检索结果-内蒙古大学图书馆

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

Joint relay selection and power allocation protocol for two-way relaying system

引用

International Journal of Wireless and Mobile computing 2019年第4期17卷 349-360页

作者： Zhi, Hui Zhu, Jun Hu, Yanjun Key Lab of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei Anhui230601 China

The Joint Relay Selection and Power Allocation (JRSPA) for Amplify-and-Forward (AF) relaying (JRSPA-AF) combines Power Allocation (PA) and Relay Selection (RS) very well in Two- Way Relaying (TWR) system. In order to explore the system performance of JRSPA-AF protocol, the outage probability and Diversity-Multiplexing Trade-off (DMT) of JRSPA-AF protocol are analysed. One upper bound, two lower bounds and approximate expressions of outage probability along with the DMT are derived. Based on this, the idea of JRSPA is introduced into Decode-and- Forward (DF) relaying TWR system, and the JRSPA-DF protocol is proposed. Moreover, the exact expressions of outage probability and DMT for JRSPA-DF are derived. Simulations are developed to validate the analytical results, the results show that the approximation outage probability of JRSPA-AF can be used well to evaluate system outage probability over the entire transmission SNR region, and the analysis results of outage probability of JRSPA-DF are consistent with its simulation results. In addition, the simulation results indicate that JRSPA-DF can achieve lower outage probability than JRSPA-AF and other protocols. Copyright © 2019 Inderscience Enterprises Ltd.

关键词： Probability

来源：评论

学校读者我要写书评

暂无评论

Data Augmentation for Deep Learning Based a Utomaticmo Dulation Recognition

SSRN

引用

SSRN 2023年

作者： Ma, Hongbin Feng, Zhixi Yang, Shuyuan Wang, Min Wu, Tao Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xidian University Xi’an710071 China Key Laboratory of Radar Signal Processing Xidian University Xi’an710071 China Xidian University China Institute School of Artificial Intelligence Xidian University TaiBai road 2# Shannxi Province Xi’an China

Data augmentation, as a strategy to expand training datasets, can be utilized to improve the accuracy of Deep Learning(DL)-based models to a certain extent. However, existing data augmentation methods for the DL-based Automatic Modulation Recognition (AMR) task lack a theoretical basis, and the availability of these methods to different types of AMR models has not been evaluated. In this paper, we first model the transformation process from an information source to a modulation signal and analyze the parameters that influence the difference between modulation signals for the theoretical basis of data augmentation in the AMR field. Then we propose nine signal augmentation methods to generate argumentation signals by modifying the influence parameters in the amplitude domain, the time domain, and the frequency domain. Finally, we conduct a series of experiments on the public RML2016.10a and RML2016.10b datasets to evaluate the effectiveness of the proposed methods andanalyze the availability of these methods to Convolutional Neural Network (CNN)-based AMR models, and hybrid AMR models based on CNN and Recurrent Neural Network. © 2023, The Authors. All rights reserved.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

Novel Sparse Array Design Based on the Maximum Inter-Element Spacing Criterion

TechRxiv

引用

TechRxiv 2022年

作者： Shi, Wanlu Li, Yingsong de Lamare, Rodrigo C. College of Information and Communication Engineering Harbin Engineering University Harbin150001 China The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Anhui Hefei China G¨¢vea Rio de Janeiro Brazil The University of York United Kingdom

A novel sparse array (SA) structure is proposed based on the maximum inter-element spacing (IES) constraint (MISC) criterion. Compared with the traditional MISC array, the proposed SA configurations, termed as improved MISC (IMISC) has significantly increased uniform degrees of freedom (uDOF) and reduced mutual coupling. In particular, the IMISC arrays are composed of six uniform linear arrays (ULAs), which can be determined by an IES set. The IES set is constrained by two parameters, namely the maximum IES and the number of sensors. The uDOF of the IMISC arrays is derived and the weight function of the IMISC arrays is analyzed as well. The proposed IMISC arrays have a great advantage in terms of uDOF against the existing SAs, while their mutual coupling remains at a low level. Simulations are carried out to demonstrate the advantages of the IMISC arrays. © , CC BY.

