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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Li, Yichong Liu, Xiaojian Cheng, Fan Pan, Xiaoyong Yang, Yang Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai200240 China

ISBN: (纸本)9798350386226

RNA-binding proteins (RBPs) are essential for gene expression, and the complex RNA-protein interaction mechanisms require analysis of global RNA information. Therefore, accurate prediction of RBP binding sites on full-length RNA transcripts is crucial for understanding these mechanisms and their roles in diseases. While machine learning methods can predict RBP binding to RNA fragments, extending this to full-length transcripts presents challenges due to sequence length and data imbalance. In this paper, we introduce RBP-Former, a binding site joint prediction model designed specifically for full-length RNA transcripts that can be used for multiple RBPs. This model processes information at both coarse and fine-grained levels to fully exploit sequence data and its interactions with multiple RBPs. We develop multi-level imbalance learning strategies, achieving favorable results on imbalanced data. Our method outperforms existing methods in predicting binding sites on full-length RNA transcripts for multiple RBPs, demonstrating its effectiveness in handling imbalanced label and sample distributions. © 2024 IEEE.

关键词： Binding sites

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Self-Prompting Large Language Models for Zero-Shot Open-Domain QA

Self-Prompting Large Language Models for Zero-Shot Open-Doma...

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2024 Conference of the North American Chapter of the Association for Computational Linguistics: human Language Technologies, NAACL 2024

作者： Li, Junlong Wang, Jinyuan Zhang, Zhuosheng Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China SJTU-Paris Elite Institute of Technology Shanghai Jiao Tong University China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China

ISBN: (纸本)9798891761148

Open-Domain Question Answering (ODQA) aims to answer questions without explicitly providing specific background documents. This task becomes notably challenging in a zero-shot setting where no data is available to train tailored retrieval-reader models. While recent Large Language Models (LLMs) like GPT-3 have demonstrated their effectiveness in zero-shot ODQA using direct prompting methods, these methods still fall short of fully harnessing the potential of LLMs when implicitly invoked. In this paper, we propose a Self-Prompting framework to explicitly utilize the massive knowledge encoded in the parameters of LLMs and their strong instruction understanding abilities. Concretely, we prompt LLMs step by step to generate multiple pseudo QA pairs with background passages and explanations entirely from scratch. These generated elements are then utilized for in-context learning. Experimental results show that our method significantly surpasses previous state-of-the-art zero-shot methods on three widely-used ODQA datasets and even achieves comparable performance with various customized fine-tuned models on full training data. ©2024 Association for Computational Linguistics.

关键词： Zero-shot learning

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VAction: A Lightweight and Integrated VR Training System for Authentic Film-Shooting Experience 25

VAction: A Lightweight and Integrated VR Training System for...

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Proceedings of the 2025 CHI Conference on human Factors in Computing Systems

作者： Shaocong Wang Che Qu Minjing Yu Chao Zhou Yuntao Wang Yu-Hui Wen Yuanchun Shi Yong-Jin Liu Department of Computer Science and Technology Tsinghua University Beijing China Film & TV Technology Department Beijing Film Academy Beijing Beijing China College of Intelligence and Computing Tianjin University Tianjin China Human Computer Interaction Technology and Intelligent Information Processing Laboratory Institute of Software Chinese Acadamy of Sciences Beijing China School of Computer Science and Technology Beijing Jiaotong University Beijing China Department of Computer science and Technology Tsinghua University Beijing China Computer Science and Technology Tsinghua University Beijing Beijing China

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A Lightweight Method to Survey with Protecting Privacy yet Maintaining Accuracy 18th

A Lightweight Method to Survey with Protecting Privacy yet...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Wang, Yuxuan Di, Xinyu Liu, Ningbo Zhang, Xianchao Ren, Wei School of Computer Science China University of Geosciences Wuhan China Department of Computer Science University of Toronto TorontoONM5S 1A1 Canada International College Chongqing University of Technology Chongqing China Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province Jiaxing University Jiaxing China Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing University Jiaxing China Ministry of Education Chengdu China State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR CASM Beijing China

