检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Zhang, Yinglin Higashita, Risa Fu, Huazhu Xu, Yanwu Zhang, Yang Liu, Haofeng Zhang, Jian Liu, Jiang Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Cixi Institute of Biomedical Engineering Chinese Academy of Sciences China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China Tomey Corporation Nagoya451-0051 Japan Global Big Data Technologies Centre University of Technology SydneyNSW Australia Inception Institute of Artificial Intelligence United Arab Emirates Intelligent Healthcare Unit Baidu Beijing100085 China

Corneal endothelial cell segmentation plays a vital role in quantifying clinical indicators such as cell density, coefficient of variation, and hexagonality. However, the corneal endothelium’s uneven reflection and the subject’s tremor and movement cause blurred cell edges in the image, which is difficult to segment, and need more details and context information to release this problem. Due to the limited receptive field of local convolution and continuous downsampling, the existing deep learning segmentation methods cannot make full use of global context and miss many details. This paper proposes a Multi-Branch hybrid Transformer Network (MBT-Net) based on the transformer and body-edge branch. Firstly, We use the convolutional block to focus on local texture feature extraction and establish long-range dependencies over space, channel, and layer by the transformer and residual connection. Besides, We use the body-edge branch to promote local consistency and to provide edge position information. On the self-collected dataset TM-EM3000 and public Alisarine dataset, compared with other State-Of-The-Art (SOTA) methods, the proposed method achieves an improvement. Copyright © 2021, The Authors. All rights reserved.

关键词： Endothelial cells

来源：评论

学校读者我要写书评

暂无评论

Structure-Inclusive Similarity Based Directed GNN: A Method that Can Control Information Flow to Predict Drug-Target Binding Affinity

SSRN

引用

SSRN 2024年

作者： Huang, Jipeng Sun, Chang Li, Minglei Tang, Rong Xie, Bin Wei, Jin-Mao College of Computer Science Nankai University Tianjin300071 China Centre for Bioinformatics and Intelligent Medicine Nankai University Tianjin300071 China Tianjin Key Laboratory of Network and Data Security Technology Tianjin300071 China College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China

Exploring the association between drugs and targets is essential for drug discovery and repurposing. Comparing with the traditional methods that regard the exploration as a binary classification task, predicting the drug-target binding affinity can provide more specific information. Many studies work based on the assumption that similar drugs may interact with the same target. These methods constructed a symmetric graph according to the undirected drug similarity or target similarity. Although these similarities can measure the difference between two molecules, it is unable to analyze the inclusion relationship of their substructure. For example, if drug A contains all the substructures of drug B, then in the message-passing mechanism of the graph neural network, drug A should acquire all the properties of drug B, while drug B should only obtain some of the properties of A. To this end, we proposed a structure-inclusive similarity (SIS) which measures the similarity of two drugs by considering the inclusion relationship of their substructures. Based on SIS, we constructed a drug graph and a target graph, respectively, and predicted the binding affinities between drugs and targets by a graph convolutional network-based model. Experimental results show that considering the inclusion relationship of the substructure of two molecules can effectively improve the accuracy of the prediction model. The performance of our SIS-based prediction method outperforms several state-of-the-art methods for drug-target binding affinity prediction. The case studies demonstrate that our model is a practical tool to predict the binding affinity between drugs and targets. © 2024, The Authors. All rights reserved.

关键词： Molecules

来源：评论

学校读者我要写书评

暂无评论

Triplet Deep Subspace Clustering via Self-Supervised data Augmentation

Triplet Deep Subspace Clustering via Self-Supervised Data Au...

