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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yingze Xu Yan Wang Xuping Xie Feilong Wang Qiong Chen Huiyan Sun College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China College of Computer Science and Technology School of Artificial Intelligence Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China School of Artificial Intelligence Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

ISBN: (纸本)9781665429825

The deconvolution of infiltrating immune cells and stromal cells from complex tumor tissues is significant for studying the impact of these cells on tumor development, as well as assisting cancer therapies. Integrating cell-specific marker genes from several references, we put forward an AutoEncoder-based matrix factorization method to estimate the cell proportion in heterogeneous samples. The proportion predicted by our method achieved over 0.95 Pearson correlation coefficient (PCC) with the ground truth. Moreover, through analyzing association between cell proportion of tumor tissues and clinical information of the tumor patients, we found that the proportion of cancer-associated fibroblast (CAF) gradually increased with the progress of tumor, and the proportion of B cell in tissues was significantly related to the five-year survival rate of tumor patients.

关键词： Head Deconvolution Medical treatment Melanoma Fibroblasts Neck Reliability

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Event Prediction Model Combining Ordinary Differential Equation and Hypernetworks

SSRN

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SSRN 2023年

作者： Zhang, Junhe Ma, Fuyuan Wang, Xin Yang, Bo Wang, Ying College of Computer Science and Technology Jilin University Jilin Changchun130012 China College of Artificial Intelligence Jilin University Jilin Changchun130012 China Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Jilin Changchun130012 China

Modeling the temporal evolution patterns of network events is crucial for enhancing user experience and creating commercial value. Existing methods combine graph neural networks and time-series models to capture spatial and temporal features, but they assume non-interaction among nodes when the network structure is stable, implying that node states do not undergo updates. Moreover, utilizing a single model to fit all events in these methods may lead to an excessive emphasis on global evolutionary patterns, potentially neglecting the individual heterogeneity among events. In this paper, we propose an event prediction model combining Ordinary Differential Equations and Hypernetworks, named ODEH. Firstly, to model the continuous evolution process of network structures under stability, we utilize ordinary differential equations based on graph neural networks to capture the continuous information dissemination among nodes and the interaction between node representation channels. Then, we use a message passing mechanism to model the direct impact of network topology changes on network entities. Finally, a hypernetwork is used to capture the individual characteristics of events and fine-tune the prediction model. Experimental results on multiple datasets demonstrate ODEH's superiority over baseline methods, highlighting its effectiveness in event prediction. © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Fast Explainable Recommendation Model by Combining Fine-Grained Sentiment in Review Data

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computational Intelligence and Neuroscience 2022年第0期2022卷 4940401页

作者： Wang, Ying He, Xin Wang, Hongji Sun, Yudong Wang, Xin College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun130012 China College of Artificial Intelligence Jilin University Changchun130012 China

With the rapid development of e-commerce, recommendation system has become one of the main tools that assists users in decision-making, enhances user's experience, and creates economic value. Since it is difficult to explain the implicit features generated by matrix factorization, explainable recommendation system has attracted more and more attention recently. In this paper, we propose an explainable fast recommendation model by combining fine-grained sentiment in review data (FSER, (Fast) Fine-grained Sentiment for Explainable Recommendation). We innovatively construct user-rating matrix, user-aspect sentiment matrix, and item aspect-descriptive word frequency matrix from the review-based data. And the three matrices are reconstructed by matrix factorization method. The reconstructed results of user-aspect sentiment matrix and item aspect-descriptive word frequency matrix can provide explanation for the final recommendation results. Experiments in the Yelp and Public Comment datasets demonstrate that, compared with several classical models, the proposed FSER model is in the optimal recommendation accuracy range and has lower sparseness and higher training efficiency than tensor models or neural network models;furthermore, it can generate explanatory texts and diagrams that have high interpretation quality. © 2022 Ying Wang et al.

