检索结果-内蒙古大学图书馆

Rore: robust and efficient antioxidant protein classification via a novel dimensionality reduction strategy based on learning of fewer features

引用

Genomics and informatics 2024年第1期22卷 1-10页

作者： Meng, Chaolu Hou, Yongqi Zou, Quan Shi, Lei Su, Xi Ju, Ying College of Computer and Information Engineering Inner Mongolia Agricultural University Hohhot China Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry Hohhot China School of Computer Science Inner Mongolia University Hohhot China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu China Department of Spine Surgery Changzheng Hospital Naval Medical University Huangpu District No. 415 Fengyang Road Shanghai China Foshan Women and Children Hospital Foshan China School of Informatics Xiamen University Xiamen China

In protein identification, researchers increasingly aim to achieve efficient classification using fewer features. While many feature selection methods effectively reduce the number of model features, they often cause information loss caused by merely selecting or discarding features, which limits classifier performance. To address this issue, we present Rore, an algorithm based on a feature-dimensionality reduction strategy. By mapping the original features to a latent space, Rore retains all relevant feature information while using fewer representations of the latent features. This approach significantly preserves the original information and overcomes the information loss problem associated with previous feature selection. Through extensive experimental validation and analysis, Rore demonstrated excellent performance on an antioxidant protein dataset, achieving an accuracy of 95.88% and MCC of 91.78%, using vectors including only 15 features. The Rore algorithm is available online at http://112.124.26.17:8021/Rore. © The Author(s) 2024.

关键词： Dimensionality reduction Protein classifier Protein sequence Robust feature

来源：评论

学校读者我要写书评

暂无评论

What improves the generalization of graph transformers? a theoretical dive into the self-attention and positional encoding 24

What improves the generalization of graph transformers? a th...

引用

Proceedings of the 41st International Conference on Machine Learning

作者： Hongkang Li Meng Wang Tengfei Ma Sijia Liu Zaixi Zhang Pin-Yu Chen Department of Electrical Computer and System Engineering Rensselaer Polytechnic Institute Troy NY Department of Biomedical Informatics Stony Brook University Stony Brook NY Department of Computer Science and Engineering Michigan State University East Lansing MI and MIT-IBM Watson AI Lab IBM Research MA Department of Computer Science and Engineering University of Science and Technology of China Hefei Anhui China IBM Thomas J. Watson Research Center Yorktown Heights NY

Graph Transformers, which incorporate self-attention and positional encoding, have recently emerged as a powerful architecture for various graph learning tasks. Despite their impressive performance, the complex non-convex interactions across layers and the recursive graph structure have made it challenging to establish a theoretical foundation for learning and generalization. This study introduces the first theoretical investigation of a shallow Graph Transformer for semi-supervised node classification, comprising a self-attention layer with relative positional encoding and a two-layer perceptron. Focusing on a graph data model with discriminative nodes that determine node labels and non-discriminative nodes that are class-irrelevant, we characterize the sample complexity required to achieve a desirable generalization error by training with stochastic gradient descent (SGD). This paper provides the quantitative characterization of the sample complexity and number of iterations for convergence dependent on the fraction of discriminative nodes, the dominant patterns, and the initial model errors. Furthermore, we demonstrate that self-attention and positional encoding enhance generalization by making the attention map sparse and promoting the core neighborhood during training, which explains the superior feature representation of Graph Transformers. Our theoretical results are supported by empirical experiments on synthetic and real-world benchmarks.

关键词：

来源：评论

学校读者我要写书评

暂无评论

AlGaAs soliton microcombs at room temperature

AlGaAs soliton microcombs at room temperature

引用

2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Wu, Lue Xie, Weiqiang Xiang, Chao Chang, Lin Yu, Yan Chen, Hao-Jing Yamamoto, Yoshihisa Bowers, John E. Vahala, Kerry J. Suh, Myoung-Gyun T. J. Watson Laboratory of Applied Physics California Institute of Technology PasadenaCA91125 United States Department of Electrical and Computer Engineering University of California Santa Barbara Santa BarbaraCA93106 United States Physics & Informatics Laboratories NTT Research Inc. SunnyvaleCA94085 United States

Soliton generation in AlGaAs microresonators at room temperation is reported for the first time. The destabilizing thermo-optic effect is shown to instead provide stability for high soliton repetition rates. The optic... 详细信息

ISBN: (纸本)9781957171258

关键词： Aluminum gallium arsenide

来源：评论

学校读者我要写书评

暂无评论

Multi-Criteria Decision Making for Machine Learning Algorithms Using AHP-VIKOR techniques: Case Study Adult Autism Diagnosis 5

Multi-Criteria Decision Making for Machine Learning Algorith...

