Mobile technology is being increasingly adopted in teleconsultation for its convenience and mobility. Although widely accepted by physicians for informal online consultations, the effectiveness of mobile platforms in ...
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Human mobility trajectories are fundamental resources for analyzing mobile behaviors in urban computing ***,these trajectories,typically collected from location-based services,often suffer from sparsity and irregulari...
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Human mobility trajectories are fundamental resources for analyzing mobile behaviors in urban computing ***,these trajectories,typically collected from location-based services,often suffer from sparsity and irregularity in *** support the development of mobile applications,there is a need to recover or estimate missing locations of unobserved time slots in these trajectories at a fine-grained spatial–temporal *** methods for trajectory recovery rely on either individual user trajectories or collective mobility patterns from all *** potential to combine individual and collective patterns for precise trajectory recovery remains ***,current methods are sensitive to the heterogeneous temporal distributions of the observable trajectory *** this paper,we propose CLMove(where CL stands for contrastive learning),a novel model designed to capture multilevel mobility patterns and enhance robustness in trajectory *** features a two-stage location encoder that captures collective and individual mobility *** graph neural network based networks in CLMove explore location transition patterns within a single trajectory and across various user *** also design a trajectory-level contrastive learning task to improve the robustness of the *** experimental results on three representative real-world datasets demonstrate that our CLMove model consistently outperforms state-of-the-art methods in terms of trajectory recovery accuracy.
Relation extraction is an important task in natural language processing. Existing relation extraction tasks usually use data augmentation to construct positive and negative samples for contrastive learning training. A...
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MicroRNAs(miRNAs)are closely related to numerous complex human diseases,therefore,exploring miRNA-disease associations(MDAs)can help people gain a better understanding of complex disease *** increasing number of compu...
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MicroRNAs(miRNAs)are closely related to numerous complex human diseases,therefore,exploring miRNA-disease associations(MDAs)can help people gain a better understanding of complex disease *** increasing number of computational methods have been developed to predict ***,the sparsity of the MDAs may hinder the performance of many *** addition,many methods fail to capture the nonlinear relationships of miRNA-disease network and inadequately leverage the features of network and neighbor *** this study,we propose a deep matrix factorization model with variational autoencoder(DMFVAE)to predict potential *** first decomposes the original association matrix and the enhanced association matrix,in which the enhanced association matrix is enhanced by self-adjusting the nearest neighbor method,to obtain sparse vectors and dense vectors,***,the variational encoder is employed to obtain the nonlinear latent vectors of miRNA and disease for the sparse vectors,and meanwhile,node2vec is used to obtain the network structure embedding vectors of miRNA and disease for the dense ***,sample features are acquired by combining the latent vectors and network structure embedding vectors,and the final prediction is implemented by convolutional neural network with channel *** evaluate the performance of DMFVAE,we conduct five-fold cross validation on the HMDD v2.0 and HMDD v3.2 datasets and the results show that DMFVAE performs ***,case studies on lung neoplasms,colon neoplasms,and esophageal neoplasms confirm the ability of DMFVAE in identifying potential miRNAs for human diseases.
Traditional graph classification requires large amounts of lab.led data, which is expensive and time-consuming to acquire, especially in some special scenarios that domain knowledge is indispensable for lab.ling graph...
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Recent developments in Neural Radiance Fields (NeRF) have showcased notable progress in the synthesis of novel views. Nevertheless, there is limited research on inpainting 3D scenes using implicit representations. Tra...
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In the past few years, the COVID-19 pandemic has affected all aspects of people's daily life. In particular, people in large cities have also been greatly affected. How to accurately and timely evaluate and analyz...
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Single-cell RNA sequencing (scRNA-seq) technology has emerged as a valuable tool for classifying cell types across various species, tissues, and environmental conditions, thereby advancing the field of life sciences. ...
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作者:
Dong, JiayiWang, FeiFudan University
Shanghai Key Lab of Intelligent Information Processing School of Computer Science and Technology Shanghai China
Since single-cell RNA sequencing (scRNA-seq) has revolutionized the study of cellular dynamics, the construction of gene relationships based on dynamic information has attracted much attention. However, the sparsity a...
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Cells serve as the fundamental building blocks of living organisms and necessitate the utilization of diverse microscopic imaging techniques for observation. Nevertheless, there exist numerous limitations within phase...
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