addition, the stop band characteristics in the WLAN band (5.725–5.825GHz) are realized by adding a U-shaped resonant stub to the right side of the feeder of the monopole antenna. The experimental results show that it has a good reflection coefficient in the passband bandwidth range of 2.90–11.7GHz, covering the 3.1–10.6GHz frequency band specified by UWB, the isolation between the components is greater than 15dB, and good envelope correlation. The coefficient ECC is <0.02, which proves that the antenna is a potential candidate for ultra-wideband applications.

关键词： Correlation Resonant frequency Bandwidth Metamaterials Ultra wideband antennas Reflection coefficient Passband

来源：评论

学校读者我要写书评

暂无评论

Preference Multi-Objective Evolutionary Optimization Based on Angle Decompositio

SSRN

引用

SSRN 2023年

作者： Gao, Yubo Yu, Fan Zou, Juan Tang, Huanrong Zheng, Jinhua Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Xiangtan University Xiangtan411105 China School of Computer Science School of Cyberspace Science University of Xiangtan Xiangtan411105 China School of Traffic and Transport Engineering Central South University Changsha410083 China

The optimal solution set of multi-objective optimization problems (MOPs) exists in a Pareto optimal front (PF) consisting of many Pareto non-dominated solutions. Still, the decision maker (DM) may be interested in only some of the solutions in the PF, called the region of interest (ROI). However, the existing strategies to determine the ROI for the DM are only limited to obtaining the preference region close to the preference point, which does not fully realize the essence of the preference multi-objective optimization problems (PMOPs), i.e., the solutions closer to the preference point should be more preferred by the DM. To better investigate the effectiveness of solving PMOPs, this paper proposes a preference multi-objective optimization evolutionary algorithm (PMOEA) based on the angle decomposition between objectives (MOEA/D-DAP) to avoid the challenge of preference information loss in PMOPs with increasing objective space dimension. Meanwhile, the reference vector generation method proposed by the MOEA/D-DAP enables preference solutions that align with the DM's needs to be closer to the preference points. Therefore, the preference solutions that are more in line with the needs of the DM exist closer to the preference points. Experiments on benchmark problems with dual and high dimensional objectives demonstrate the high effectiveness of the proposed MOEA/D-DAP in obtaining the ROI of the PMOPs. © 2023, The Authors. All rights reserved.

关键词： Multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

S2CCT: Self-Supervised Collaborative CNN-Transformer for Few-shot Medical Image Segmentation

S2CCT: Self-Supervised Collaborative CNN-Transformer for Few...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Rongzhou Zhou Ziqi Shu Weixing Xie Junfeng Yao Qingqi Hong Center for Digital Media Computing School of Film School of Informatics Xiamen University Xiamen China National Institute for Data Science in Health and Medicine Xiamen University Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Institute of Artificial Intelligence Xiamen University Xiamen China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Self-supervised pre-training followed by fine-tuning is a potent paradigm for few-shot learning, leveraging extensive unlabeled data with remarkable efficacy. Current self-supervised methods often lean towards Vision Transformers (ViTs) rather than CNN-Transformer hybrid architectures, which generally demonstrate superior performance. However, this reliance on ViTs can lead to poor perception of local features by the model. The challenge lies in designing a suitable proxy task for hybrid architectures like CNN-Transformers, which have significant structural differences. Additionally, the current organization of CNN-Transformer hybrid backbones is often sequential, hindering collaboration during pre-training and the acquisition of robust representations. To address these issues, we propose Self-Supervised Collaborative CNN-Transformer (S 2 CCT) for few-shot medical image segmentation. This framework introduces three innovative designs: (1) a composite proxy task based on image masking and image super-resolution tailored for CNN-Transformer hybrid architectures, enabling the backbone to acquire robust representations during pre-training that can be transferred to downstream tasks; (2) a parallel CNN-Transformer architecture that better attends to multi-scale features in images, making it more suitable for dense prediction tasks like image segmentation; (3) a sparse and dense feature fusion module to enhance collaboration between the two encoders. Experiments demonstrate that S 2 CCT outperforms previous state-of-the-art methods on two public medical image segmentation benchmarks, i.e., ACDC and KiTs19. The code and pretrained models will be released soon.

关键词： Image segmentation Three-dimensional displays Superresolution Collaboration Organizations Feature extraction Transformers Medical diagnosis Medical diagnostic imaging Knowledge transfer

来源：评论

学校读者我要写书评

暂无评论

Interpretable multi-view attention network for drug-drug interaction prediction

Interpretable multi-view attention network for drug-drug int...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xuan Lin Qi Wen Sijie Yang Zu-Guo Yu Yahui Long Xiangxiang Zeng College of Computer Science Xiangtan University China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University China Singapore Immunology Network (SIgN) Agency for Science Technology and Research(A*STAR) Singapore College of Information Science and Engineering Hunan University China

