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2025 CHI Conference on Human Factors in computing Systems, CHI 2025

作者： Chan, Ka I Hu, Siying Wang, Yuntao Xu, Xuhai Lu, Zhicong Shi, Yuanchun Institute Tsinghua University Beijing China Department of Computer Science City University of Hong Kong Hong Kong Key Laboratory of Pervasive Computing Ministry of Education Department of Computer Science and Technology Tsinghua University Beijing China School of Computer Science and Technology Qinghai University Xining China Google New York CityNY United States Department of Computer Science George Mason University FairfaxVA United States Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China Qinghai University Xining China

ISBN: (纸本)9798400713941

It is pivotal for patients to receive accurate health information, diagnoses, and timely treatments. However, in China, the significant imbalanced doctor-to-patient ratio intensifies the information and power asymmetries in doctor-patient relationships. Health information-seeking, which enables patients to collect information from sources beyond doctors, is a potential approach to mitigate these asymmetries. While HCI research predominantly focuses on common chronic conditions, our study focuses on specialized disorders, which are often familiar to specialists but not to general practitioners and the public. With Hemifacial Spasm (HFS) as an example, we aim to understand patients' health information and top-tier1 medical resource seeking journeys in China. Through interviews with three neurosurgeons and 12 HFS patients from rural and urban areas, and applying Actor-Network Theory, we provide empirical insights into the roles, interactions, and workflows of various actors in the health information-seeking network. We also identified five strategies patients adopted to mitigate asymmetries and access top-tier medical resources, illustrating these strategies as subnetworks within the broader health information-seeking network and outlining their advantages and challenges. © 2025 Copyright held by the owner/author(s).

关键词： Diagnosis

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UniDSeg: Unified Cross-Domain 3D Semantic Segmentation via Visual Foundation Models Prior 38

UniDSeg: Unified Cross-Domain 3D Semantic Segmentation via V...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wu, Yao Xing, Mingwei Zhang, Yachao Luo, Xiaotong Xie, Yuan Qu, Yanyun School of Informatics Xiamen University China Institute of Artificial Intelligence Xiamen University China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University China School of Computer Science and Technology East China Normal University China Chongqing Institute of East China Normal University China

3D semantic segmentation using an adapting model trained from a source domain with or without accessing unlabeled target-domain data is the fundamental task in computer vision, containing domain adaptation and domain generalization. The essence of simultaneously solving cross-domain tasks is to enhance the generalizability of the encoder. In light of this, we propose a groundbreaking universal method with the help of off-the-shelf Visual Foundation Models (VFMs) to boost the adaptability and generalizability of cross-domain 3D semantic segmentation, dubbed UniDSeg. Our method explores the VFMs prior and how to harness them, aiming to inherit the recognition ability of VFMs. Specifically, this method introduces layer-wise learnable blocks to the VFMs, which hinges on alternately learning two representations during training: (i) Learning visual prompt. The 3D-to-2D transitional prior and task-shared knowledge is captured from the prompt space, and then (ii) Learning deep query. Spatial Tunability is constructed to the representation of distinct instances driven by prompts in the query space. Integrating these representations into a cross-modal learning framework, UniDSeg efficiently mitigates the domain gap between 2D and 3D modalities, achieving unified cross-domain 3D semantic segmentation. Extensive experiments demonstrate the effectiveness of our method across widely recognized tasks and datasets, all achieving superior performance over state-of-the-art methods. Remarkably, UniDSeg achieves 57.5%/54.4% mIoU on "A2D2/sKITTI" for domain adaptive/generalized tasks. Code is available at https://***/Barcaaaa/UniDSeg. © 2024 Neural information processing systems foundation. All rights reserved.