关键词： Matrix algebra

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Acupuncture and Tuina Knowledge Graph for Ancient Literature of Traditional Chinese Medicine

Acupuncture and Tuina Knowledge Graph for Ancient Literature...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Han, Xiaosong Li, Xiaoran Liang, Yanchun Wang, Xinghao Xu, Dong Guan, Renchu Jilin University Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Changchun China University of Missouri Christopher S. Bond Life Sciences Center Department of Electrical Engineering and Computer Science Columbia United States

ISBN: (纸本)9781665401265

The Traditional Chinese Medicine's ancient literature recorded the massive medical theories and abundant medical experiences. To better understand and utilize, the knowledge from the literature, the Acupuncture and Tuina Knowledge Graph is proposed in this paper. Meanwhile, a deep learning network is established for acupuncture and tuina-related entity recognition and entity-relationship extraction. Finally, the trained network is able to reach an 82%+ F1-score for NER and 70%+ F1-score for relationship extraction. © 2021 IEEE.

关键词： Acupuncture

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Multimodal Hypothetical Summary for Retrieval-based Multi-image Question Answering 39

Multimodal Hypothetical Summary for Retrieval-based Multi-im...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Li, Peize Si, Qingyi Fu, Peng Lin, Zheng Wang, Yan School of Artificial Intelligence Jilin University Changchun China Institute of Information Engineering Chinese Academy of Sciences Beijing China Huawei Technologies Co. Ltd. Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun China

ISBN: (纸本)157735897X

Retrieval-based multi-image question answering (QA) task involves retrieving multiple question-related images and synthesizing these images to generate an answer. Conventional "retrieve-then-answer" pipelines often suffer from cascading errors because the training objective of QA fails to optimize the retrieval stage. To address this issue, we propose a novel method to effectively introduce and reference retrieved information into the QA. Given the image set to be retrieved, we employ a multimodal large language model (visual perspective) and a large language model (textual perspective) to obtain multimodal hypothetical summary in question-form and description-form. By combining visual and textual perspectives, MHyS captures image content more specifically and replaces real images in retrieval, which eliminates the modality gap by transforming into text-to-text retrieval and helps improve retrieval. To more advantageously introduce retrieval with QA, we employ contrastive learning to align queries (questions) with MHyS. Moreover, we propose a coarse-to-fine strategy for calculating both sentence-level and word-level similarity scores, to further enhance retrieval and filter out irrelevant details. Our approach achieves a 3.7% absolute improvement over state-of-the-art methods on RETVQA and a 14.5% improvement over CLIP. Comprehensive experiments and detailed ablation studies demonstrate the superiority of our method. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Contrastive Learning

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A Survey on Fairness in Large Language Models

arXiv

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arXiv 2023年

作者： Li, Yingji Du, Mengnan Song, Rui Wang, Xin Wang, Ying College of Computer Science and Technology Jilin University Changchun130012 China Department of Data Science New Jersey Institute of Technology Newark United States School of Artificial Intelligence Jilin University Changchun130012 China Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun130012 China

Large Language Models (LLMs) have shown powerful performance and development prospects and are widely deployed in the real world. However, LLMs can capture social biases from unprocessed training data and propagate the biases to downstream tasks. Unfair LLM systems have undesirable social impacts and potential harms. In this paper, we provide a comprehensive review of related research on fairness in LLMs. Considering the influence of parameter magnitude and training paradigm on research strategy, we divide existing fairness research into oriented to medium-sized LLMs under pre-training and fine-tuning paradigms and oriented to large-sized LLMs under prompting paradigms. First, for medium-sized LLMs, we introduce evaluation metrics and debiasing methods from the perspectives of intrinsic bias and extrinsic bias, respectively. Then, for large-sized LLMs, we introduce recent fairness research, including fairness evaluation, reasons for bias, and debiasing methods. Finally, we discuss and provide insight on the challenges and future directions for the development of fairness in LLMs. Copyright © 2023, The Authors. All rights reserved.