关键词： Degrees of freedom (mechanics)

来源：评论

学校读者我要写书评

暂无评论

Denoiser-Guided Image Deconvolution with Arbitrary Boundaries and Incomplete Observations

SSRN

引用

SSRN 2023年

作者： He, Liangtian Gao, Shaobing Deng, Liang-Jian Wang, Yilun Wang, Chao Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Mathematical Sciences Anhui University Hefei230601 China College of Computer Science Sichuan University Chengdu610065 China Chengdu611731 China American Family Insurance ChicagoIL60654 United States College of Mathematics Physics and Information Zhejiang Ocean University Zhoushan316022 China

Non-blind image deconvolution (NBID) has been a significant and long-standing research topic in the field of imaging sciences. While enduring efforts have been made over the past decades, they are extensively focusing on the scenario where the blur process is artificially assumed, e.g., typically under periodic boundary condition, as the involved matrix inversion can be efficiently tackled using the fast Fourier transforms (FFTs). However, despite its popularity, these boundary conditions are not always realistic assumptions and can lead to severe artifacts in the recovered image. Therefore, image deconvolution with arbitrary boundaries remains as a fundamental problem to be investigated. In this paper, we propose a principled framework with the aid of Plug-and-Play (PnP) scheme to address NBID in a more realistic scenario of unknown boundary. Specifically, the local and nonlocal denoiser priors with implicit optimization objectives are incorporated into a unified model, and the degradation operator is formulated as a composition of a convolution and a spatial mask. Two kinds of experiments: deconvolution, deconvolution with inpainting, all with unknown boundaries are tested. Both quantitative and qualitative evaluations demonstrate that the proposed denoiser-guided PnP framework is highly flexible and effective to cope with the general image deconvolution tasks. © 2023, The Authors. All rights reserved.

关键词： Fast Fourier transforms

来源：评论

学校读者我要写书评

暂无评论

Investigation on ULA Fitting Promoting Low Coupling Sparse Arrays

Investigation on ULA Fitting Promoting Low Coupling Sparse A...

引用

International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Yichang Yao Wanlu Shi Yingsong Li Zhixiang Huang Guohui Yang Kuang Zhang College of Information and Communication Engineering Harbin Engineering University Harbin China Anhui Province Key Laboratory of Target Recognition and Feature Extraction LuAn China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China

A low coupling sparse array (LCSA) is presented, analyzed and discussed on the basis of the recent proposed uniform linear array (ULA) fitting scheme with close-expressions, and its performance with different uniform subarrays in the basic layer is investigated via parameter estimation like direction-of-arrival(DOA). The results gives a superior performance for the presented LCSA.

关键词： Couplings Mutual coupling Direction-of-arrival estimation Fitting Estimation Computational electromagnetics

来源：评论

学校读者我要写书评

暂无评论

Some bounds on the cardinality of the b-symbol weight spectrum of codes

arXiv

引用

arXiv 2024年

作者： Zhu, Hongwei Li, Shitao Shi, Minjia Xia, Shu-Tao Solé, Patrick Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen518055 China School of Mathematical Sciences Anhui University Hefei230601 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Mathematical Sciences Anhui University Hefei230601 China Centrale Marseille CNRS I2M Aix Marseille University Marseille13009 France State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China

The size of the Hamming distance spectrum of a code has received great attention in recent research. The main objective of this paper is to extend these significant theories to the b-symbol distance spectrum. We examine this question for various types of codes, including unrestricted codes, additive codes, linear codes, and cyclic codes, successively. For the first three cases, we determine the maximum size of the b-symbol distance spectra of these codes smoothly. For the case of cyclic codes, we introduce three approaches to characterize the upper bound for the cardinality of the b-symbol weight spectrum of cyclic codes, namely the period distribution approach, the primitive idempotent approach, and the b-symbol weight formula approach. As two by-products of this paper, the maximum number of symplectic weights of linear codes is determined, and a basic inequality among the parameters [n, k, dH(C)]q of cyclic codes is provided. © 2024, CC0.

关键词： Hamming distance

来源：评论

学校读者我要写书评

暂无评论

A Graph Sequence Generator and Multi-head Self-attention Mechanism based Knowledge Graph Reasoning Architecture

A Graph Sequence Generator and Multi-head Self-attention Mec...