引用

IEEE International Conference on data Mining (ICDM)

作者： Zhao Zhang Xianzhen Li Haijun Zhang Yi Yang Shuicheng Yan Meng Wang School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China School of Computer Science and Technology Soochow University Suzhou China Harbin Institute of Technology (Shenzhen) Shenzhen China Centre for Artificial Intelligence University of Technology Sydney Sydney NSW Australia Sea AI Lab (SAIL) & National University of Singapore Singapore

ISBN: (纸本)9781665423991

Deep subspace clustering (DSC) with the auto-encoder and self-expression layer is of great concern due to encouraging performance. However, existing methods usually adopt a “single-task” strategy based on a single dataset, without considering other related tasks or data. As such, they cannot discover other useful information to improve the clustering task. Besides, the local structure preservation of the latent codes in mapping is usually ignored. In this paper, we therefore present an effective “multi-task” strategy via the self-supervised data augmentation, and propose a new end-to-end trainable Triplet Deep Subspace Clustering Network (TDSC-net). Specifically, TDSC-net firstly generates triplet data (i.e., anchor, positive and negative data) from input data by a spectral clustering module and a self-supervised data augmentation module. This can enable it to inherit the merits of self-supervised learning and multitask learning implicitly. After that, TDSC-net builds a triplet deep autoencoder network with a self-expression layer, which takes the triplet data as input, where they share the common network layers (i.e., autoencoder and self-expression layers) over the triple tasks for complementary learning and mutual supervision. A triplet loss is also included to retain the local information of deep latent codes, which will also benefit the self-expression. Furthermore, TDSC-net separates the self-expression layer from decoding process to improve the efficiency of reconstruction. Extensive results on several public datasets demonstrate the effectiveness of our triplet-task DSC strategy.

关键词： Codes Conferences Multitasking Decoding data mining Task analysis

来源：评论

学校读者我要写书评

暂无评论

WiLoc: Encoding-based WiFi Indoor Localization

WiLoc: Encoding-based WiFi Indoor Localization

引用

International Conference on Indoor Positioning and Indoor Navigation (IPIN)

作者： Zhao Huang Mikko Valkama Juan Zhang Meng Xu Cunyi Yin Minglei Guan Computer and Information Science Northumbria University Newcastle UK Department of Electrical Engineering Tampere University Tampere Finland School of Information Technology & Management University of International Business and Economics Beijing China Centre for Intelligent Multidimensional Data Analysis Limited Hongkong China School of Artificial Intelligence Shenzhen Polytechnic University Shenzhen China

ISBN: (数字)9798350366402

ISBN: (纸本)9798350366419

WiFi Indoor localization plays a crucial role in an emerging application domain for tracking indoor people, however, the serious issue is that the WiFi signals from access points (APs) vary greatly over time and the deployment structure of APs may be changed, for example, some APs are replaced or removed over time, which cause localization accuracy reduced. To solve this problem, this paper presents WiLoc, a Long-term WiFi localization with Lightweight Siamese Neural Network. This method introduces a Siamese neural encoder-based framework to learn the similarity between three inputs, where the Siamese network only consists of three linear layers without any convolutional layer or transformer. The triplet loss function is utilized to supervise the training of the feature encoder. Then, the encodings from this encoder are input to K-Nearest Neighbors (KNN) to predict the user’s positions. Extensive experiments on the UJI dataset, show the proposed WiLoc can effectively relieve the degradation of localization accuracy over time compared to the state-of-the-art algorithms, the degradation is reduced from 51% to 12.1%, and the average localization error is 2.06 m.

关键词： Location awareness Degradation Training Accuracy Convolution Neural networks Nearest neighbor methods Transformers Real-time systems Wireless fidelity

来源：评论

学校读者我要写书评

暂无评论

Language Guided Robotic Grasping with Fine-Grained Instructions

Language Guided Robotic Grasping with Fine-Grained Instructi...