引用

5th International Conference on engineering technology and its Applications, IICETA 2022

作者： Jassim, Mustafa Mohammed Ali, Manar Hassan Elamer, Amer S. Jaber, Mustafa Musa Mohammed, Mohammed Q. Alkhayyat, Ahmed Al-Farahidi University Department of Medical Instruments Engineering Techniques Baghdad10021 Iraq Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Informatics Baghdad10011 Iraq Dijlah University Collage Department of Computer Science Baghdad10021 Iraq Al-Esraa University College Department of Information Technology Baghdad10021 Iraq The Islamic University College of Technical Engineering Najaf Iraq

ISBN: (纸本)9781665472159

autism spectrum disorder is known as a neurodevelopmental disorder with sensory problems. Many infected adults die frequently due to the psychological conditions they live in. Machine learning helps doctors detect cases of autism spectrum disorders in adults. It has proven very successful in this field. In this study, after pre-processing and solving the data class imbalance problem the data we proposed to use MCDM methods to know prioritization and the best method to diagnose autism between four algorithms (SVM, Random Forest, Neural Network, and KNN), after obtaining the classification results we utilized AHP method to assign weights to each classification performance metrics based on expert assessment then he used both the obtained weights and the classification results to enter them into the VIKOR method to make a decision based on the weighted table where we would get the ranking of the algorithms. The random Forest method scores the best in this classification for diagnosing autism, with an accuracy rate of 0.9957 and sensitivity 0.9841. © 2022 IEEE.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Resource Provisioning Techniques in Multi-Access Edge Computing Environments: Outlook, Expression, and Beyond

引用

Mobile Information Systems 2022年第1期2022卷

作者： Durga, S. Daniel, Esther Onesimu, J. Andrew Sei, Yuichi Department of Information Technology Sri Krishna College of Engineering and Technology Coimbatore641008 India Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore641114 India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal576104 India Department of Informatics The University of Electro-Communications Chofu1828585 Japan

Mobile cloud computing promises a research foundation in information and communication technology (ICT). Multi-access edge computing is an intermediate solution that reduces latency by delivering cloud computing services close to IoT and mobile clients (MCs), hence addressing the performance issues of mobile cloud computing. However, the provisioning of resources is a significant and challenging process in mobile cloud-based environments as it organizes the heterogeneous sensing and processing capacities to provide the customers with an elastic pool of resources. Resource provisioning techniques must meet quality of service (QoS) considerations such as availability, responsiveness, and reliability to avoid service-level agreement (SLA) breaches. This investigation is essential because of the unpredictable change in service demands from diverse regions and the limits of MEC's available computing resources. In this study, resource provisioning approaches for mobile cloud computing are thoroughly and comparatively studied and classified as taxonomies of previous research. The paper concludes with an insightful summary that gives recommendations for future enhancements. © 2022 S. Durga et al.

关键词： Quality of service

来源：评论

学校读者我要写书评

暂无评论

Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks

arXiv

引用

arXiv 2024年

作者： Hou, Junlin Liu, Sicen Bie, Yequan Wang, Hongmei Tan, Andong Luo, Luyang Chen, Hao The Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong The Department of Engineering Shenzhen MSU-BIT University Shenzhen China The Department of Biomedical Informatics Harvard University Cambridge United States The Department of Computer Science and Engineering Department of Chemical and Biological Engineering Division of Life Science Hong Kong University of Science and Technology Hong Kong HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Futian Shenzhen China

The increasing demand for transparent and reliable models, particularly in high-stakes decision-making areas such as medical image analysis, has led to the emergence of eXplainable Artificial Intelligence (XAI). Post-hoc XAI techniques, which aim to explain black-box models after training, have raised concerns about their fidelity to model predictions. In contrast, Self-eXplainable AI (S-XAI) offers a compelling alternative by incorporating explainability directly into the training process of deep learning models. This approach allows models to generate inherent explanations that are closely aligned with their internal decision-making processes, enhancing transparency and supporting the trustworthiness, robustness, and accountability of AI systems in real-world medical applications. To facilitate the development of S-XAI methods for medical image analysis, this survey presents a comprehensive review across various image modalities and clinical applications. It covers more than 200 papers from three key perspectives: 1) input explainability through the integration of explainable feature engineering and knowledge graph, 2) model explainability via attention-based learning, concept-based learning, and prototype-based learning, and 3) output explainability by providing textual and counterfactual explanations. This paper also outlines desired characteristics of explainability and evaluation methods for assessing explanation quality, while discussing major challenges and future research directions in developing S-XAI for medical image analysis. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Distributed Gradient Clustering: Convergence and the Effect of Initialization

Distributed Gradient Clustering: Convergence and the Effect ...

引用

Asilomar Conference on Signals, Systems & computers

作者： Aleksandar Armacki Himkant Sharma Dragana Bajović Dušan Jakovetić Mrityunjoy Chakraborty Soummya Kar Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA USA Electronics and Electrical Communication Engineering Indian Institute of Technology Kharagpur Kharagpur India Department of Power Electronics and Computer Engineering Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia Department of Mathematics and Informatics Faculty of Sciences University of Novi Sad Novi Sad Serbia

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

We study the effects of center initialization on the performance of a family of distributed gradient-based clustering algorithms introduced in [1], that work over connected networks of users. In the considered scenario, each user contains a local dataset and communicates only with its immediate neighbours, with the aim of finding a global clustering of the joint data. We perform extensive numerical experiments, evaluating the effects of center initialization on the performance of our family of methods, demonstrating that our methods are more resilient to the effects of initialization, compared to centralized gradient clustering [2]. Next, inspired by the $K$ -means++ initialization [3], we propose a novel distributed center initialization scheme, which is shown to improve the performance of our methods, compared to the baseline random initialization.

