检索结果-内蒙古大学图书馆

TechRxiv 2023年

作者： Wang, Junkai Quan, Lijun Jin, Zhi Wu, Hongjie Ma, Xuhao Wang, Xuejiao Xie, Jingxin Pan, Deng Chen, Taoning Wu, Tingfang Lyu, Qiang School of Computer Science and Technology of Soochow University Jiangsu215006 China Province Key Lab for Information Processing Technologies Soochow University Jiangsu215006 China Collaborative Innovation Center of Novel Software Technology and Industrialization Jiangsu210000 China School of Electronic and Information Engineering Suzhou University of Science and Technology Jiangsu215009 China

This study aims to address the complexity of predicting RNA-small molecule binding sites to explore the potential value in the field of RNA drug targets. To address this challenge, we propose the MultiModRLBP method, which integrates multi-modal features using deep learning algorithms. These features include 3D structural properties at the nucleotide base level, relational graphs based on overall RNA structure, and semantically rich RNA sequence information. Different deep modules are employed to learn high-dimensional different modility of semantic features. RNAbert is fine-tuned to learn sequence information, relational graph convolutional network focuses on capturing various relationships between different nodes in the RNA graph, and CNN directly and indirectly captures local and global 3D structural features. Consequently, MultiModRLBP can better reflect the inherent complex relationships within RNA. Furthermore, we expanded the dataset to include RNA complexes with the same RNA sequences but varying structures, enabling the model to better grasp subtle changes at the structural level. The research results demonstrate that the performance of MultiModRLBP outperforms state of the art methods across multiple classic test sets, especially in predicting binding sites located in pockets or those that are distantly distributed along the sequences. This advancement holds promise in reducing the costs of RNA-targeted drugs development. © 2023, CC BY.

关键词： Binding sites

来源：评论

学校读者我要写书评

暂无评论

IMRG: Impedance Matching Oriented Receiver Grouping for MIMO WPT System

IMRG: Impedance Matching Oriented Receiver Grouping for MIMO...

引用

International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Lulu Tang Hao Zhou Weiming Guo Wangqiu Zhou Xing Guo Xiaoyan Wang LINKE Lab School of Computer Science and Technology University of Science and Technology of China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China Deqing Alpha Innovation Institute Huzhou Zhejiang China School of Computer Science and Technoloay Anhui University China Graduate School of Science and Engineering Ibaraki University Ibaraki Japan

In recent years, multiple-input multiple-output (MIMO) technology has been imported into magnetic resonance coupled (MRC) enabled wireless power transfer (WPT) systems for concurrent charging of multiple devices. Besides the traditional performance optimization methods (e.g., TX current scheduling, system frequency adjustment, etc.), receiver (RX) grouping will also severely influence the achieved power- delivered-to-load (PDL). In this paper, we investigate the optimal RX grouping issue to maximize the proportional fairness of RX achieved PDL, which is a joint optimization problem involving RX grouping and time-slice allocation among groups. By decoupling the problem, we solve the group generation sub-problem with a impedance-matching based greedy algorithm to generate potential RX group candidates, and we further solve the time slice allocation sub-problem with a genetic algorithm to distribute resources among group candidates. We prototype the proposed system, denoted as IMRG, and conduct extensive experiments to evaluate the performance. The experimental results validate the effectiveness of the proposed algorithm, e.g., IMRG achieves average 59.6% PDL improvement through RX grouping compared to the simultaneous charging scheme.

关键词： Impedance matching Prototypes Magnetic resonance Receivers Wireless power transfer MIMO Sensors

来源：评论

学校读者我要写书评

暂无评论

Towards Bi-level Supply/Demand Balanced Charging Systems via Online Power Scheduling

引用

IEEE Transactions on Mobile Computing 2025年

作者： Lu, Xinyu Lou, Jiong Li, Jie Xu, Runhui Wu, Chentao Liu, Zhi Luo, Yuan Yang, Yang Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China Yancheng Blockchain Research Institute China Shanghai Jiao Tong University Shanghai200240 China Department of Computer Science and Engineering China Shanghai Jiao Tong University Innovation Center of Intelligent Connected Electric Vehicle in Global Institute of Future Technology Shanghai200240 China University of Electro-Communications Graduate School of Informatics and Engineering Tokyo182-8585 Japan Guangzhou511453 China Peng Cheng Laboratory Shenzhen518055 China Terminus Group Beijing100027 China

