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Algorithm and axiomatization of rough fuzzy sets based finite dimensional fuzzy vectors

中国高等学校学术文摘·计算机科学 2009年第4期3卷 560-568页

作者： Mingfen WU School of Information Science Wuyi UniversityJiangmen 529020China Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190Ch

Rough sets,proposed by Pawlak and rough fuzzy sets proposed by Dubois and Prade were expressed with the different computing formulas that were more complex and not conducive to computer operations,In this paper,we use the composition of a fuzzy matrix and fuzzy vectors in a given non-empty finite universal,constitute an algebraic system composed of finite dimensional fuzzy vectors and discuss some properties of the algebraic system about a basis and *** give an effective calculation representation of rough fuzzy sets by the inner and outer products that unify computing of rough sets and rough fuzzy sets with a *** basis of the algebraic system play a key role in this *** give some essential properties of the lower and upper approximation operators generated by reflexive,symmetric,and transitive fuzzy *** reflexive,symmetric,and transitive fuzzy relations are charac terized by the basis of the algebraic system.A set of axioms,as the axiomatic approach,has been constructed to characterize the upper approximation of fuzzy sets on the basis of the algebraic system.

关键词： fuzzy vector fuzzy matrix fuzzy relation operation basis lower and upper approximation rough fuzzy sets unary operator

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Relation Reconstructive Binarization of word embeddings

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Frontiers of Computer Science 2022年第2期16卷 47-54页

作者： Feiyang PAN Shuokai LI Xiang AO Qing HE Key Lab of Intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing TechnologyCASBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China

Word-embedding acts as one of the backbones of modern natural language processing(NLP).Recently,with the need for deploying NLP models to low-resource devices,there has been a surge of interest to compress word embeddings into hash codes or binary vectors so as to save the storage and memory ***,existing work learns to encode an embedding into a compressed representation from which the original embedding can be *** these methods aim to preserve most information of every individual word,they often fail to retain the relation between words,thus can yield large loss on certain *** this end,this paper presents Relation Reconstructive Binarization(R2B)to transform word embeddings into binary codes that can preserve the relation between *** its heart,R2B trains an auto-encoder to generate binary codes that allow reconstructing the wordby-word relations in the original embedding *** showed that our method achieved significant improvements over previous methods on a number of tasks along with a space-saving of up to 98.4%.Specifically,our method reached even better results on word similarity evaluation than the uncompressed pre-trained embeddings,and was significantly better than previous compression methods that do not consider word relations.

关键词： embedding compression variational auto-encoder binary word embedding

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Nested relation extraction with iterative neural network

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Frontiers of Computer Science 2021年第3期15卷 109-122页

作者： Yixuan CAO Dian CHEN Zhengqi XU Hongwei LI Ping LUO Key Lab of Intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing TechnologyCASBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China

Most existing researches on relation extraction focus on binary flat relations like Bomln relation between a Person and a *** a large portion of objective facts de-scribed in natural language are complex,especially in professional documents in fields such as finance and biomedicine that require precise *** example,“the GDP of the United States in 2018 grew 2.9%compared with 2017”describes a growth rate relation between two other relations about the economic index,which is beyond the expressive power of binary flat ***,we propose the nested relation extraction problem and formulate it as a directed acyclic graph(DAG)structure extraction ***,we propose a solution using the Iterative Neural Network which extracts relations layer by *** proposed solution achieves 78.98 and 97.89 FI scores on two nested relation extraction tasks,namely semantic cause-and-efFect relation extraction and formula ***,we observe that nested relations are usually expressed in long sentences where entities are mentioned repetitively,which makes the annotation difficult and ***,we extend our model to incorporate a mention-insensitive mode that only requires annotations of relations on entity concepts(instead of exact mentions)while preserving most of its *** mention-insensitive model performs better than the mention sensitive model when the random level in mention selection is higher than 0.3.