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IMAGGS: a radiogenomic framework for identifying multi-way associations in breast cancer subtypes

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Journal of Genetics and Genomics 2024年第4期51卷 443-453页

作者： Shuyu Liang Sicheng Xu Shichong Zhou Cai Chang Zhiming Shao Yuanyuan Wang Sheng Chen Yunxia Huang Yi Guo Department of Electronic Engineering School of Information Science and TechnologyFudan UniversityShanghai 200433China The Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention(MICCAI)of Shanghai Shanghai 200032China Shanghai Key Laboratory of Metabolic Remodeling and Health Institute of Metabolism and Integrative BiologyFudan UniversityShanghai 200433China Department of Ultrasound Fudan University Shanghai Cancer CenterDepartment of OncologyShanghai Medical CollegeFudan UniversityShanghai 200032China Department of Breast Surgery Fudan University Shanghai Cancer CenterDepartment of OncologyShanghai Medical CollegeFudan UniversityShanghai 200032China

Investigating correlations between radiomic and genomic profiling in breast cancer(BC)molecular subtypes is crucial for understanding disease mechanisms and providing personalized *** present a well-designed radiogenomic framework image–gene–gene set(IMAGGS),which detects multi-way associations in BC subtypes by integrating radiomic and genomic *** dataset consists of 721 patients,each of whom has 12 ultrasound(US)images captured from different angles and gene mutation *** better characterize tumor traits,12 multi-angle US images are fused using two distinct ***,we analyze complex many-to-many associations between phenotypic and genotypic features using a machine learning algorithm,deviating from the prevalent one-to-one relationship pattern observed in previous *** radiomic and genomic features are screened using these *** addition,gene set enrichment analysis is performed to investigate the joint effects of gene sets and delve deeper into the biological functions of BC *** further validate the feasibility of IMAGGS in a glioblastoma multiforme dataset to demonstrate the scalability of IMAGGS across different modalities and *** together,IMAGGS provides a comprehensive characterization for diseases by associating imaging,genes,and gene sets,paving the way for biological interpretation of radiomics and development of targeted therapy.

关键词： IMAGGS Radiogenomic framework "lmage-gene-geneset"associations Molecular subtypes Breast cancer

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Hexagonal Convolutional Neural Network for Spatial Transcriptomics Classification

Hexagonal Convolutional Neural Network for Spatial Transcrip...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Gao, Jing Zhang, Fa Hu, Kai Cui, Xuefeng Xiangtan University Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan China Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Medical Technology Beijing Institute of Technology Beijing China Xiangnan University Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province Chenzhou China Shandong University School of Computer Science and Technology Qingdao China

ISBN: (纸本)9781665468190

Recent advances in spatial transcriptomics have enabled the comprehensive measurement of transcriptional profiles while retaining the spatial contextual information. Identifying spatial domains is a critical step in the analysis of spatially resolved transcriptomics. Existing unsupervised methods perform poorly on this task owing to the large amount of noise and dropout events in the transcriptomic profiles. To address this problem, we first extend an unsupervised algorithm to a supervised learning method that can identify useful features and reduce noise hindrance. Second, inspired by the classical convolution in convolutional neural networks (CNNs), we designed a regular hexagonal convolution to compensate for the missing gene expression patterns from adjacent nodes. Compared with the graph convolution in graph neural networks (GNNs), our hexagonal convolution can preserve the relative spatial location information of different nodes in graph-structured data. Third, based on the hexagonal convolution, a novel hexagonal Convolutional Neural Network (hexCNN) is proposed for spatial transcriptomics classification. Finally, we compared the proposed hexCNN with existing methods on the DLPFC dataset. The results show that hexCNN achieves a classification accuracy of 87.2% and an average Rand index (ARI) of 78.2% (1.9% and 3.3% higher than those of GNNs). © 2022 IEEE.

关键词： Convolution

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Safe Online Convex Optimization with Heavy-Tailed Observation Noises 39

Safe Online Convex Optimization with Heavy-Tailed Observatio...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Yang, Yunhao Xue, Bo Hao, Yunzhi Li, Ying Wan, Yuanyu School of Software Technology Zhejiang University Ningbo China Department of Computer Science City University of Hong Kong China Big Graph Center School of Computer and Computing Science Hangzhou City University Hangzhou China State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Bangsheng Technology Co. Ltd. Hangzhou China Institute of Blockchain and Data Security Hangzhou China

