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Toward an accurate mobility trajectory recovery using contrastive learning

Frontiers of information technology & Electronic Engineering 2024年第11期25卷 1479-1496页

作者： Yushan LIU Yang CHEN Jiayun ZHANG Yu XIAO Xin WANG Shanghai Key Lab of Intelligent Information Processing School of Computer ScienceFudan UniversityShanghai 200433China Department of Information and Communications Engineering Aalto UniversityEspoo FI-02150Finland

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.

关键词： Human mobility Mobility trajectory recovery Contrastive learning

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Gaining Technological Autonomy and Social-emotional Support: A Case Study of How and Why Chinese Older Adults Engage with a Semi-acquaintance Online Community

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Proceedings of the ACM on Human-computer Interaction 2024年第CSCW2期8卷 1-35页

作者： Qian, Zhigu Fu, Jiaojiao Zhou, Yangfan School of Computer Science Fudan University Shanghai Key Laboratory of Intelligent Information Processing Shanghai China School of Information Science and Engineering East China University of Science and Technology Shanghai China

Older adults are often underserved and marginalized in technology engagement due to their reluctance and the barriers they face in adopting and engaging with mainstream technology. However, Pinxiaoquan, a social feature of an e-commerce platform in China, has gained a large number of older users. This work investigates how and why Chinese older adults use Pinxiaoquan, aiming to unveil the underlying logic and inspire technology-inclusive design for older adults. To this end, we conducted a mixed-methods qualitative study over two years, which included online observation, and semi-interview. We found that Pinxiaoquan’s success among Chinese older adults is mainly due to its ability to inspire technological autonomy and provide social-emotional support. Rather than simply lowering technical barriers or asking them to seek assistance outside the platform, Pinxiaoquan builds a semi-acquaintance online community based on location and social ties that allows older adults to realize technical support mutually. Pinxiaoquan also fulfills their social-emotional needs, such as free online expression, memory creation and preservation, relationship expansion and maintenance, and a sense of value. Our research contributes to the HCI community by highlighting the importance of improving older adults’ technology autonomy following their specific social and cultural background and social-emotional needs. This work also provides unique insights and implications for building inclusive technology for the growing aging population. © 2024 Copyright held by the owner/author(s).

关键词： Marketplaces

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Chinese Named Entity Recognition Augmented with Lexicon Memory

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Journal of computer science & technology 2023年第5期38卷 1021-1035页

作者：周奕郑骁庆黄萱菁 School of Computer Science Fudan UniversityShanghai 200438China Shanghai Key Laboratory of Intelligent Information Processing Shanghai 200438China

Inspired by the concept of content-addressable retrieval from cognitive science,we propose a novel fragment-based Chinese named entity recognition(NER)model augmented with a lexicon-based memory in which both character-level and word-level features are combined to generate better feature representations for possible entity *** that the boundary information of entity names is particularly useful to locate and classify them into pre-defined categories,position-dependent features,such as prefix and suffix,are introduced and taken into account for NER tasks in the form of distributed *** lexicon-based memory is built to help generate such position-dependent features and deal with the problem of out-of-vocabulary *** results show that the proposed model,called LEMON,achieved state-of-the-art performance with an increase in the Fl-score up to 3.2%over the state-of-the-art models on four different widely-used NER datasets.

关键词： named entity recognition(NER) lexicon-based memory content-addressable retrieval position-dependent feature neural network

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Hi-NeRF: Hybridizing 2D Inpainting with Neural Radiance Fields for 3D Scene Inpainting 17th

Hi-NeRF: Hybridizing 2D Inpainting with Neural Radiance Fie...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Huang, Xianliang Chen, Shuhang Zhong, Zhizhou Gou, Jiajie Guan, Jihong Zhou, Shuigeng Shanghai Key Lab of Intelligent Information Processing and School of Computer Science Fudan University Shanghai China School of Computer Science and Technology Tongji University Shanghai China