引用

IEEE/RSJ International Conference on intelligent Robots and Systems (IROS)

作者： Qiang Sun Haitao Lin Ying Fu Yanwei Fu Xiangyang Xue school of Data Science and Shanghai Key Lab of Intelligent Information Processingxs Fudan University and Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence Zhejiang Normal University Jinhua China Fudan University China School of statistics and information Shanghai University of International Business and Economics China Beijing Institute of Technology China

Given a single RGB image and the attribute-rich language instructions, this paper investigates the novel problem of using Fine-grained instructions for the Language guided robotic Grasping (FLarG). This problem is made challenging by learning fine-grained language descriptions to ground target objects. Recent advances have been made in visually grounding the objects simply by several coarse attributes [1]. However, these methods have poor performance as they cannot well align the multi-modal features, and do not make the best of recent powerful large pre-trained vision and language models, e.g., CLIP. To this end, this paper proposes a FLarG pipeline including stages of CLIP-guided object localization, and 6-DoF category-level object pose estimation for grasping. Specially, we first take the CLIP-based segmentation model CRIS as the backbone and propose an end-to-end DyCRIS model that uses a novel dynamic mask strategy to well fuse the multi-level language and vision features. Then, the well-trained instance segmentation backbone Mask R-CNN is adopted to further improve the predicted mask of our DyCRIS. Finally, the target object pose is inferred for the robotics grasping by using the recent 6-DoF object pose estimation method. To validate our CLIP-enhanced pipeline, we also construct a validation dataset for our FLarG task and name it RefNOCS. Extensive results on RefNOCS have shown the utility and effectiveness of our proposed method. The project homepage is available at https://***/.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Heterogeneous influence of individuals’ behavior on mask efficacy in gathering environments

引用

Frontiers of Engineering Management 2022年第4期9卷 550-562页

作者： Haochen SUN Xiaofan LIU Zhanwei DU Ye WU Haifeng ZHANG Xiaoke XU College of Information and Communication Engineering Dalian Minzu UniversityDalian 116600China Web Mining Laboratory Department of Media and CommunicationCity University of Hong KongHong KongChina WHO Collaborating Centre for Infectious Disease Epidemiology and Control School of Public HealthLi Ka Shing Faculty of MedicineThe University of Hong KongHong KongChina Laboratory of Data Discovery for Health Hong Kong Science and Technology ParkHong KongChina Computational Communication Research Center Beijing Normal University(Zhuhai)Zhuhai 519087China School of Journalism and Communication Beijing Normal UniversityBeijing 100875China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Mathematical ScienceAnhui UniversityHefei 230601China

Wearing masks is an easy way to operate and popular measure for preventing *** masks can slow down the spread of viruses,their efficacy in gathering environments involving heterogeneous person-to-person contacts remains ***,we aim to investigate the epidemic prevention effect of masks in different real-life gathering *** study uses four real interpersonal contact datasets to construct four empirical networks to represent four gathering *** transmission of COVID-19 is simulated using the Monte Carlo simulation *** heterogeneity of individuals can cause mask efficacy in a specific gathering environment to be different from the baseline efficacy in general ***,the heterogeneity of gathering environments causes the epidemic prevention effect of masks to *** masks can greatly reduce the probability of clustered epidemics and the infection scale in primary schools,high schools,and ***,the use of masks alone in primary schools and hospitals cannot control *** high schools with social distancing between classes and in workplaces where the interpersonal contact is relatively sparse,masks can meet the need for *** the heterogeneity of individual behavior,if individuals who are more active in terms of interpersonal contact are prioritized for mask-wearing,the epidemic prevention effect of masks can be ***,asymptomatic infection has varying effects on the prevention effect of masks in different *** effect can be weakened or eliminated by increasing the usage rate of masks in high schools and ***,the effect on primary schools and hospitals cannot be *** study contributes to the accurate evaluation of mask efficacy in various gathering environments to provide scientific guidance for epidemic prevention.

关键词： COVID-19 masks behavioral heterogeneity asymptomatic infection

来源：评论

学校读者我要写书评

暂无评论

Dictionary Pair-based data-Free Fast Deep Neural Network Compression

Dictionary Pair-based Data-Free Fast Deep Neural Network Com...

