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12th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2023

作者： Lei, Ningjie Cai, Jinxiang Qian, Yixin Zheng, Zhilong Han, Chao Liu, Zhiyue Huang, Qingbao School of Electrical Engineering Guangxi University Guangxi Nanning China School of Computer Electronics and Information Guangxi University Guangxi Nanning China Guangxi Key Laboratory of Multimedia Communications and Network Technology Nanning China

ISBN: (纸本)9783031446986

With the increasing popularity of online videos, research on video corpus retrieval (VCR) has made significant progress. However, existing VCR models have not performed well in the medical field due to the unique characteristics of medical VCR task. Specifically, the open-ended queries used in medical VCR are more challenging compared to image-caption style queries, and the long duration of medical videos poses a great burden on model retrieval efficiency. To address these challenges, we propose a two-stage framework based on GPT-3.5 and cross-modal contrastive global-span (CCGS) for medical video VCR (termed GPT-CMR). In the first stage, we leverage the powerful natural language processing capabilities of the large language model (LLM) GPT-3.5 to improve retrieval efficiency. In the second stage, we use CCGS model to further enhance retrieval accuracy. Additionally, we developed a CCGS-VCR Analyzer to leverage the characteristics of the CCGS model’s output without additional training costs. According to the official result, our method achieve first place in Track 2 of the NLPCC 2023 Task 5 competition. Experiments show that our method has retrieval efficiency and accuracy far exceeding the official baseline. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Efficiency

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PlaNet: Learning to Mitigate Atmospheric Turbulence in Planetary Images 39

PlaNet: Learning to Mitigate Atmospheric Turbulence in Plane...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xia, Yifei Zhou, Chu Zhu, Chengxuan Xu, Chao Shi, Boxin National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China National Engineering Research Center of Visual Technology School of Computer Science Peking University China National Institute of Informatics Japan National Key Lab of General AI School of Intelligence Science and Technology Peking University China

ISBN: (纸本)157735897X

Obtaining planetary images with good visual quality is not an easy task since they are usually degenerated by atmospheric turbulence during the imaging procedure. Existing atmospheric turbulence mitigation methods designed for conventional images cannot be applied to planetary images, since the objects on the Earth have totally different degeneration patterns to planets. Besides, in planetary imaging, photographers often capture as many frames as possible to reduce the noise level of planetary images, which requires the method designed for planetary images to support an arbitrary number of input frames. In this paper, we propose a vertical distance-aware turbulence simulation pipeline to synthesize realistic planetary images in accordance with their unique degeneration patterns at a large scale with affordable computational cost, and design a neural network to mitigate the turbulence with flexible input frames by adopting an edge-based supervision strategy to handle the background scarcity issue. Experimental results show that our method achieves state-of-the-art performance on both synthetic and real-world images. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Adaptive Sensor Scheduling for Federated Learning over 6G in-X Subnetworks 2

Adaptive Sensor Scheduling for Federated Learning over 6G in...

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2nd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2023

作者： Wu, Qiaoli Gao, Bo Xiong, Ke Beijing Jiaotong University Engineering Research Center of Network Management Technology for High-Speed Railway Ministry of Education School of Computer and Information Technology Beijing China

ISBN: (纸本)9798350348002

In the upcoming 6G era, various end devices with data and computing resources can collaborate through federated learning (FL) to achieve the goal of ubiquitous intelligence. Inside any of those 'X' (i.e. everything) devices, however, a6G in-X subnetwork that wirelessly uploads training data from onboard sensors to an embedded access point (AP) may become a communication bottleneck during the process of FL. To support communication-efficient FL over in-X subnetworks, this paper proposes an adaptive sensor scheduling algorithm that jointly solves a queue stability problem and an age of information (AoI) optimization problem, based on Lyapunov optimization and MaxRatio scheduling methods. Simulations show that the proposed algorithm achieves higher FL accuracy, while guaranteeing queue stability at APs and better data freshness (quality) for data uploading over in-X subnetworks. © 2023 IEEE.

关键词： Scheduling algorithms

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FIMD: Fusion-Inspired Modality Distillation for Enhanced MRI Segmentation in Incomplete Multi-Modal Scenarios 24

FIMD: Fusion-Inspired Modality Distillation for Enhanced MRI...