With the rise of transportation electrification, an increasing number of charging stations have been established, forming a city-scale charging system. These charging stations serve as intermediaries that connect supply and demand, drawing power from the grid and renewable energy sources to provide electricity to electric vehicles. Maintaining a delicate balance between supply and demand has emerged as a significant challenge for the charging system. On a macroscopic level, it impacts the power grid's peak load and reliability, while locally, it influences electric vehicle detour events. To comprehensively model the spatio-temporal characteristics in the charging system, we partition the charging system by adopting a supply-demand-aware approach and propose OPS, an online power scheduling algorithm based on the regularization technique. OPS aims to achieve a bi-level balance between supply and demand while constraining the power output of the charging system. We substantiate the efficacy of OPS through rigorous theoretical proofs, demonstrating its comparability to the optimal solution. Furthermore, we conduct extensive evaluation experiments with real-world data sets to establish the feasibility of the proposed methodology in alleviating the supply-demand imbalance. The results indicate that OPS attains an empirical competitive ratio of less than 1.2. © 2002-2012 IEEE.

关键词： Charging stations

来源：评论

学校读者我要写书评

暂无评论

Learning invariant molecular representation in latent discrete space 23

Learning invariant molecular representation in latent discre...

引用

Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Xiang Zhuang Qiang Zhang Keyan Ding Yatao Bian Xiao Wang Jingsong Lv Hongyang Chen Huajun Chen College of Computer Science and Technology Zhejiang University and ZJU-Hangzhou Global Scientific and Technological Innovation Center and Zhejiang University - Ant Group Joint Laboratory of Knowledge Graph ZJU-Hangzhou Global Scientific and Technological Innovation Center Tencent AI Lab School of Software Beihang University Zhejiang Lab

Molecular representation learning lays the foundation for drug discovery. However, existing methods suffer from poor out-of-distribution (OOD) generalization, particularly when data for training and testing originate from different environments. To address this issue, we propose a new framework for learning molecular representations that exhibit invariance and robustness against distribution shifts. Specifically, we propose a strategy called "first-encoding-then-separation" to identify invariant molecule features in the latent space, which deviates from conventional practices. Prior to the separation step, we introduce a residual vector quantization module that mitigates the over-fitting to training data distributions while preserving the expressivity of encoders. Furthermore, we design a task-agnostic self-supervised learning objective to encourage precise invariance identification, which enables our method widely applicable to a variety of tasks, such as regression and multi-label classification. Extensive experiments on 18 real-world molecular datasets demonstrate that our model achieves stronger generalization against state-of-the-art baselines in the presence of various distribution shifts. Our code is available at https://***/HICAI-ZJU/iMoLD.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Cross-Point Adversarial Attack Based on Feature Neighborhood Disruption Against Segment Anything Model

Cross-Point Adversarial Attack Based on Feature Neighborhood...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Yan Jiang Guisheng Yin Ye Yuan Jingjing Chen Zhipeng Wei College of Computer Science and Technology Harbin Engineering University Harbin China Shanghai Key Lab of Intell. Info. Processing School of CS Fudan University Shanghai China Shanghai Collaborative Innovation Center of Intelligent Visual Computing Shanghai China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Segment anything model (SAM) has received significant attention owing to its outstanding segmentation performance. However, it may still face security threats from adversarial examples. Since SAM interactively realizes the prediction of target areas according to user-specified prompts (e.g., points), adversarial examples generated by existing end-to-end attack methods usually exhibit limited attack performance when faced with different point prompts. To this end, we propose a cross-point adversarial attack method based on feature neighborhood disruption against SAM, called CP-FND attack. CP-FND aims to generate adversarial examples capable of effectively deceiving SAM under different user-specified point prompts. Specifically, CP-FND forces the intermediate feature of adversarial examples to be similar to the designed disruption features without relying on any specified point prompt. Subsequently, the continuity and relevance of contextual features are disrupted, thereby fooling SAM and suppressing its predicted masks. Extensive experiments demonstrate that CP-FND achieves superior cross-point adversarial attack performance against SAM compared to state-of-the-art methods.

关键词： Image segmentation Correlation Security Faces

来源：评论

学校读者我要写书评

暂无评论

ReAssigner: A Plug-and-Play Virtual Machine Scheduling Intensifier for Heterogeneous Requests

ReAssigner: A Plug-and-Play Virtual Machine Scheduling Inten...