关键词： computational linguistics

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Object Attribute Matters in Visual Question Answering

arXiv

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arXiv 2023年

作者： Li, Peize Si, Qingyi Fu, Peng Lin, Zheng Wang, Yan School of Artificial Intelligence Jilin University Changchun China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun China

Visual question answering is a multimodal task that requires the joint comprehension of visual and textual information. However, integrating visual and textual semantics solely through attention layers is insufficient to comprehensively understand and align information from both modalities. Intuitively, object attributes can naturally serve as a bridge to unify them, which has been overlooked in previous research. In this paper, we propose a novel VQA approach from the perspective of utilizing object attribute, aiming to achieve better object-level visual-language alignment and multimodal scene understanding. Specifically, we design an attribute fusion module and a contrastive knowledge distillation module. The attribute fusion module constructs a multimodal graph neural network to fuse attributes and visual features through message passing. The enhanced object-level visual features contribute to solving fine-grained problem like counting-question. The better object-level visual-language alignment aids in understanding multimodal scenes, thereby improving the model’s robustness. Furthermore, to augment scene understanding and the out-of-distribution performance, the contrastive knowledge distillation module introduces a series of implicit knowledge. We distill knowledge into attributes through contrastive loss, which further strengthens the representation learning of attribute features and facilitates visual-linguistic alignment. Intensive experiments on six datasets, COCO-QA, VQAv2, VQA-CPv2, VQA-CPv1, VQAvs and TDIUC, show the superiority of the proposed method. © 2023, CC BY.

关键词： Distillation

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Multimodal Hypothetical Summary for Retrieval-based Multi-image Question Answering

arXiv

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arXiv 2024年

作者： Li, Peize Si, Qingyi Fu, Peng Lin, Zheng Wang, Yan School of Artificial Intelligence Jilin University Changchun China Institute of Information Engineering Chinese Academy of Sciences Beijing China Huawei Technologies Co. Ltd. Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education College of Computer Science and Technology Jilin University Changchun China

Retrieval-based multi-image question answering (QA) task involves retrieving multiple question-related images and synthesizing these images to generate an answer. Conventional "retrieve-then-answer" pipelines often suffer from cascading errors because the training objective of QA fails to optimize the retrieval stage. To address this issue, we propose a novel method to effectively introduce and reference retrieved information into the QA. Given the image set to be retrieved, we employ a multimodal large language model (visual perspective) and a large language model (textual perspective) to obtain multimodal hypothetical summary in question-form and description-form. By combining visual and textual perspectives, MHyS captures image content more specifically and replaces real images in retrieval, which eliminates the modality gap by transforming into text-to-text retrieval and helps improve retrieval. To more advantageously introduce retrieval with QA, we employ contrastive learning to align queries (questions) with MHyS. Moreover, we propose a coarse-to-fine strategy for calculating both sentence-level and word-level similarity scores, to further enhance retrieval and filter out irrelevant details. Our approach achieves a 3.7% absolute improvement over state-of-the-art methods on RETVQA and a 14.5% improvement over CLIP. Comprehensive experiments and detailed ablation studies demonstrate the superiority of our method. Copyright © 2024, The Authors. All rights reserved.

关键词： Question answering

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Acupuncture and Tuina Knowledge Graph for Ancient Literature of Traditional Chinese Medicine

Acupuncture and Tuina Knowledge Graph for Ancient Literature...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiaosong Han Xiaoran Li Yanchun Liang Xinghao Wang Dong Xu Renchu Guan Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Jilin University Changchun China Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Software Jilin University Changchun China Christopher S. Bond Life Sciences Center University of Missouri Columbia USA

ISBN: (纸本)9781665429825

The Traditional Chinese Medicine’s ancient literature recorded the massive medical theories and abundant medical experiences. To better understand and utilize, the knowledge from the literature, the Acupuncture and Tuina Knowledge Graph is proposed in this paper. Meanwhile, a deep learning network is established for acupuncture and tuina-related entity recognition and entity-relationship extraction. Finally, the trained network is able to reach an 82%+ F1-score for NER and 70%+ F1-score for relationship extraction.

关键词： Deep learning Conferences Labeling Bioinformatics

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Revisiting the efficacy of weak consistencies: a study of forward checking

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Science China(Information Sciences) 2021年第7期64卷 233-235页

作者： Zhe LI Zhezhou YU Hongbo LI Jinsong GUO Zhanshan LI Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Jilin University College of Information Science and Technology Northeast Normal University Department of Computer Science University of Oxford

Dear editor,Arc consistency (AC)[1] is currently the most popular local consistency for solving constraint satisfaction problems(CSP). To solve CSPs more efficiently, extensive work [2–4]has been done to find local c... 详细信息

关键词： Information Systems and Communication Service

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