ISBN: (纸本)9783031714665

In a survey, a lot of questions and answers are listed and participators will select one of the answers for each question. Sometimes, participators may not be willing to select the preferred one item in the answers because the item may damage the privacy of themselves. If this question is not answered, then it reveals that the question or the answers are sensitive, which also damages the privacy. Besides, each participator may have distinct privacy concerns even for the same questions and answers, thus the privacy-aware questions and answers cannot be determined and be removed in advance. Thus, the dilemma is how to select the answers for privacy-aware questions yet maintain the utility of the answering results in the survey. In this paper, we propose a general method to protect the various privacy in the investigation without the knowledge of privacy to be protected in advance. The main idea is that participators can randomly select answers if the privacy is the concern for themselves in the answers for this question. Our method is lightweight so as to be convenient in practices. We extensively analyze the accuracy of survey results to justify that our method can protect the privacy yet maintain the utility of the investigation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Differential privacy

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Layered interactions: Exploring Non-Intrusive Digital Craftsmanship Design Through Lacquer Art Interfaces 25

Layered Interactions: Exploring Non-Intrusive Digital Crafts...

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Proceedings of the 2025 CHI Conference on human Factors in Computing Systems

作者： Yan Dong Hanjie Yu Yanran Chen Zipeng Zhang Wu Qiong Department of Information Art & Design Academy of Arts & Design Tsinghua University Beijing China Department of Information Art & Design Academy of Arts & Design Tsinghua University beijing China Pervasive Human Computer Interaction Laboratory Tsinghua University Beijing China

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On the Robustness of Editing Large Language Models

On the Robustness of Editing Large Language Models

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Ma, Xinbei Ju, Tianjie Qiu, Jiyang Zhang, Zhuosheng Zhao, Hai Liu, Lifeng Wang, Yulong School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China Baichuan Intelligent Technology China

ISBN: (纸本)9798891761643

Large language models (LLMs) have played a pivotal role in building communicative AI, yet they encounter the challenge of efficient updates. Model editing enables the manipulation of specific knowledge memories and the behavior of language generation without retraining. However, the robustness of model editing remains an open question. This work seeks to understand the strengths and limitations of editing methods, facilitating practical applications of communicative AI. We focus on three key research questions. RQ1: Can edited LLMs behave consistently resembling communicative AI in realistic situations? RQ2: To what extent does the rephrasing of prompts lead LLMs to deviate from the edited knowledge memory? RQ3: Which knowledge features are correlated with the performance and robustness of editing? Our empirical studies uncover a substantial disparity between existing editing methods and the practical application of LLMs. On rephrased prompts that are flexible but common in realistic applications, the performance of editing experiences a significant decline. Further analysis shows that more popular knowledge is memorized better, easier to recall, and more challenging to edit effectively. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Virtual Reality Pōwhiri—Practicing an indigenous welcoming ceremony 23

Virtual Reality Pōwhiri—Practicing an indigenous welcoming...

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Proceedings of the 35th Australian computer-human interaction Conference

作者： Laurence Lloyd-Jones Claudia Ott Holger Regenbrecht Hēmi Whaanga Human Computer Interaction (HCI) Lab University of Otago New Zealand Information Science University of Otago New Zealand Department of Information Science University of Otago New Zealand Te P?tahi-a-Toi Massey University New Zealand

ISBN: (纸本)9798400717079

Pōwhiri is a traditional welcoming ceremony in Māori culture in Aotearoa New Zealand. Certain protocols (tikanga) have to be followed and must be learned. There is a general lack of understanding regarding this due to the scarcity of pōwhiri. The immersive and experiential nature of Virtual Reality (VR) can be used as a tool to increase understanding and confidence. We have implemented a VR learning tool in the reconstructed context of Te Rau Aroha marae in Bluff which allows for safe practice of pōwhiri before applying it in a real welcoming ceremony. A cultural evaluation study was conducted first followed by a user study to determine understanding and confidence gains, highlighting the complexity of the topic. The user study clearly showed that the system was useful to increase participants’ understanding in regards to pōwhiri as well as their confidence surrounding pōwhiri. We are confident that our experiences and key findings from the studies can be used to drive further development of VR tools in the context of visualising cultural ceremonies.