ISBN: (纸本)157735897X

We investigate safe online convex optimization (SOCO), where each decision must satisfy a set of unknown linear constraints. Assuming that the unknown constraints can be observed with a sub-Gaussian noise for each chosen decision, previous studies have established a high-probability regret bound of O(T2/3). However, this assumption may not hold in many practical scenarios. To address this limitation, in this paper, we relax the assumption to allow any noise that admits finite (1+ϵ)-th moments for some ϵ ∈ (0, 1], and propose two algorithms that enjoy an O(Tcϵ) regret bound with high probability, where T is the time horizon and cϵ = (1+ϵ)/(1+2ϵ). The key idea of our two algorithms is to respectively utilize the median-of-means and truncation techniques to achieve accurate estimation under heavy-tailed noises. To the best of our knowledge, these are the first algorithms designed to handle SOCO with heavy-tailed observation noises. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Convex optimization

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BuildEnVR: An Immersive Analysis System for Environmental Field

BuildEnVR: An Immersive Analysis System for Environmental Fi...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Zhenghan Zhou Kebin Liu Yantong Xie Hangxu Jin Shi Liu Ruiqing Wang Haitian Zhao Borong Lin Xiaofang Mu Hui Qi Global Innovation Exchange Tsinghua University Fuzhou Fuyao Institute for Advanced Study School of Architecture Carnegie Mellon University School of Architecture Tsinghua University College of Computer Science and Technology Taiyuan Normal University Shanxi Key Laboratory of Intelligent Optimization Computing and Blockchain Technology

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Amidst global warming and escalating extreme weather events, indoor environmental quality’s impact on human health and public hygiene gains prominence. Environmental parameters exist essentially as fields, which are characterized by high dimensionality, density and complexity, and contain massive amounts of information in space. To facilitate visualization and analysis of indoor environmental field, we design and implement BuildEnVR, an immersive analysis system by virtual reality, enabling remote analysis of real-time and historical environmental field data. Grounded in user needs and cognitive psychology, three visualization modes emerge: the Virtual Sensor mode enables users to access perceptual data in real-time at any 3D coordinates in ambient space, the 4D Heatmap mode visualizes spatial variations and trends over time in environmental field data, and the Synaesthesia mode realizes the fusion display of multi-dimensional environmental field data, allowing users to quickly understand the overall condition of the indoor environment with a low cognitive load. Extensive user surveys validate BuildEnVR’s intuitiveness and precision, and it is suitable for both experts and general users.

关键词： Wireless communication Wireless sensor networks Three-dimensional displays Buildings Psychology Data visualization Cognitive load Real-time systems Space heating Meteorology Climate change Global warming Environmental factors Immersive learning

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Secure Multi-keyword Retrieval with Integrity Guarantee for Outsourced ADS-B Data in Clouds 29

Secure Multi-Keyword Retrieval with Integrity Guarantee for ...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Yang, Shangru Ding, Yong Wang, Yujue Liang, Hai Yang, Changsong Wang, Huiyong Guilin University of Electronic Technology Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin China Hkct Institute for Higher Education Institute of Cyberspace Technology Hong Kong Hong Kong Hangzhou Innovation Institute of Beihang University Hangzhou China Guilin University of Electronic Technology School of Mathematics and Computing Science Guilin China

ISBN: (纸本)9798350330717

With the development of Automatic Dependent Surveillance-Broadcast (ADS-B) technology in the aviation industry, a large amount of data are generated in ADS-B systems everyday. Cloud storage can satisfy the requirement of storing a large amount of ADS-B data well, however, the data stored on cloud server also raises problems about the security and confidentiality. This paper proposes a secure ADS-B data outsourcing scheme with integrity guarantee and multi-keyword retrieval (MRDP) to address these issues. In our MRDP, an index is generated for each piece of ADS-B data through Bloom filters to enable multi-keyword retrieval from clouds. Also, a unique label is produced for each piece of ADS-B data for integrity verification of query results. Our MRDP solution enjoys completeness property in that all query results with regard to the multiple keywords would be returned by the cloud server, otherwise it could be detected by the user. Security analysis indicated that our scheme offers integrity, completeness and privacy protection on outsourced ADS-B data, under the computational Diffie-Hellman (CDH) assumption and the discrete logarithm (DL) assumption. Theoretical and experimental analyses demonstrate the practicality of our proposed MRDP construction compared to existing solutions. © 2023 IEEE.