Drug-drug interaction (DDI) plays an increasingly crucial role in drug discovery. Predicting potential DDI is also essential for clinical research. Given the high cost and risk of wet-lab experiments, in-silico DDI prediction is an alternative choice. Recently, deep learning methods have been developed for DDI prediction. However, most of existing methods focus on feature extraction from either molecular SMILES sequences or drug interactive networks, ignoring the valuable complementary information that can be derived from these two views. In this paper, we propose a novel interpretable Multi-View Attention network (MVA-DDI) for DDI prediction. MVA-DDI can effectively extracts drug representations from different perspectives to improve DDI prediction. Specifically, for a given drug, we design a transformer-based encoder and a graph convolutional networkbased encoder to learn sequence and graph representations from SMILES sequence and molecular graph, respectively. To fully exploit the complementary information between the sequence and molecular views, an attention mechanism is further adopted to adaptively aggregate the sequence and graph representations by taking the importance of different views into accounts, generating the final drug representations. Comparison experiments demonstrated that our MVA-DDI 1 model achieved superior performance to state-of-the-art models on DDI prediction.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Distributed Traffic Engineering in Hybrid Software Defined Networks: A Multi-agent Reinforcement Learning Framework

arXiv

引用

arXiv 2023年

作者： Guo, Yingya Tang, Qi Ma, Yulong Tian, Han Chen, Kai The College of Computer and Data Science Fuzhou University China The Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University China The Key Laboratory of Spatial Data Mining & Information Sharing Ministry of Education Fujian 350003 China The Department of Computer Science & Engineering Hong Kong University of Science & Technology Hong Kong

Traffic Engineering (TE) is an efficient technique to balance network flows and thus improves the performance of a hybrid Software Defined Network (SDN). Previous TE solutions mainly leverage heuristic algorithms to centrally optimize link weight setting or traffic splitting ratios under the static traffic demand. Note that as the network scale becomes larger and network management gains more complexity, it is notably that the centralized TE methods suffer from a high computation overhead and a long reaction time to optimize routing of flows when the network traffic demand dynamically fluctuates or network failures happen. To enable adaptive and efficient routing in TE, we propose a Multi-agent Reinforcement Learning method CMRL that divides the routing optimization of a large network into multiple small-scale routing decision-making problems. To coordinate the multiple agents for achieving a global optimization goal, we construct an interactive environment for training the routing agents that own partial link utilization observations. To optimize credit assignment of multi-agent, we introduce the difference reward assignment mechanism for encouraging agents to take better action. Extensive simulations conducted on the real traffic traces demonstrate the superiority of CMRL in improving TE performance, especially when traffic demands change or network failures happen. Copyright © 2023, The Authors. All rights reserved.

关键词： Global optimization

来源：评论

学校读者我要写书评

暂无评论

Energy Consumption and Time Delay optimization of MEC Based on MultiDimensional Game 5

Energy Consumption and Time Delay optimization of MEC Based ...

引用

5th IEEE International Conference on Cloud computing and Big Data Analytics, ICCCBDA 2020

作者： Wang, Hui Wang, Yi Lu, Xingjun Hu, Yanjun Anhui University Key Laboratory of Intelligent Computing Signal Processing Ministry of Education Hefei230601 China Key Laboratory of Wireless Sensor Network & Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China

ISBN: (纸本)9781728160245

Mobile edge computing (MEC) reduces latency and power consumption of mobile devices by deploying servers on the network edge. Due to the limited computing resources of the MEC server, excessive offloading of the users in local cells will bring unexpected queuing delay and high energy consumption. To deal with these problems, this paper proposes a Multi-user Queue Offloading multi-dimensional game algorithm (MQO). By the algorithm, users could choose the most suitable strategy, in which the strategy space is two-dimensional to satisfy different needs of different users to a certain extent, including the offload strategy and power control of users. Simulation results show that MQO can improve the performance of both overhead and offloading number with the increase of users. © 2020 IEEE.

关键词： Energy utilization

来源：评论

学校读者我要写书评

暂无评论

Interblock-interference-free precoding using square-root decomposition for faster-than-Nyquist signaling

引用

Digital Communications and Networks 2025年

作者： Yan Wang Qiang Li Liping Li Yingsong Li Xingwang Li Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei 230601 China School of Physics and Electronic Information Engineering Henan Polytechnic University Jiaozuo China

Precodings using square-root decomposition, including Cholesky and G-To-Minus-Half (GTMH) precodings, are promising for eliminating the Faster-Than-Nyquist (FTN)-induced Intersymbol-Interference (ISI). However, the existing precodings using square-root decomposition either ignore Interblock-Interference (IBI) or increase the signal power, deteriorating the Bit Error Rate (BER) performance for high-order modulations and severe ISI. To overcome these drawbacks, we adopt two approaches for constructing the circular ISI matrix. The first approach inserts a Cyclic Prefix/Suffix (CPS) after each precoded symbol block, while the second approach replaces the linear convolution of the FTN shaping and the matched filter by the circular convolution, resulting in the Circular FTN (CFTN). Using these two approaches, we propose three IBI-free precodings, i.e., CPS-Cholesky, CFTN-Cholesky and CFTN-GTMH precodings. Furthermore, employing QR decomposition shows that the GTMH and Cholesky precodings can be converted interchangeably. Thus, we demonstrate that the GTMH precoding is essentially equivalent to the Cholesky precoding. Simulation results indicate that the BER performance of three IBI-free precodings approaches Nyquist performance for moderate ISI. However, as ISI intensifies, the CPS-Cholesky scheme increases the transmit power, causing BER performance degradation. In contrast, the CFTN-Cholesky and CFTN-GTMH precodings maintain optimal BER performance even for severe ISI. Considering 128-amplitude phase shift keying with a code rate of 1/2, the BER loss of CFTN-Cholesky and CFTN-GTMH precodings for the ideal BER of 10-5 is approximately 0.002 dB and 0.005 dB when packing factor is 0.7 and roll-off factor is 0.3. To the best knowledge of the authors, this is the optimal performance achievable through precoding.

关键词： Faster-than-Nyquist Intersymbol-interference Interblock-interference Bit error rate Precoding

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：