引用

2022 IEEE International Conference on Big Data, Big Data 2022

作者： Cui, Haochuan Sheng, Junjie Jin, Bo Hu, Yiqiu Su, Li Zhu, Lei Zhou, Wenli Wang, Xiangfeng East China Normal University School of Computer Science and Technology Shanghai200062 China Huawei Cloud Computing Technologies Co. Algorithm Innovation Lab. Xi'an710049 China Huawei Cloud Computing Technologies Co. Computing Service Product Dept. Xi'an710049 China

ISBN: (纸本)9781665480451

With the rapid development of cloud computing, virtual machine scheduling has become one of the most important but challenging issues for the cloud computing community, especially for practical heterogeneous request sequences. By analyzing the impact of request heterogeneity on some popular heuristic schedulers, it can be found that existing scheduling algorithms can not handle the request heterogeneity properly and efficiently. In this paper, a plug-and-play virtual machine scheduling intensifier, called Resource Assigner (ReAssigner), is proposed to enhance the scheduling efficiency of any given scheduler for heterogeneous requests. The key idea of ReAssigner is to pre-assign roles to physical resources and let resources of the same role form a virtual cluster to handle homogeneous requests. ReAssigner can cooperate with arbitrary schedulers by restricting their scheduling space to virtual clusters. With evaluations on the real dataset from Huawei Cloud, the proposed ReAssigner achieves significant scheduling performance improvement compared with some state-of-the-art scheduling methods. © 2022 IEEE.

关键词： Cloud computing

来源：评论

学校读者我要写书评

暂无评论

FedTracker: Furnishing Ownership Verification and Traceability for Federated Learning Model

arXiv

引用

arXiv 2022年

作者： Shao, Shuo Yang, Wenyuan Gu, Hanlin Qin, Zhan Fan, Lixin Yang, Qiang Ren, Kui The State Key Laboratory of Blockchain and Data Security Zhejiang University China The School of Cyber Science and Technology Sun Yat-sen University China The Webank AI Lab China The Computer Science and Engineering Department Hong Kong University of Science and Technology Hong Kong

Federated learning (FL) is a distributed machine learning paradigm allowing multiple clients to collaboratively train a global model without sharing their local data. However, FL entails exposing the model to various participants. This poses a risk of unauthorized model distribution or resale by the malicious client, compromising the intellectual property rights of the FL group. To deter such misbehavior, it is essential to establish a mechanism for verifying the ownership of the model and as well tracing its origin to the leaker among the FL participants. In this paper, we present FedTracker, the first FL model protection framework that provides both ownership verification and traceability. FedTracker adopts a bi-level protection scheme consisting of global watermark mechanism and local fingerprint mechanism. The former authenticates the ownership of the global model, while the latter identifies which client the model is derived from. FedTracker leverages Continual Learning (CL) principles to embed the watermark in a way that preserves the utility of the FL model on both primitive task and watermark task. FedTracker also devises a novel metric to better discriminate different fingerprints. Experimental results show FedTracker is effective in ownership verification, traceability, and maintains good fidelity and robustness against various watermark removal attacks. © 2022, CC BY.

关键词： Learning systems

来源：评论

学校读者我要写书评

暂无评论

Efficient COVID-19 detection using data mining algorithms: a comparison of basic and hybrid approaches

引用

Soft Computing 2025年第3期29卷 1437-1451页

作者： Saidi, Mohammad Gheibi, Mohammad Ghazikhani, Adel Lotfata, Aynaz Chahkandi, Benyamin Familsamavati, Sajad Behzadian, Kourosh Department of Computer Science Imam Reza International University Mashhad Iran MS of Software Engineering Information and Communication Technology Unit Mashhad Municipality Fire and Safety Services Organization Mashhad Iran Big Data Lab Imam Reza International University Mashhad Iran Institute for Nanomaterials Advanced Technologies and Innovation Technical University of Liberec Liberec46117 Czech Republic Tallinn10615 Estonia Department of Pathology Microbiology and Immunology School of Veterinary Medicine University of California Davis95616 United States Department of Urban and Regional Planning San Jose State University San Jose95192 United States Faculty of Civil and Environmental Engineering Gdansk University of Technology Narutowicza Street 11/12 80-233 Gdansk Poland Tecnologico de Monterrey Escuela de Ingeniería y Ciencias Monterrey Mexico School of Computing and Engineering University of West London London United Kingdom

Accurate and efficient diagnosis of COVID-19 remains a significant challenge due to the limitations of current detection methods, such as blood tests and chest scans, which can be time-consuming and error-prone. This study aims to compare the performance of basic and hybrid data mining algorithms in diagnosing COVID-19, using blood test results and clinical information to identify the most effective approach. A dataset of 200 records from suspected and infected COVID-19 patients, with 23 characteristics and one diagnostic class, was analysed. Nine data mining algorithms were tested: four basic algorithms (Naive Bayes, Support Vector Machine, Decision Tree, K-Nearest Neighbor) and five hybrid algorithms (Random Forest, AdaBoost, Majority Voting, XGBoost, Bagging). The study also integrated Response Surface Methodology (RSM) and Adaptive-Network-based Fuzzy Inference System (ANFIS) to enhance model performance. The Bagging algorithm demonstrated superior performance with an accuracy of 88%, sensitivity of 74%, and F-criterion of 78%. The integration of RSM and ANFIS further showed that a smart model could be developed for efficient pandemic crisis management, achieving up to 100% accuracy when considering key factors like AST, Albumin, and CRP. The findings suggest that Bagging and hybrid data mining algorithms can significantly improve COVID-19 detection, reducing time and errors in identifying exposed individuals. The study highlights the potential of combining machine learning techniques with RSM-ANFIS models for effective pandemic management and decision-making in medical settings. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

