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23rd China National Conference on Computational Linguistics, CCL 2024

作者： Li, Lishuang Mi, Liteng Zhang, Beibei Xiang, Yi Feng, Yubo Qin, Xueyang Tang, Jingyao School of Computer Science and Technology Dalian University of Technology Liaoning Dalian China School of Computer Science and Technology Shandong Institute of Petroleum and Chemical Technology Shandong Dongying China

ISBN: (纸本)9789819783663

Biomedical Event Causal Relation Extraction (BECRE) is an important task in biomedical information extraction. Existing methods usually use pre-trained language models to learn semantic representations and then predict the event causal relation. However, these methods struggle to capture sufficient cues in biomedical texts for predicting causal relations. In this paper, we propose a Path Reasoning-based Relation-aware Network (PRRN) to explore deeper cues for causal relations using reinforcement learning. Specifically, our model reasons the relation paths between entity arguments of two events, namely entity relation path, which connects the two biomedical events through the multi-hop interactions between entities to provide richer cues for predicting event causal relations. In PRRN, we design a path reasoning module based on reinforcement learning and propose a novel reward function to encourage the model to focus on the length and contextual relevance of entity relation paths. The experimental results on two datasets suggest that PRRN brings considerable improvements over the state-of-the-art models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Reinforcement learning

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Unified entropy entanglement with tighter constraints on multipartite systems

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Chinese Physics B 2023年第3期32卷 96-101页

作者：孙琪李陶靳志祥梁登峰 School of Mathematics and Statistics Beijing Technology and Business UniversityBeijing 100048China School of Computer Science and Techonology Dongguan University of TechnologyDongguan 523808China

Monogamy and polygamy relations characterize the distributions of entanglement in multipartite *** provide a characterization of multiqubit entanglement constraints in terms of unified-(q,s)entropy.A class of tighter monogamy inequalities of multiqubit entanglement based on theα-th power of unified-(q,s)entanglement forα≥1 and a class of polygamy inequalities in terms of theβ-th power of unified-(q,s)entanglement of assistance are established in this *** results present a general class of the monogamy and polygamy relations for bipartite entanglement measures based on unified-(q,s)entropy,which are tighter than the existing *** is more,some usual monogamy and polygamy relations,such as monogamy and polygamy relations based on entanglement of formation,Renyi-q entanglement of assistance and Tsallis-q entanglement of assistance,can be obtained from these results by choosing appropriate parameters(q,s)in unified-(q,s)entropy *** examples are also presented for illustration.

关键词： entanglement monogamy relations polygamy relations

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Community Detection Applications in Mobile Networks:Towards Data-Driven Design for Next Generation Cellular Networks

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Journal of Communications and Information Networks 2023年第2期8卷 155-163页

作者： Ayman Gaber Nashwa Abdelbaki Tamer Arafa School of Information Technology and Computer Science Nile UniversityGiza 12677Egypt

The existing radio access network(RAN)is facing many challenges to meet the very strict speed and latency requirements by different mobile applications in addition to the increasing pressure to reduce operating *** and development in RAN have been accelerated to tackle these challenges and to define how next generation mobile networks should look *** role of machine learning(ML)and artificial intelligence(AI)driven innovations within the RAN domain is strengthening and attracting lots of attention to tackle many of the challenging *** this paper we surveyed RAN network base stations(BSs)clustering and its applications in the *** paper also demonstrates how to leverage community detection algorithms to understand underlying community structures within *** areas(TAs)novel framework was developed by adapting existing community detection algorithm to solve the problem of statically partitioning a set of BSs into TA according to mobility ***,live network dataset in dense urban part of Cairo is used to assess how the developed framework is used to partition this part of the network more efficiently compared to other clustering *** obtained showed that the new methodology saved up to 34.6%of inter TA signaling overhead and surpassing other conventional clustering algorithms.

关键词： RAN mobility optimization RAN intelligence community detection ML

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Two Stages Segmentation Algorithm of Breast Tumor in DCE-MRI Based on Multi-Scale Feature and Boundary Attention Mechanism

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computers, Materials & Continua 2024年第7期80卷 1543-1561页

作者： Bing Li Liangyu Wang Xia Liu Hongbin Fan Bo Wang Shoudi Tong School of Automation Harbin University of Science and TechnologyHarbin150080China Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration Harbin150080China School of Computer Engineering Technology Guangdong Institute of Science and TechnologyZhuhai519090China

Nuclearmagnetic resonance imaging of breasts often presents complex *** tumors exhibit varying sizes,uneven intensity,and indistinct *** characteristics can lead to challenges such as low accuracy and incorrect segmentation during tumor ***,we propose a two-stage breast tumor segmentation method leveraging multi-scale features and boundary attention ***,the breast region of interest is extracted to isolate the breast area from surrounding tissues and ***,we devise a fusion network incorporatingmulti-scale features and boundary attentionmechanisms for breast tumor *** incorporate multi-scale parallel dilated convolution modules into the network,enhancing its capability to segment tumors of various sizes through multi-scale convolution and novel fusion ***,attention and boundary detection modules are included to augment the network’s capacity to locate tumors by capturing nonlocal dependencies in both spatial and channel ***,a hybrid loss function with boundary weight is employed to address sample class imbalance issues and enhance the network’s boundary maintenance capability through additional *** was evaluated using breast data from 207 patients at RuijinHospital,resulting in a 6.64%increase in Dice similarity coefficient compared to the *** results demonstrate the superiority of the method over other segmentation techniques,with fewer model parameters.