关键词： 3D Reconstruction Indigenous User Interfaces VR Learning Volumetric Video

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From Missing Pieces to Masterpieces: Image Completion with Context-Adaptive Diffusion

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年 PP卷 PP页

作者： Shamsolmoali, Pourya Zareapoor, Masoumeh Zhou, Huiyu Felsberg, Michael Tao, Dacheng Li, Xuelong University of York Department of Computer Science United Kingdom University of Leicester School of Computing and Mathematical Sciences United Kingdom Linkoping University Computer Vision Laboratory Sweden Nanyang Technological University College of Computing & Data Science Singapore China Northwestern Polytechnical University Key Laboratory of Intelligent Interaction and Applications Ministry of Industry and Information Technology Xi'an China

Image completion is a challenging task, particularly when ensuring that generated content seamlessly integrates with existing parts of an image. While recent diffusion models have shown promise, they often struggle with maintaining coherence between known and unknown (missing) regions. This issue arises from the lack of explicit spatial and semantic alignment during the diffusion process, resulting in content that does not smoothly integrate with the original image. Additionally, diffusion models typically rely on global learned distributions rather than localized features, leading to inconsistencies between the generated and existing image parts. In this work, we propose ConFill, a novel framework that introduces a Context-Adaptive Discrepancy (CAD) model to ensure that intermediate distributions of known and unknown regions are closely aligned throughout the diffusion process. By incorporating CAD, our model progressively reduces discrepancies between generated and original images at each diffusion step, leading to contextually aligned completion. Moreover, ConFill uses a new Dynamic Sampling mechanism that adaptively increases the sampling rate in regions with high reconstruction complexity. This approach enables precise adjustments, enhancing detail and integration in restored areas. Extensive experiments demonstrate that ConFill outperforms current methods, setting a new benchmark in image completion. © 1979-2012 IEEE.

关键词： computer aided design

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Learning a Subspace and Clustering Simultaneously with Manifold Regularized Nonnegative Matrix Factorization

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Guidance, Navigation and Control 2024年第3期4卷 147-165页

作者： Feiping Nie Huimin Chen Heng Huang Chris H.Q.Ding Xuelong Li School of Computer Science Northwestern Polytechnical University Xi'an 710072 P.R.China School of Artificial Intelligence Optics and Electronics(iOPEN)Northwestern Polytechnical University Xi'an 710072 P.R.China Key Laboratory of Intelligent Interaction and Applications(Northwestern Polytechnical University)Ministry of Industry and Information Technology Xi'an 710072 P.R.China Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA 15260USA Department of Computer Science and Engineering The Chinese University of Hong Kong Shenzhen 518172 P.R.China Institute of Artificial Intelligence(TeleAI)China Telecom Corporation Limited 31 Jinrong Street Beijing 100033P.R.China

With the incredible growth of high-dimensional data such as microarray gene expression data and web blogs from internet, the researchers are desirable to develop new clustering techniques to address the critical problem created by irrelevant dimensions. Properties of Nonnegative Matrix Factorization(NMF) as a clustering method were studied by relating its formulation to other methods such as K-means clustering. In this paper, by introducing clustering indicator constraints on NMF and incorporating manifold regularization to preserve geometric structures,we propose a novel manifold regularized NMF method that can simultaneously learn subspace and do clustering. As a result, our clustering results can directly assign cluster label to data points. Extensive experimental results show that our method outperforms related other methods.

关键词： Subspace learning clustering nonnegative matrix factorization manifold learning

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Isoform Function Prediction Based on Heterogeneous Graph Attention Networks

Isoform Function Prediction Based on Heterogeneous Graph Att...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Guo, Kuo Li, Yifan Chen, Hao Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Institute of Image Processing and Pattern Recognition Shanghai200240 China Carnegie Mellon University School of Computer Science Computational Biology Department PittsburghPA15213 United States

ISBN: (纸本)9798350337488

Isoforms refer to different mRNA molecules transcribed from the same gene, which can be translated into proteins with varying structures and functions. Predicting the functions of isoforms is an essential topic in bioinformatics as it can provide valuable insights into the intricate mechanisms of gene regulation and biological processes. Conventionally, gene function labels are standardized in Gene Ontology (GO) terms. However, traditional methods for predicting isoform function are largely limited by the absence of isoform-specific labels, sparse annotations, and the vast number of GO terms. To address these issues, we propose HANIso, a deep learning-based method for isoform function prediction. HANIso leverages a pretrained protein language model to extract features from protein sequences. It also integrates heterogeneous information, such as isoform sequence features, GO annotations, and isoform interaction data, using a Heterogeneous Graph Attention Network (HAN). This allows the model to learn the importance of different sources of information and their semantic relationships through the attention mechanism. Our method can predict function labels at both the gene level and isoform level. We conduct experiments on two species datasets, and the results demonstrate that our method outperforms existing methods on both AUROC and AUPRC. HANIso has the potential to overcome the limitations of traditional methods and provide a more accurate and comprehensive understanding of isoform function. © 2023 IEEE.

关键词： alternative splicing gene ontology heterogeneous graph attention network isoform function prediction protein language model

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