关键词： computers Clustering methods Clustering algorithms Convergence

来源：评论

学校读者我要写书评

暂无评论

Time-dependent alternative route planning 20

Time-dependent alternative route planning

引用

20th Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems, ATMOS 2020

作者： Kontogiannis, Spyros Paraskevopoulos, Andreas Zaroliagis, Christos D. Department of Computer Science and Engineering University of Ioannina Greece Computer Technology Institute and Press "Diophantus" Patras Greece Department of Computer Engineering and Informatics University of Patras Greece Department of Computer Engineering and Informatics University of Patras Greece Computer Technology Institute and Press "Diophantus" Patras Greece

ISBN: (纸本)9783959771702

We present a new method for computing a set of alternative origin-to-destination routes in road networks with an underlying time-dependent metric. The resulting set is aggregated in the form of a time-dependent alternative graph and is characterized by minimum route overlap, small stretch factor, small size and low complexity. To our knowledge, this is the first work that deals with the time-dependent setting in the framework of alternative routes. Based on preprocessed minimum travel-time information between a small set of nodes and all other nodes in the graph, our algorithm carries out a collection phase for candidate alternative routes, followed by a pruning phase that cautiously discards uninteresting or low-quality routes from the candidate set. Our experimental evaluation on real time-dependent road networks demonstrates that the new algorithm performs much better (by one or two orders of magnitude) than existing baseline approaches. In particular, the entire alternative graph can be computed in less than 0.384sec for the road network of Germany, and in less than 1.24sec for that of Europe. Our approach provides also "quick-and-dirty" results of decent quality, in about 1/300 of the above mentioned query times for continental-size instances. © Spyros Kontogiannis, Andreas Paraskevopoulos, and Christos D. Zaroliagis

关键词： Travel time

来源：评论

学校读者我要写书评

暂无评论

New Paradigms in Manifold Learning for Knowledge Discovery and Inverse Design of Photonic Nanostructures 19

New Paradigms in Manifold Learning for Knowledge Discovery a...

引用

Quantum Sensing and Nano Electronics and Photonics XIX 2023

作者： Zandehshahvar, Mohammadreza Javani, Mohammad Hadigheh Kiarashi, Yashar Zhu, Muliang Brown, Tyler Bao, Daqian Marzban, Mahmoodreza Pourabolghasem, Reza Adibi, Ali School of Electrical and Computer Engineering Georgia Institute of Technology 778 Atlantic Dr NW GA30332 United States Clark Atlanta University 223 James P Brawley Dr SW AtlantaGA30314 United States Department of Biomedical Informatics Emory School of Medicine 100 Woodruff Circle AtlantaGA30322 United States

ISBN: (纸本)9781510659650

This talk is focused on using the intelligent aspects of machine learning (ML) for both the understanding of the subtle properties of nanophotonic devices and their inverse design to achieve a desired response. It will be shown that by reducing the dimensionality of the problem using manifold learning techniques and simplifying the resulting networks using pruning, the computation complexity of the underlying artificial intelligence (AI) algorithms will be considerably reduced. Furthermore, by optimally defining the loss function (or the metric) for AI algorithms, priceless information about the properties of photonic nanostructures can be uncovered while facilitating the better visualization of the input-output relationship in these nanostructures. In addition, the resulting manifold-learning algorithms can be optimally trained to facilitate the inverse design of such nanostructures while minimizing the structural complexity. This talk will provide the foundation for both knowledge discovery and design in photonic nanostructures using manifold learning and metric learning and their application to the highly desired metaphotonic structures as an example platform. © 2023 SPIE.

关键词： Nanostructures

来源：评论

学校读者我要写书评

暂无评论

Enhancing Graph Representation Learning with Localized Topological Features

arXiv

引用

arXiv 2025年

作者： Z., Yan Q., Zhao Z., Ye T., Ma L., Gao Z., Tang Y., Wang C., Chen Wangxuan Institute of Computer Technology Peking University China Computer Science and Engineering Department University of California San Diego United States Department of Biomedical Informatics Stony Brook University United States Halıcıoğlu Data Science Institute University of California San Diego United States Weill Cornell Medicine Cornell University United States

Representation learning on graphs is a fundamental problem that can be crucial in various tasks. Graph neural networks, the dominant approach for graph representation learning, are limited in their representation power. Therefore, it can be beneficial to explicitly extract and incorporate high-order topological and geometric information into these models. In this paper, we propose a principled approach to extract the rich connectivity information of graphs based on the theory of persistent homology. Our method utilizes the topological features to enhance the representation learning of graph neural networks and achieve state-of-the-art performance on various node classification and link prediction benchmarks. We also explore the option of end-to-end learning of the topological features, i.e., treating topological computation as a differentiable operator during learning. Our theoretical analysis and empirical study provide insights and potential guidelines for employing topological features in graph learning tasks. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive 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：