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7th International Conference on Image and Graphics Processing, ICIGP 2024

作者： Yang, Rui Wang, Xiao Xu, Xin Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology China School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Provincial Key Laboratory of Multimedia and Network Communication Engineering Wuhan China

ISBN: (纸本)9798400716720

Magnetic Resonance Imaging (MRI) is an invaluable tool for brain tumor segmentation. However, in clinical practice, certain modalities might be unavailable, leading to potential performance degradation in prediction tasks. According to current implementations, different modalities are treated as independent entities during the feature extraction phase, ignoring their inherent complementary nature. In this paper, inspired by knowledge distillation techniques, we introduce the Fusion-Inspired Modality Distillation (FIMD) framework. FIMD leverages the output from a fused multi-modal model as a teacher to guide the training of individual modalities. Each modality benefits from the collective knowledge of all modalities, enhancing its performance. Our FIMD method, devoid of specific architectural constraints, seamlessly integrates into existing multi-modal brain tumor segmentation frameworks. Notably, extensive experiments on BraTS2020, BraTS2018, and BraTS2015 datasets indicate that FIMD can enhance the performance of current state-of-the-art (SOTA) algorithms on Dice by an average of 0.55-1.60%. Especially in the case of missing modalities, the effectiveness of the FIMD framework in enhancing the robustness and accuracy of brain tumor segmentation is demonstrated. © 2024 ACM.

关键词： Semantic Segmentation

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A Low-Rank Matching Attention Based Cross-Modal Feature Fusion Method for Conversational Emotion Recognition

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IEEE Transactions on Affective Computing 2025年第2期16卷 1177-1189页

作者： Shou, Yuntao Liu, Huan Cao, Xiangyong Meng, Deyu Dong, Bo Xi’an Jiaotong University School of Computer Science and Technology Ministry of Education Key Laboratory for Intelligent Networks and Network Security Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Shaanxi Xi’an710049 China Xi’an Jiaotong University School of Computer Science and Technology Shaanxi Xi’an710049 China SHMEEA BigKE Joint Innovation Center Shanghai201318 China Xi’an Jiaotong University School of Mathematics and Statistics Shaanxi Xi’an710049 China

Conversational emotion recognition (CER) is an important research topic in human-computer interactions. Although recent advancements in transformer-based cross-modal fusion methods have shown promise in CER tasks, they tend to overlook the crucial intra-modal and inter-modal emotional interaction or suffer from high computational complexity. To address this, we introduce a novel and lightweight cross-modal feature fusion method called Low-Rank Matching Attention Method (LMAM). LMAM effectively captures contextual emotional semantic information in conversations while mitigating the quadratic complexity issue caused by the self-attention mechanism. Specifically, by setting a matching weight and calculating inter-modal features attention scores row by row, LMAM requires only one-third of the parameters of self-attention methods. We also employ the low-rank decomposition method on the weights to further reduce the number of parameters in LMAM. As a result, LMAM offers a lightweight model while avoiding overfitting problems caused by a large number of parameters. Moreover, LMAM is able to fully exploit the intra-modal emotional contextual information within each modality and integrates complementary emotional semantic information across modalities by computing and fusing similarities of intra-modal and inter-modal features simultaneously. Experimental results verify the superiority of LMAM compared with other popular cross-modal fusion methods on the premise of being more lightweight. Also, LMAM can be embedded into any existing state-of-the-art CER methods in a plug-and-play manner, and can be applied to other multi-modal recognition tasks, e.g., session recommendation and humour detection, demonstrating its remarkable generalization ability. © 2010-2012 IEEE.

关键词： Semantics

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Transformer-Based Single-Cell Language Model:A Survey

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Big Data Mining and Analytics 2024年第4期7卷 1169-1186页

作者： Wei Lan Guohang He Mingyang Liu Qingfeng Chen Junyue Cao Wei Peng Guangxi Key Laboratory of Multimedia Communications and Network Technology School of ComputerElectronic and InformationGuangxi UniversityNanning 530004China School of Computer and Electronic Information Guangxi UniversityNanning 530004China College of Life Science and Technology Guangxi UniversityNanning 530004China Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650500China

The transformers have achieved significant accomplishments in the natural language processing as its outstanding parallel processing capabilities and highly flexible attention *** addition,increasing studies based on transformers have been proposed to model single-cell *** this review,we attempt to systematically summarize the single-cell language models and applications based on ***,we provide a detailed introduction about the structures and principles of ***,we review the single-cell language models and large language models for single-cell data ***,we explore the datasets and applications of single-cell language models in downstream tasks,such as batch correction,cell clustering,cell type annotation,gene regulatory network inference,and perturbation ***,we discuss the challenges of single-cell language models and provide promising research *** hope this review will serve as an up-to-date reference for researchers who are interested in the direction of single-cell language models.

关键词： language model transformers deep learning single-cell data

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A New Method for Predefined-Time Synchronization Control of Memristive Cohen-Grossberg Neural networks

A New Method for Predefined-Time Synchronization Control of ...

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2023 IEEE International Conference on Memristive Computing and Applications, ICMCA 2023

作者： Liu, Aidi Yu, Wenyue Huang, Lili Guo, Jian Lei, Tengfei Zhao, Hui School of Computer and Information Engineering Qilu Institute of Technology Jinan China School of Computer and Information Engineering Qilu Institute of Technology China Collaborative Innovation Center of Memristive Computing Application School of Computer and Information Engineering Qilu Institute of Technology China School of Information Science and Engineering University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan China

ISBN: (纸本)9798350316230

This article mainly conducts a series of research on the synchronization control of Cohen-Grossberg neural networks. Firstly, a memristive Cohen-Grossberg neural network model with time-varying delay was constructed, and an equivalent system was obtained by combining inverse function theory, differential inclusion, and set-valued mapping. Secondly, a new predefined-time stability theorem was proposed, and sufficient conditions were obtained to ensure that the drive-response system achieves predefined-time synchronization by designing appropriate control strategy. The simulation results show that the stability time of the error system can be adjusted by the user, verifying the correctness and of the theory. © 2023 IEEE.