关键词： ADS-B systems Cloud storage Integrity audit Multi-keyword search

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Integrating Degradation Learning into Image Restoration

Integrating Degradation Learning into Image Restoration

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Ma, Mingyu Ren, Dongwei Yang, Yajun College of Intelligence and Computing Tianjin University China School of Computer Science and Technology Harbin Institute of Technology China People's Daily Online State Key Laboratory of Communication Content Cognition China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Existing image restoration methods usually assume specific degradation model, e.g., linear combination of clean image and degradation map in image denoising and deraining. Benefiting from the power of deep learning, a restoration mapping can be learned from degraded image to latent clean image. In this paper, we propose to integrate degradation learning into image restoration (IDLIR), where degradation model can be learned from training samples. In particular, IDLIR is an iterative restoration framework, where latent clean image and degradation map can be extracted from current residual degraded image, and are then fused by a degradation network to reconstruct degraded image. Then the residual degradation image can be updated by computing the difference between input and reconstructed degraded images. By taking denoising and deraining as examples, IDLIR is compared with state-of-the-art methods on several benchmark datasets. IDLIR performs better than state-of-the-art methods quantitatively and qualitatively. © 2022 IEEE.

关键词： Image reconstruction

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Spatial-Temporal Constrained Aircraft Sequencing and Path Planning for Airport Ground Movement Problem 19th

Spatial-Temporal Constrained Aircraft Sequencing and Path P...

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19th CCF Conference on computer Supported Cooperative Work and Social computing, ChineseCSCW 2024

作者： Liu, Fengmei Sun, Jingzhe Zhu, Jie Zhao, Zheng Huang, Huimin School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China The State Key Laboratory Chinese of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100864 China Nanjing University of Aeronautics and Astronautics Nanjing210016 China

ISBN: (纸本)9789819623754

In order to effectively alleviate the congestion and delay of aircraft during peak hours in a large hub airport, the paper studies the cooperative scheduling and path planning for arrival and departure aircrafts. A cooperative aircraft scheduling and path planning model for the airport ground movement problem is established with multiple spatial and temporal constraints, such as the queue constraint, time difference constraint, corner constraint. It aims to minimize the average taxiing time for all aircrafts. A genetic algorithm with elite selection strategy (ESGA) is proposed for the problem under study. Experimental results show that the proposed algorithm can effectively obtain the aircraft sequencing and path planning solutions, and significantly reduce the average taxiing time of the aircraft. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Motion planning

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Video Summarization using Denoising Diffusion Probabilistic Model

arXiv

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arXiv 2024年

作者： Shang, Zirui Zhu, Yubo Li, Hongxi Yang, Shuo Wu, Xinxiao Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology China Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China

Video summarization aims to eliminate visual redundancy while retaining key parts of video to construct concise and comprehensive synopses. Most existing methods use discriminative models to predict the importance scores of video frames. However, these methods are susceptible to annotation inconsistency caused by the inherent subjectivity of different annotators when annotating the same video. In this paper, we introduce a generative framework for video summarization that learns how to generate summaries from a probability distribution perspective, effectively reducing the interference of subjective annotation noise. Specifically, we propose a novel diffusion summarization method based on the Denoising Diffusion Probabilistic Model (DDPM), which learns the probability distribution of training data through noise prediction, and generates summaries by iterative denoising. Our method is more resistant to subjective annotation noise, and is less prone to overfitting the training data than discriminative methods, with strong generalization ability. Moreover, to facilitate training DDPM with limited data, we employ an unsupervised video summarization model to implement the earlier denoising process. Extensive experiments on various datasets (TVSum, SumMe, and FPVSum) demonstrate the effectiveness of our method. Copyright © 2024, The Authors. All rights reserved.

关键词： Image denoising

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