引用

IEEE International Conference on data Mining (ICDM)

作者： Yangcheng Gao Zhao Zhang Haijun Zhang Mingbo Zhao Yi Yang Meng Wang School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Harbin Institute of Technology (Shenzhen) Xili University Town Shenzhen China City University of Hong Kong Hong Kong SAR Centre for Artificial Intelligence University of Technology Sydney Sydney NSW Australia

ISBN: (纸本)9781665423991

Deep neural network (DNN) compression can reduce the memory footprint of deep networks effectively, so that the deep model can be deployed on the portable devices. However, most of the existing model compression methods cost lots of time, e.g., vector quantization or pruning, which makes them inept to the real-world applications that need fast online computation. In this paper, we therefore explore how to accelerate the model compression process by reducing the computation cost. Then, we propose a new deep model compression method, termed Dictionary Pair-based data-Free Fast DNN Compression, which aims at reducing the memory consumption of DNNs without extra training and can greatly improve the compression efficiency. Specifically, our proposed method performs tensor decomposition on the DNN model with a fast dictionary pair learning-based reconstruction approach, which can be deployed on different layers (e.g., convolution and fully-connection layers). Given a pre-trained DNN model, we first divide the parameters (i.e., weights) of each layer into a series of partitions for dictionary pair-based fast reconstruction, which can potentially discover more fine-grained information and provide the possibility for parallel model compression. Then, dictionaries of less memory occupation are learned to reconstruct the weights. Extensive experiments on popular DNNs (i.e., VGG-16, ResNet-18 and ResNet-50) showed that our proposed weight compression method can significantly reduce the memory footprint and speed up the compression process, with less performance loss.

关键词： Deep learning Training Performance evaluation Dictionaries Tensors Costs Computational modeling

来源：评论

学校读者我要写书评

暂无评论

A Dynamic Adaptive Search Algorithm for CoSpace Rescue Robot

A Dynamic Adaptive Search Algorithm for CoSpace Rescue Robot

引用

International Conference on Robotics and Automation Engineering (ICRAE)

作者： Li Chengbo Wang Xiaoming Huang Zixi Pang Guangyao Han Xinyu Lin Yaguang Dalian Free Trade Zone No. 1 High School Dalian China Key Laboratory of Modern Teaching Technology Ministry of Education Xi'an China School of Computer Science Shaanxi Normal University Xi'an China Shenzhen Middle School Shenzhen China School of Data Science and Software Engineering Wuzhou University Wuzhou China Advanced Robotics and Intelligent Control Centre Singapore Polytechnic Singapore

ISBN: (纸本)9781665406987

The application of robots to assist humans in earthquake rescue has always been an important topic that researchers are constantly studying. CoSpace Rescue is an abstract modeling and simulation of earthquake rescue and a simulation platform for studying robot rescue search algorithms. Its mathematical model is a multi-objective optimization problem. In this paper, we propose a search algorithm design framework for the CoSpace Rescue robot. Based On this framework and the idea of the heuristic search algorithm, we design and implement the “Multi-objective Optimal Dynamic Adaptive On-Demand Search Algorithm”. The results of the experiment show that the algorithm can dynamically adapt to the changes in the environment and significantly improve the search efficiency of the rescue robot.

关键词： Automation Heuristic algorithms Earthquakes Mathematical models Scheduling Optimization Rescue robots