引用

International Conference on Computer Supported Cooperative Work in Design

作者： Yuejia Wu Jian-Tao Zhou College of Computer Science Inner Mongolia University National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

Knowledge Graph (KG) is an essential research direction that involves storing and managing knowledge data, but its incompleteness and sparsity hinder its development in various applications. Knowledge Graph Reasoning (KGR) is an effective method to solve this limitation via reasoning missing knowledge based on existing knowledge. The graph Convolution Network (GCN) based method is one of the state-of-the-art approaches to this work. However, there are still some problems, such as the insufficient ability to perceive graph structure and the poor effect of learning data features which may limit the reasoning accuracy. This paper proposes a KGR architecture based on a graph sequence generator and multi-head self-attention mechanism, named GaM-KGR, to improve the above problems and enhance prediction accuracy. Specifically, the GaM-KGR first introduces the graph generation model into the field of KGR for graph representation learning to obtain the hidden features in the data so that enhancing the reasoning effect and then embeds the generated graph sequence into the multi-head self-attention mechanism for subsequent processing to improve the graph structure perception ability of the proposed architecture, so that it can process the graph structure data more appropriately. Extensive experimental results show that the GaM-KGR architecture can achieve the state-of-the-art prediction results of current GCN-based models.

关键词：

来源：评论

学校读者我要写书评

暂无评论

CRSOT: Cross-Resolution Object Tracking using Unaligned Frame and Event Cameras

arXiv

引用

arXiv 2024年

作者： Zhu, Yabin Wang, Xiao Li, Chenglong Jiang, Bo Zhu, Lin Huang, Zhixiang Tian, Yonghong Tang, Jin Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University China School of Computer Science and Technology Anhui University China School of Artificial Intelligence Anhui University China Beijing Institute of Technology Beijing China School of Electronic and Information Engineering Anhui University China Peng Cheng Laboratory Shenzhen China School of Computer Science Peking University Beijing China School of Electronic and Computer Engineering Peking University Shenzhen China

Existing datasets for RGB-DVS tracking are collected with DVS346 camera and their resolution (346 × 260) is low for practical applications. Actually, only visible cameras are deployed in many practical systems, and the newly designed neuromorphic cameras may have different resolutions. The latest neuromorphic sensors can output high-definition event streams, but it is very difficult to achieve strict alignment between events and frames on both spatial and temporal views. Therefore, how to achieve accurate tracking with unaligned neuromorphic and visible sensors is a valuable but unresearched problem. In this work, we formally propose the task of object tracking using unaligned neuromorphic and visible cameras. We build the first unaligned frame-event dataset CRSOT collected with a specially built data acquisition system, which contains 1,030 high-definition RGB-Event video pairs, 304,974 video frames. In addition, we propose a novel unaligned object tracking framework that can realize robust tracking even using the loosely aligned RGB-Event data. Specifically, we extract the template and search regions of RGB and Event data and feed them into a unified ViT backbone for feature embedding. Then, we propose uncertainty perception modules to encode the RGB and Event features, respectively, then, we propose a modality uncertainty fusion module to aggregate the two modalities. These three branches are jointly optimized in the training phase. Extensive experiments demonstrate that our tracker can collaborate the dual modalities for high-performance track- Copyright © 2024, The Authors. All rights reserved.

关键词： Cameras

来源：评论

学校读者我要写书评

暂无评论

Interactive Prototype Learning and Self-Learning for Few-Shot Medical Image Segmentation

SSRN

引用

SSRN 2024年

作者： Song, Yuhui Xu, Chenchu Wang, Boyan Du, Xiuquan Chen, Jie Zhang, Yanping Li, Shuo School of Computer Science and Technology Anhui University Hefei230601 China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230051 China Department of Computer Science and Technology Tsinghua University Beijing100084 China School of Engineering Case Western Reserve University Cleveland44106 United States

Few-shot learning alleviates the heavy dependence of medical image segmentation tasks on large-scale labeled data, but it shows a strong performance gap when dealing with new targets and new tasks compared with traditional deep learning. Existing few-shot medical image segmentation models mainly realize segmentation by learning the semantic class knowledge of a few known (support) samples and extending it to unknown (query) samples. However, the neglect of the differences between known and unknown samples leads to a certain deviation in the transfer of class knowledge, and the trained model tends to overfit the class knowledge of training classes, which greatly reduces the segmentation and generalization performance of few-shot model. In this work, we propose an interactive prototype learning and self-learning network to alleviate the above problems. Specifically, we propose an interactive prototype learning network to promote feature consistency between support images and query images to improve the accuracy of prototype metric learning. In addition, to avoid overfitting the training class knowledge and reduce the over-dependence of query segmentation on the class knowledge, we introduce a query features-guided self-learning network to achieve the finer segmentation of query images through its rich feature information. Our model obtained the competitive segmentation results on three multi-organ and multi-modal public datasets that show a substantial improvement in generalization ability. © 2024, The Authors. All rights reserved.

关键词： Zero-shot learning

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：