关键词： nested relation extraction mention insensitive relation iterative neural network

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DualGOFiller: A Dual-Channel Graph Neural Network with Contrastive Learning for Enhancing Function Prediction in Partially Annotated Proteins 29th

DualGOFiller: A Dual-Channel Graph Neural Network with Cont...

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29th International Conference on Research in Computational Molecular Biology, RECOMB 2025

作者： Wang, Shaojun Liu, Hancheng Zhai, Weiqi Zhu, Shanfeng Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai200433 China MOE Frontiers Center for Brain Science Fudan University Shanghai200433 China Ministry of Education Shanghai200433 China Shanghai Key Lab of Intelligent Information Processing and Shanghai Institute of Artificial Intelligence Algorithm Fudan University Shanghai200433 China Zhangjiang Fudan International Innovation Center Shanghai200433 China

ISBN: (纸本)9783031902512

Protein function prediction remains a critical and challenging task in the field of bioinformatics. Despite various existing computational methods, most focus on predicting functions for novel proteins, often overlooking those with incomplete annotations. Predicting missing Gene Ontology (GO) terms for these partially annotated proteins is also important for gaining a comprehensive understanding of their biological roles. To address this gap, we introduce DualGOFiller, a dual-channel graph neural network (GNN) model, which is a novel method specifically focused on predicting missing GO terms for partially annotated proteins. DualGOFiller integrates heterogeneous (protein-GO bipartite graph) and homogeneous (protein-protein interaction network and GO directed acyclic graph) network information, enriched with attribute data from protein sequences and GO term descriptions. It further employs graph contrastive learning (GCL) to strengthen the alignment across different views. Experiments demonstrate the superiority of DualGOFiller over state-of-the-art methods, including traditional recommendation system approaches and several protein function prediction methods. The ablation study further confirms the significant contributions of key component in DualGOFiller for boosting predictive performance. The source code is available at https://***/Zhulab-Fudan/DualGOFiller. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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A framework for Chinese event semantic constraint predicates and their generation using PEFT LLM and RAG

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International Journal of Machine Learning and Cybernetics 2025年

作者： Huang, Qiaojuan Wang, Shi He, Qing Cao, Cungen Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

Event semantic analysis is a crucial area of research in natural language processing. It focuses on deeply understanding the semantics of events and their components. While existing semantic frameworks and event semantic labelling cover a wealth of semantic information, further research is needed to explore the fine-grained semantic relationships across participants in different events. To address this problem, we construct a head–tail event semantic constraint predicate framework that relates the complex semantics between different event components. However, considering the large-scale data, an automated method for generating constraint predicates is essential. Therefore, we combine parameter-efficient fine-tuning (PEFT) of large language models (LLMs) with retrieval-augmented generation (RAG) technologies. Specifically, this paper makes four significant contributions. Firstly, we construct a detailed framework of head–tail events semantic constraint predicates by combining manual definitions with the GPT-4o model. The framework includes 117 types of constraint predicates between head–tail events. Secondly, based on this framework, we annotate a dataset of head–tail events semantic constraint predicates and apply it to PEFT LLMs. Thirdly, we use PEFT LLMs prediction error data to construct an event knowledge base covering event entities, semantic roles, and entity types. Fourth, using the event knowledge base, the PEFT LLMs integrate the dual RAG techniques of keywords and vectors to improve the accuracy of generating the constraint predicates. Experimental results show that the method outperforms the baselines, achieving a precision of 97.18% and recall of 97.39%. This research fills the gap in semantic relationships between components across events and provides a novel approach for complex semantic generation tasks. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Domain Knowledge

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DIVE: Inverting Conditional Diffusion Models for Discriminative Tasks

arXiv

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arXiv 2025年

作者： Li, Yinqi Chang, Hong Hou, Ruibing Shan, Shiguang Chen, Xilin Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