关键词： Dynamic contrast-enhanced magnetic resonance imaging(DCE-MRI) breast tumor segmentation multi-scale dilated convolution boundary attention the hybrid loss function with boundary weight

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A two-level meta-heuristic approach for the minimum dominating tree problem

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Frontiers of computer science 2023年第1期17卷 229-231页

作者： Caiquan XIONG Hang LIU Xinyun WU Na DENG School of Computer Science Hubei University of TechnologyWuhan 430068China

1 Introduction The minimum dominating tree(MDT)problem was first proposed by Zhang et al.[1]to produce a routing backbone of a *** et al.[2]proved that the MDT problem is NP-hard and introduced an approximation framework for solving *** important MDT problem algorithms are the artificial bee colony(ABC_DT)algorithm and ant colony optimization(ACO_DT)algorithm proposed by Sundar and Singh[3],the evolutionary algorithm with guided mutation(EA/G-MP)proposed by Chaurasia and Singh[4],the variable neighborhood search algorithm proposed by Dražićet al.[5],one improved artificial bee colony(ABC_DTP)algorithm proposed by Singh and Sundar[6],and a hybrid algorithm combining genetic algorithm proposed by Hu et al.[7].In this paper,we develop a two-level meta-heuristic(TLMH)for solving the MDT problem,aiming to find a dominating tree with the minimum weight for a given graph.

关键词： artificial dominating Singh

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A multilayer network diffusion-based model for reviewer recommendation

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Chinese Physics B 2024年第3期33卷 700-717页

作者：黄羿炜徐舒琪蔡世民吕琳媛 Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu 611731China Yangtze Delta Region Institute (Huzhou) University of Electronic Science and Technology of ChinaHuzhou 313001China Institute of Dataspace Hefei Comprehensive National Science CenterHefei 230088China Big Data Research Center University of Electronic Science and Technology of ChinaChengdu 611731China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China School of Cyber Science and Technology University of Science and Technology of ChinaHefei 230026China

With the rapid growth of manuscript submissions,finding eligible reviewers for every submission has become a heavy *** systems are powerful tools developed in computer science and information science to deal with this ***,most existing approaches resort to text mining techniques to match manuscripts with potential reviewers,which require high-quality textual information to perform *** this paper,we propose a reviewer recommendation algorithm based on a network diffusion process on a scholar-paper multilayer network,with no requirement for textual *** network incorporates the relationship of scholar-paper pairs,the collaboration among scholars,and the bibliographic coupling among *** results show that our proposed algorithm outperforms other state-of-the-art recommendation methods that use graph random walk and matrix factorization and methods that use machine learning and natural language processing,with improvements of over 7.62%in recall,5.66%in hit rate,and 47.53%in ranking *** work sheds light on the effectiveness of multilayer network diffusion-based methods in the reviewer recommendation problem,which will help to facilitate the peer-review process and promote information retrieval research in other practical scenes.

关键词： reviewer recommendation multilayer network network diffusion model recommender systems complex networks

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OAAFormer:Robust and Efficient Point Cloud Registration Through Overlapping-Aware Attention in Transformer

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Journal of computer science & technology 2024年第4期39卷 755-770页

作者： Jun-Jie Gao Qiu-Jie Dong Rui-An Wang Shuang-Min Chen Shi-Qing Xin Chang-He Tu Wenping Wang School of Computer Science and Technology Shandong UniversityQingdao 266237China School of Information and Technology Qingdao University of Science and TechnologyQingdao 266061China College of Engineering Texas A&M UniversityTexasTX 77843U.S.A.

In the domain of point cloud registration,the coarse-to-fine feature matching paradigm has received significant attention due to its impressive *** paradigm involves a two-step process:first,the extraction of multilevel features,and subsequently,the propagation of correspondences from coarse to fine ***,this approach faces two notable ***,the use of the Dual Softmax operation may promote one-to-one correspondences between superpoints,inadvertently excluding valuable ***,it is crucial to closely examine the overlapping areas between point clouds,as only correspondences within these regions decisively determine the actual *** these issues,we propose OAAFormer to enhance correspondence *** the one hand,we introduce a soft matching mechanism to facilitate the propagation of potentially valuable correspondences from coarse to fine *** the other hand,we integrate an overlapping region detection module to minimize mismatches to the greatest extent ***,we introduce a region-wise attention module with linear complexity during the fine-level matching phase,designed to enhance the discriminative capabilities of the extracted *** on the challenging 3DLoMatch benchmark demonstrate that our approach leads to a substantial increase of about 7%in the inlier ratio,as well as an enhancement of 2%-4%in registration ***,to accelerate the prediction process,we replace the Conventional Random Sample Consensus(RANSAC)algorithm with the selection of a limited yet representative set of high-confidence correspondences,resulting in a 100 times speedup while still maintaining comparable registration performance.