A biosensing system using a multiparameter nonlinear dynamic analysis of cardiomyocyte beating for drug-induced arrhythmia recognition

引用

Microsystems & Nanoengineering 2022年第3期8卷 277-290页

作者： Hao Wang Yue Wu Quchao Zou Wenjian Yang Zhongyuan Xu Hao Dong Zhjing Zhu Depeng Wang Tanxing Wang Ning Hu Diming Zhang State Key Laboratory of Optoelectronic Materials and Technologies Guangdong Province Key Laboratory of Display Material and TechnologySchool of Electronics and Information TechnologySun Yat-sen UniversityGuangzhou 510006China Research Center for Itelligent Sensing Systems Zhejiang LabHangzhou 311121China ZJU-Hangzhou Global Scientific and Technological Innovation Center Department of ChemistryThe Second Affiliated Hospital Zhejiang University School of MedicineDepartment of Clinical Medical EngineeringZhejiang UniversityHangzhou 310058China Key Laboratory of Novel Target and Drug Study for Neural Repair of Zhejang Province School of MedicineSchool of Computer&Computing ScienceZhejiang University City CollegeHangzhou 310015China School of Brain Science and Brain Medicine Zhejiang UniversityHangzhou 310058China College of Energy and Power Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China E-LinkCare Meditech Co LtdHangzhou 310011China State Key Laboratory of Transducer Technology Chinese Academy of SciencesShanghai 200050China

Cardiovascular disease is the number one cause of death in ***,cardiotoxicity is one of the most important adverse effects assessed by arrhythmia recognition in drug ***,cell-based techniques developed for arhythmia recognition primarily employ linear methods such as time-domain analysis that detect and compare individual waveforms and thus fall short in some applications that require automated and efficient arrhythmia recognition from large *** carried out the frst report to develop a biosensing system that integrated impedance measurement and multiparameter nonlinear dynamic algorithm(MNDA)analysis for druginduced arrhythmia recognition and *** biosensing system cultured cardiomyocytes as physiologically relevant models,used interdigitated electrodes to detect the mechanical beating of the cardiomyocytes,and employed MNDA analysis to recognize drug-induced arrhythmia from the cardiomyocyte beating *** best performing MNDA parameter,approximate entropy,enabled the system to recognize the appearance of sertindoleand norepinephrine-induced arrhythmia in the *** MNDA reconstruction in phase space enabled the system to classify the different arrhythmias and quantify the severity of *** new biosensing system utilizing MNDA provides a promising and alternative method for drug-induced arrhythmia recognition and classification in cardiological and pharmaceutical applications.

关键词： arrhythmia cardio analysis

来源：评论

学校读者我要写书评

暂无评论

Rethinking Disentanglement in Unsupervised Domain Adaptation for Medical Image Segmentation

Rethinking Disentanglement in Unsupervised Domain Adaptation...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Yan Wang Yixin Chen Yingying Zhang Haogang Zhu School of Instrumentation and Optoelectronic Engineering Beihang University Beijing China State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Biomedical Engineering Beihang University Beijing China BioMind Technology Zhongguancun Medical Engineering Center Beijing China School of Biological Science and Medical Engineering Beihang University Beijing China Blockchain Lab. Xiong’An Intelligent City Innovation Federation China

Domain adaptation has become an important topic because the trained neural networks from the source domain generally perform poorly in the target domain due to domain shifts, especially for medical image analysis. Previous DA methods mainly focus on disentangling domain features. However, it is based on feature independence, which often can not be guaranteed in reality. In this work, we present a new DA approach called Dimension-based Disentangled Dilated Domain Adaptation (D 4 A) to disentangle the storage locations between the features to tackle the problem of domain shift for medical image segmentation tasks without the annotations of the target domain. We use Adaptive Instance Normalization (AdaIN) to encourage the content information to be stored in the spatial dimension, and the style information to be stored in the channel dimension. In addition, we apply dilated convolution to preserve anatomical information avoiding the loss of information due to downsampling. We validate the proposed method for cross-modality medical image segmentation tasks on two public datasets, and the comparison experiments and ablation studies demonstrate the effectiveness of our method, which outperforms the state-of-the-art methods.

关键词：

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：