关键词： Neural network models

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Neuroevosp Arse: A Biologically Plausible Framework for Efficient 20

Neuroevosp Arse: A Biologically Plausible Framework for Effi...

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20th International computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2023

作者： Shen, Zhao Zhang, Sen Zheng, Peixiang Huang, Yuqi School of Computer Science and Engineering University of Electronic Science and Technology of China China Network &education Technology Center NorthWest A&F University City YangLing China

ISBN: (纸本)9798350318982

SNNs have shown great potential in terms of power efficiency and event-driven processing during inference. To fully utilize their low power consumption and further improve their efficiency, researchers have explored pruning methods to create sparse SNNs without redundant connections after training. However, parameter redundancy still poses a challenge for SNNs during training. Inspired by the dynamic rewiring process observed in the human brain, we propose an efficient NeuroEvoSparse framework for SNNs, called NeuroEvoSparse-SNNs, to enable sparse SNN training from the beginning. In this framework, the pruning and regeneration of synaptic connections in SNNs evolve dynamically during learning, while maintaining a specific level of structural sparsity. This allows NeuroEvoSparse-SNNs to explore all potential parameters over time and find the optimal sparse connectivity. In summary, our proposed NeuroEvoSparse framework is a promising approach for training sparse SNNs from scratch. By incorporating biologically plausible evolutionary mechanisms and addressing the gap in expressiveness between sparse and dense training, this framework effectively learns SNNs with sparse structures while minimizing accuracy loss. This work has significant implications for lightweight training and inference of SNNs, which can lead to low power consumption and minimal memory usage in various applications. © 2023 IEEE.

关键词： Energy efficiency

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Data-driven production optimization using particle swarm algorithm based on the ensemble-learning proxy model

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Petroleum Science 2023年第5期20卷 2951-2966页

作者： Shu-Yi Du Xiang-Guo Zhao Chi-Yu Xie Jing-Wei Zhu Jiu-Long Wang Jiao-Sheng Yang Hong-Qing Song School of Civil and Resource Engineering University of Science and Technology BeijingBeijing100083China National&Local Joint Engineering Lab for Big Data Analysis and Computer Technology Beijing100190China CNPC International Hong Kong Limited Abu DhabiUnited Arab Emirates Computer Network Information Center Chinese Academy of SciencesBeijing065007China China National Petroleum Corporation Langfang065007HebeiChina

Production optimization is of significance for carbonate reservoirs,directly affecting the sustainability and profitability of reservoir *** physics-based numerical simulations suffer from insufficient calculation accuracy and excessive time consumption when performing production *** establish an ensemble proxy-model-assisted optimization framework combining the Bayesian random forest(BRF)with the particle swarm optimization algorithm(PSO).The BRF method is implemented to construct a proxy model of the injectioneproduction system that can accurately predict the dynamic parameters of producers based on injection data and production *** the help of proxy model,PSO is applied to search the optimal injection pattern integrating Pareto front *** experimental testing,the proxy model not only boasts higher prediction accuracy compared to deep learning,but it also requires 8 times less time for *** addition,the injection mode adjusted by the PSO algorithm can effectively reduce the gaseoil ratio and increase the oil production by more than 10% for carbonate *** proposed proxy-model-assisted optimization protocol brings new perspectives on the multi-objective optimization problems in the petroleum industry,which can provide more options for the project decision-makers to balance the oil production and the gaseoil ratio considering physical and operational constraints.

关键词： Production optimization Random forest The Bayesian algorithm Ensemble learning Particle swarm optimization

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A boosting-type convergence result for *** with factorized multi-class classifiers 24

A boosting-type convergence result for *** with factorized m...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Xin Zou Zhengyu Zhou Jingyuan Xu Weiwei Liu School of Computer Science and National Engineering Research Center for Multimedia Software and Institute of Artificial Intelligence and Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University

ISBN: (纸本)9798331314385

ADABOOST is a well-known algorithm in boosting. Schapire and Singer propose, an extension of ADABOOST, named ***, for multi-class classification problems. Kégl shows empirically that *** works better when the classical one-against-all base classifiers are replaced by factorized base classifiers containing a binary classifier and a vote (or code) vector. However, the factorization makes it much more difficult to provide a convergence result for the factorized version of ***. Then, Kégl raises an open problem in COLT 2014 to look for a convergence result for the factorized ***. In this work, we resolve this open problem by presenting a convergence result for *** with factorized multi-class classifiers.

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