关键词： point cloud registration coarse-to-fine overlapping region feature matching Transformer

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Variance-SNR Based Noise Suppression on Linear Canonical Choi-Williams Distribution of LFM Signals

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Chinese Journal of Electronics 2022年第05期31卷 804-820页

作者： ZHANG Zhichao School of Mathematics and Statistics Nanjing University of Information Science and Technology School of Computer Science and Engineering Macau University of Science and Technology

By solving the existing expectation-signal-to-noise ratio(expectation-SNR) based inequality model of the closed-form instantaneous cross-correlation function type of Choi-Williams distribution(CICFCWD),the linear canonical transform(LCT) free parameters selection strategies obtained are usually *** the second-order moment variance outperforms the first-order moment expectation in accurately characterizing output SNRs, this paper uses the variance analysis technique to improve parameters selection strategies. The CICFCWD's average variance of deterministic signals embedded in additive zero-mean stationary circular Gaussian noise processes is first obtained. Then the so-called variance-SNRs are defined and applied to model a variance-SNR based inequality. A stronger inequalities system is also formulated by integrating expectation-SNR and variance-SNR based inequality models. Finally, a direct application of the system in noisy one-component and bi-component linear frequency-modulated(LFM) signals detection is studied. Analytical algebraic constraints on LCT free parameters newly derived seem more accurate than the existing ones, achieving better noise suppression effects. Our methods have potential applications in optical, radar, communication and medical signal processing.

关键词： linear canonical Choi-Williams distribution AWGN closed-form instantaneous cross-correlation function variance-SNR based inequality deterministic signals transforms LFM signal denoising statistical distributions Gaussian noise frequency modulation linear canonical transform free parameters signal detection average variance second-order moment variance expectation-signal-to-noise ratio variance-SNR based noise suppression CICFCWD analytical algebraic constraints LCT bicomponent linear frequency-modulated signals detection one-component linear frequency-modulated signals detection additive zero-mean stationary circular Gaussian noise processes LFM signals

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Pupillometry Analysis of Rapid Serial Visual Presentation at Five Presentation Rates

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Tsinghua science and technology 2024年第2期29卷 543-552页

作者： Xi Luo Yanfei Lin Rongxiao Guo Xirui Zhao Shangen Zhang Xiaorong Gao School of Integrated Circuits and Electronics Beijing Institute of TechnologyBeijing 100081China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China School of Medicine Tsinghua UniversityBeijing 100084China

In this study,the effect of presentation rates on pupil dilation is investigated for target recognition in the Rapid Serial Visual Presentation(RSVP)*** this experiment,the RSVP paradigm with five different presentation rates,including 50,80,100,150,and 200 ms,is *** pupillometry data of 15 subjects are collected and *** pupillometry results reveal that the peak and average amplitudes for pupil size and velocity at the 80-ms presentation rate are considerably higher than those at other presentation *** average amplitude of pupil acceleration at the 80-ms presentation rate is significantly higher than those at the other presentation *** latencies under 50-and 80-ms presentation rates are considerably lower than those of 100-,150-,and 200-ms presentation ***,no considerable differences are observed in the peak,average amplitude,and latency of pupil size,pupil velocity,and acceleration under 100-,150-,and 200-ms presentation *** results reveal that with the increase in the presentation rate,pupil dilation first increases,then decreases,and later reaches *** 80-ms presentation rate results in the largest point of pupil *** correlation is observed between pupil dilation and recognition accuracy under the five presentation rates.

关键词： pupil dilation presentation rate Rapid Serial Visual Presentation(RSVP)

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Attentive Large Kernel Network With Mixture of Experts for Video Deblurring

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IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 5575-5588页

作者： Zhang, Chaopeng Xiong, Ruiqin Fan, Xiaopeng Zhao, Debin Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Peking University Institute of Digital Media School of Electronic Engineering and Computer Science Beijing100871 China Peng Cheng Laboratory Shenzhen519055 China

Video deblurring is a fundamental problem in low-level vision, and many methods have employed designs based on CNNs and transformers. Traditional CNNs often require deeper architectures to achieve a larger receptive field, which may not be optimal for spatially non-uniform blurs and intense motion blurs. While transformers offer a large receptive field, their quadratic complexity due to attention designs typically imposes a significant computational burden. In addressing these issues, we present an Attentive Large Kernel Network with Mixture of Experts (ALK-MoE). In ALK-MoE, an attentive large kernel backbone network is proposed. On one hand, it inherently extends the network’s receptive field through its large kernel design. On the other hand, it addresses the quadratic complexity of attention by employing a sophisticated attention design, thus maintaining its ability to capture long-range dependencies. Furthermore, to achieve more precise and robust alignment of inter-frame features using optical flow for better utilization of clear frames, a mixture of experts model is proposed. It involves integrating optical flow updates between different experts in a residual manner. Our ablation experiments and experiments on multiple datasets indicate that ALK-MoE achieves comparable or superior performance compared to Transformer-based methods, with lower complexity. © 1991-2012 IEEE.

关键词： Multimedia systems

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