ISBN: (纸本)9789819609710

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. Traditional approaches utilizing 3D networks for direct 3D inpainting often falter in high-resolution settings, mainly due to GPU memory constraints. This paper introduces Hi-NeRF, an innovative 3D inpainting approach designed to remove arbitrary 3D objects by hybridizing 2D inpainting strategies with NeRF techniques. Recognizing that prevailing 2D inpainting methods often fail to grasp the 3D geometric intricacies of scenes, we leverage the unique capability of NeRF in capturing these structures. Additionally, we propose a multi-view perceptual loss (MVPL) to harness multi-view data, ensuring that 2D inpainting and implicit 3D representations can mutually compensate for each other. Furthermore, we refine the output from the Segment Anything Model (SAM) using image dilation to produce accurate multi-view masks. To finalize the process, we employ Instant-NGP to efficiently retrieve 3D-consistent scenes from 3D-consistent inpainted images. As there is no multi-view 3D scene datasets with corresponding masks, we construct both real-world and synthetic scenes for the multi-view 3D scene inpainting task, which serves as a benchmark dataset. Experimental results on both indoor and outdoor scenes highlight the superiority of our approach over the existing 2D inpainting methods and NeRF-based baselines. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： 3D modeling

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DMFVAE:miRNA-disease associations prediction based on deep matrix factorization method with variational autoencoder

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Frontiers of computer science 2024年第6期18卷 259-270页

作者： Pijing WEI Qianqian WANG Zhen GAO Ruifen CAO Chunhou ZHENG Information Materials and Intelligent Sensing Laboratory of Anhui Province Institutes of Physical Science and Information TechnologyAnhui UniversityHefei 230601China Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei 230601China Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial IntelligenceAnhui UniversityHefei 230601China

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.

关键词： miRNA-disease association deep matrix factorization self-adjusted nearest neighbor variational encoder network structure

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HARNESSING JOINT RAIN-/DETAIL-AWARE REPRESENTATIONS TO ELIMINATE INTRICATE RAINS 12

HARNESSING JOINT RAIN-/DETAIL-AWARE REPRESENTATIONS TO ELIMI...

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12th International Conference on Learning Representations, ICLR 2024

作者： Ran, Wu Ma, Peirong He, Zhiquan Ren, Hao Lu, Hong School of Computer Science Fudan University China Shanghai Key Lab of Intelligent Information Processing China

Recent advances in image deraining have focused on training powerful models on mixed multiple datasets comprising diverse rain types and backgrounds. However, this approach tends to overlook the inherent differences among rainy images, leading to suboptimal results. To overcome this limitation, we focus on addressing various rainy images by delving into meaningful representations that encapsulate both the rain and background components. Leveraging these representations as instructive guidance, we put forth a Context-based Instance-level Modulation (CoI-M) mechanism adept at efficiently modulating CNN- or Transformer-based models. Furthermore, we devise a rain-/detail-aware contrastive learning strategy to help extract joint rain-/detail-aware representations. By integrating CoI-M with the rain-/detail-aware Contrastive learning, we develop CoIC, an innovative and potent algorithm tailored for training models on mixed datasets. Moreover, CoIC offers insight into modeling relationships of datasets, quantitatively assessing the impact of rain and details on restoration, and unveiling distinct behaviors of models given diverse inputs. Extensive experiments validate the efficacy of CoIC in boosting the deraining ability of CNN and Transformer models. CoIC also enhances the deraining prowess remarkably when real-world dataset is included. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Rain

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Simulation extractable SNARKs based on target linearly collision-resistant oracle

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science China(Technological sciences) 2024年第9期67卷 2853-2866页

作者： WANG LiGuan LI Yuan ZHANG ShuangJun CAI DongLiang KAN HaiBin Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan UniversityShanghai 200433China Shanghai Engineering Research Center of Blockchain Shanghai 200433China Yiwu Research Institute of Fudan University Yiwu 322000China

The famous zero-knowledge succinct non-interactive arguments of knowledge(zk-SNARK) was proposed by Groth in ***, the construction is based on quadratic arithmetic programs which are highly efficient concerning the proof length and the verification complexity. Since then, there has been much progress in designing zk-SNARKs, achieving stronger security,and simulated extractability, which is analogous to non-malleability and has broad applications. In this study, following Groth's pairing-based zk-SNARK, a simulation extractability zk-SNARK under the random oracle model is constructed. Our construction relies on a newly proposed property named target linearly collision-resistant, which is satisfied by random oracles under discrete logarithm assumptions. Compared to the original Groth16 zk-SNARK, in our construction, both parties are allowed to use such a random oracle, aiming to get the same random number. The resulting proof consists of 3 group elements and only 1 pairing equation needs to be verified. Compared to other related works, our construction is shorter in proof length and simpler in verification while preserving simulation extractability. The results also extend to achieve subversion zero-knowledge SNARKs.