Diffusion models have shown remarkable progress in various generative tasks such as image and video generation. This paper studies the problem of leveraging pretrained diffusion models for performing discriminative tasks. Specifically, we extend the discriminative capability of pretrained frozen generative diffusion models from the classification task [1], [2] to the more complex object detection task, by "inverting" a pretrained layout-to-image diffusion model. To this end, a gradient-based discrete optimization approach for replacing the heavy prediction enumeration process, and a prior distribution model for making more accurate use of the Bayes’ rule, are proposed respectively. Empirical results show that this method is on par with basic discriminative object detection baselines on COCO dataset. In addition, our method can greatly speed up the previous diffusion-based method [1], [2] for classification without sacrificing accuracy. Code and models are available at https://***/LiYinqi/DIVE. © 2025, CC BY-NC-ND.

关键词： Object detection

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Staggered Grid Scheme for the FFT-Based Methods

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Chinese Journal of Electronics 2019年第5期28卷 1066-1072页

作者： XIE Jiaye KONG Weibin PANG Lili SONG Weiju HUANG Zhixiang WU Xianliang Industrial Center Nanjing Institute of Technology State Key Laboratory of Millimeter Waves College of Information Engineering Yancheng Institute of Technology Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University

A staggered grid scheme is proposed to reduce both the total memory requirement and the CPU time of generating the corrected near matrix in the FFTbased methods. Two sets of Cartesian grids are used to project the source points and the field points, *** proposed method does not lower the efficiency of computing far matrix-vector products, compared with the traditional uniform Cartesian grid scheme. Some numerical experiments are provided to demonstrate both the correctness and the efficiency of the proposed method.

关键词： Green’s function Singularity RFG-FFT Staggered grid scheme Corrected near matrix

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The sound and complete R-calculus for revising propositional theories

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Science China(information Sciences) 2015年第9期58卷 5-16页

作者： LI Wei SUI YueFei SUN MeiYing State Key Laboratory of Software Development Environment Beihang University Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of Sciences University of Chinese Academy of Sciences

The AGM postulates are for the belief revision(revision by a single belief), and the DP postulates for the iterated revision(revision by a finite sequence of beliefs). Li gave an R-calculus for R-configurations?|Γ,where ? is a set of atomic formulas or the negations of atomic formulas, and Γ is a finite set of formulas. In this paper, two deduction systems for the revision of a theory by another theory are given such that the systems are sound and complete, that is, if ?|Γ■Γ′ is provable then Γ′■ ? is consistent and Γ′- ? is a maximal subset of Γ such that(Γ′- ?) ∪ ? is consistent; and for any finite theories ? and Γ, there is a finite theoryΓ′ such that Γ′- ? is a maximal subset of Γ such that(Γ′- ?) ∪ ? is consistent, and ?|Γ ? Γ′ is ***, if?|Γ■Γ′ is provable then Γ′ satisfies the AGM and the DP postulates.

关键词： belief revision R-calculus minimal change the AGM postulates the DP postulates

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Fast assignment reduction in inconsistent incomplete decision systems

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Journal of Systems Engineering and Electronics 2014年第1期25卷 83-94页

作者： Min Li Shaobo Deng Shengzhong Feng Jianping Fan Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Nanchang Institute of Technology Graduate School of Chinese Academy of Sciences Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of Sciences

This paper focuses on fast algorithm for computing the assignment reduct in inconsistent incomplete decision systems. It is quite inconvenient to judge the assignment reduct directly ac-cording to its definition. We propose the judgment theorem for the assignment reduct in the inconsistent incomplete decision system, which greatly simplifies judging this type reduct. On such basis, we derive a novel attribute significance measure and construct the fast assignment reduction algorithm （F-ARA）, intended for com-puting the assignment reduct in inconsistent incomplete decision systems. Final y, we make a comparison between F-ARA and the discernibility matrix-based method by experiments on 13 Univer-sity of California at Irvine （UCI） datasets, and the experimental results prove that F-ARA is efficient and feasible.

关键词： assignment reduction upper approximation reduction,inconsistent incomplete decision system rough set.

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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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