来源：评论

学校读者我要写书评

暂无评论

scMonica: Single-cell Mosaic Omics Nonlinear Integration and Clustering Analysis

scMonica: Single-cell Mosaic Omics Nonlinear Integration and...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiaoli Li Rui Zhang Saba Aslam Huijun Li Yuxi Chen Zequn Zhang Ruey-Song Huang Hongyan Wu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Centre for Cognitive and Brain Sciences University of Macau Macau SAR China Faculty of Science and Technology University of Macau Macau SAR China Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Beijing China Faculty of Health Sciences University of Macau Macau SAR China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Single-cell mosaic integration has revolutionized our understanding of cellular heterogeneity, offering unprecedented resolution into cellular states and contexts. While large language models (LLMs) have achieved success in the analysis of single-cell omics data, their application specifically focused on the integration of mosaic data remains limited. Current computational approaches often fall short in addressing these challenges. They frequently depend on assumptions of data completeness and uniform quality, failing to manage the variability and noise effectively introduced by missing data. Moreover, these models might not efficiently process large, diverse datasets or account for biological heterogeneity and long-ranging dependencies across various data types. To address these shortcomings, we introduce the Single-cell Mosaic Omics Nonlinear Integration and Clustering Analysis (scMonica) framework, which employs a LSTM-transformer hybrid architecture. This innovative model combines the strengths of Long Short-Term Memory (LSTM) networks, which excel at capturing long-range dependencies within sequential gene expression patterns, with transformers, renowned for their attention mechanisms that handle the complex, non-linear interactions characteristic of multi-layered datasets. By leveraging these complementary strengths, our approach enhances the integration process significantly, allowing for nuanced management of the intrinsic heterogeneity and sparsity of mosaic datasets. Comprehensive evaluations demonstrate the robustness and effectiveness of our approach, offering unparalleled versatility and accuracy in multi-omics data analysis. These advancements underscore scMonica’s potential to drive significant insights in single-cell developmental biology, oncology, and beyond. We discuss the underlying technologies, analyze their applications, and contemplate future directions that promise to extend the boundaries of both research and clinical domains.

关键词： Biological system modeling Microprocessors Large language models Noise Computer architecture Transformers Oncology Robustness Gene expression Long short term memory

来源：评论

学校读者我要写书评

暂无评论

ResfEANet: ResNet-fused External Attention Network for Tuberculosis Diagnosis using Chest X-ray Images

引用

Computer Methods and Programs in Biomedicine Update 2024年 5卷

作者： Ejiyi, Chukwuebuka Joseph Qin, Zhen Nnani, Ann O Deng, Fuhu Ejiyi, Thomas Ugochukwu Ejiyi, Makuachukwu Bennedith Agbesi, Victor Kwaku Bamisile, Olusola Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu China College of Environmental Science and Engineering Hohai University Nanjing China Department of Pure and Industrial Chemistry University of Nigeria Nsukka Enugu Nigeria Pharmacy Department University of Nigeria Nsukka Enugu Nigeria School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Sichuan Industrial Internet Intelligent Monitoring and Application Engineering Technology Research Centre Chengdu University of Technology Chengdu China

Pulmonary tuberculosis (TB), the most prevalent form of TB, remains a major global public health concern, contributing to more than a million deaths each year. The accurate and timely diagnosis of this disease is of paramount importance for effective control and treatment. Chest X-ray (CXR) images have emerged as a valuable tool for screening lung diseases, including TB, owing to their cost-effectiveness and non-invasiveness. Despite advancements in technology, the challenges associated with interpreting CXR images persist, primarily due to the scarcity of trained radiologists. This underscores the pressing need for an automated and cost-effective computer-aided system capable of diagnosing TB, assisting medical practitioners in distinguishing between TB-positive and negative CXR scans. In response to this need, we introduce an innovative approach called ResNet-fused External Attention Network (ResfEANet). This network excels in accurately classifying TB from CXR images, achieving remarkable levels of accuracy and sensitivity. ResfEANet is built upon ResNet and incorporates an External Attention mechanism, albeit with fewer residual network blocks than ResNet-50 resulting in a relatively shallow network with fewer layers. This approach proves highly effective in feature extraction and yields competitive results in the classification of TB. Our method was employed to train a model that demonstrated an impressive accuracy rate of 97.59% and a remarkable sensitivity of 100% in binary classification tasks with optimal computational cost. These outcomes suggest that our proposed approach has the potential to serve as a valuable secondary tool in clinical decision-making, providing crucial assistance to radiologists and healthcare professionals. © 2023

关键词： Chest X-ray images External Attention ResfEANet ResNet Tuberculosis

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：