关键词： quadratic arithmetic program simulation extractability subversion zero-knowledge succinct non-interactive arguments of knowledge target linearly collision-resistant

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Weakly-Supervised Graph Classification with Even a Single key Subgraph Per Class 24

Weakly-Supervised Graph Classification with Even a Single Ke...

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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Zhang, Lu Zhang, Chenbo Guan, Jihong Zhou, Shuigeng School of Computer Science Fudan University Shanghai Key Lab of Intelligent Information Processing China Tongji University Department of Computer Science & Technology China

ISBN: (纸本)9798331506681

Traditional graph classification requires large amounts of labeled data, which is expensive and time-consuming to acquire, especially in some special scenarios that domain knowledge is indispensable for labeling graphs. Observing that some key subgraphs can determine the properties of graphs (e.g. the toxicity of drug molecules depend on some toxic functional groups), in this paper we explore to classify graphs using unlabeled graphs plus a small number of key subgraphs for each class, which is called weakly-supervised graph classification. To this end, we develop the WeGraph method, where the graph classifier is trained with subgraph-based self-supervised learning and divergence- minimization based fine-tuning. Moreover, we design a key subgraph extraction algorithm to iteratively extract and update the key subgraphs, which makes the training process a closed loop. We conduct extensive experiments on different types of graph datasets to evaluate the effectiveness of WeGraph. Experimental results show that WeGraph can achieve high performance even when only one key subgraph is provided for each class. © 2024 IEEE.

关键词： Self-supervised learning

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GROD: Joint Inference of Gene Regulatory Networks and Data Imputation in Single-Cell RNA Sequencing with Temporal Consideration

GROD: Joint Inference of Gene Regulatory Networks and Data I...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Dong, Jiayi Wang, Fei Fudan University Shanghai Key Lab of Intelligent Information Processing School of Computer Science and Technology Shanghai China

ISBN: (纸本)9798350337488

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 and dropout events inherent in scRNA-seq data present challenges for downstream analysis like Gene Regulatory Network (GRN) inference. Existing data imputation methods have yielded good results beneficial for cell clustering, but it does not adequately consider gene expression dynamics. To address this, we introduce GROD, a novel deep learning approach that simultaneously infers GRN and imputes scRNA-seq data from a temporal perspective. GROD consists of three key components: an encoder, a graph learner, and a decoder. Experimental results demonstrate that GROD outperforms existing methods in both GRN inference and data imputation tasks, providing superior accuracy in capturing gene regulatory relationships and accurately imputing missing values. By integrating these two tasks, GROD enables more accurate downstream analysis and facilitates deeper insights into cellular dynamics. © 2023 IEEE.

关键词： cellular dynamics data imputation gene regulatory network scRNA-seq

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scGAT: A Cell-Type Annotation Framework for Single-Cell Transcriptomics Using Graph Attention Network and Meta Learning 1

scGAT: A Cell-Type Annotation Framework for Single-Cell Tran...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Dong, Jiayi Li, Ming Wang, Fei School of Computer Science and Technology Fudan University Shanghai Key Lab of Intelligent Information Processing Shanghai China

ISBN: (纸本)9798350358780

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. Automatic cell annotation plays a crucial role in bioinformatics, addressing the challenges associated with time-consuming and labor-intensive manual cell annotation. Although numerous cell annotation algorithms exist, their performance is often constrained by the quality of reference data and inherent technical noise in biological experiments. To address these challenges, we propose the integration of a heterogeneous graph neural network into our cell annotation model, named scGAT. This integration leverages the intricate relationships between expressed genes and cells while disregarding unexpressed genes. Additionally, we introduce a novel approach that utilizes prototype cells to mitigate technical noise. However, the use of prototype cells may lead to limitations in sample size, prompting us to employ meta-learning techniques to determine the model's initial parameters. To assess the effectiveness of scGAT in cell annotation tasks, we conducted comparative analyses using real datasets against other models. The experimental results clearly demonstrate that scGAT significantly outperforms alternative methods. © 2023 IEEE.

关键词： Bioinformatics

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