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DMSACNN: Deep Multiscale Attentional Convolutional Neural Network for EEG-Based Motor Decoding

IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Liu, Ke Xing, Xin Yang, Tao Yu, Zhuliang Xiao, Bin Wang, Guoyin Wu, Wei Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing400065 China Chongqing University of Posts and Telecommunications Key Laboratory of Big Data Intelligent Computing China Chongqing University of Posts and Telecommunications Key Laboratory of Cyberspace Big Data Intelligent Security of Ministry of Education China South China University of Technology College of Automation Science and Engineering Guangzhou510641 China Chongqing Normal University National Center for Applied Mathematics in Chongqing Chongqing401331 China Shanghai Jiao Tong University School of Medicine Songjiang Hospital Songjiang Research Institute Shanghai Key Laboratory of Emotions and Affective Disorders Shanghai201600 China

Objective: Accurate decoding of electroencephalogram (EEG) signals has become more significant for the brain-computer interface (BCI). Specifically, motor imagery and motor execution (MI/ME) tasks enable the control of external devices by decoding EEG signals during imagined or real movements. However, accurately decoding MI/ME signals remains a challenge due to the limited utilization of temporal information and ineffective feature selection methods. Methods: This paper introduces DMSACNN, an end-to-end deep multiscale attention convolutional neural network for MI/ME-EEG decoding. DMSACNN incorporates a deep multiscale temporal feature extraction module to capture temporal features at various levels. These features are then processed by a spatial convolutional module to extract spatial features. Finally, a local and global feature fusion attention module is utilized to combine local and global information and extract the most discriminative spatiotemporal features. Main results: DMSACNN achieves impressive accuracies of 78.20%, 96.34% and 70.90% for hold-out analysis on the BCI-IV-2a, High Gamma and OpenBMI datasets, respectively, outperforming most of the state-of-the-art methods. Conclusion and significance: These results highlight the potential of DMSACNN in robust BCI applications. Our proposed method provides a valuable solution to improve the accuracy of the MI/ME-EEG decoding, which can pave the way for more efficient and reliable BCI systems. The source code for DMSACNN is available at https://***/xingxin-99/***. © 2025 IEEE.

关键词： Convolutional neural networks

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A Fusion Tuning Method for Named Entity Recognition 12th

A Fusion Tuning Method for Named Entity Recognition

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12th CCF Conference on bigdata, bigdata 2024

作者： Wang, Jitian Chen, Yanping Zou, Anqi Qin, Yongbin Huang, Ruizhang Text Computing and Cognitive Intelligence Engineering Research Center of National Education Ministry Guizhou University Guiyang550025 China State Key Laboratory of Public Big Data Guizhou University Guiyang China College of Computer Science and Technology Guizhou University Guiyang550025 China

ISBN: (纸本)9789819610235

In named entity recognition, the main methods for constructing deep neural networks are fine-tuning and prompt tuning. Fine-tuning is a commonly used paradigm to optimize neural networks by using task-specific objective functions to make them more adapted to solve downstream tasks. In which pre-trained language models are used as external resources to provide contextual representations. Prompt tuning is a recent paradigm in which the downstream tasks are reformulated to look more like those solved during the original PLM training with the help of a textual prompt. It is effective to utilize potential knowledge of PLMs. In this paper, we propose the fusion approach, which sufficiently fuses the semantics obtained by extracting fine-tuning and prompt-tuning. In our approach, the model makes decisions based on the information obtained from the reconstructive fusion of the fine-tuned semantics and the semantics of the pre-trained language model. The representation obtained by fine-tuning and the representation obtained by prompt tuning are reconstructed into a multidimensional semantics using a gate fusion module (GFM). In particularly, our model has two gate components, one gate to extract the semantics of fine-tuning and the other gate to extract the semantics of prompt tuning, and finally the reconstructed semantic representation is obtained. This takes full advantage of the potential knowledge of PLM. Our model is evaluated on the CONLL2003, ACE2005 and GENIA corpora. It achieves performance close to the state-of-the-art in F1 scores. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Semantics

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A Triangular Stable Node Network based on Self-supervised Learning for personalized prediction

A Triangular Stable Node Network based on Self-supervised Le...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Qing Gao, Qian Fan, Jun Zhang, Zhiqiang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd. No.1999 Shunhua road Shandong Jinan250101 China

ISBN: (纸本)9798350368741

In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study aims to bridge this gap by firstly constructing a triangular stable node network model, tailored to manage implicit information with precision. Recognizing the challenge of pinpointing novel structures within large-scale graphs, we propose SLTSNN-a triangular stable node network based on self-supervised learning for personalized prediction. SLTSNN innovates by maximizing mutual information between graph-level and patch-level representations, while augmenting graph representations through extended enhanced representations. Additionally, it incorporates triangular data associations and introduces a triangular allocation attention network, which emphasizes strongly correlated preference features among similar users. Furthermore, SLTSNN employs contrastive learning to maximize mutual information between graph vectors and hidden representations, distinguishing between high-order global and local representations. The model's effectiveness in enhancing user preferences and capturing new graph structures is evidenced by its performance on hit rate and normalized discounted cumulative gain metrics across three datasets. © 2025 IEEE.

关键词： graph contrastive learning graph neural networks recommender systems self-supervised learning

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N-Body Simulation Inspired by Metaheuristics Optimization

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Computer Systems Science & Engineering 2022年第6期41卷 1143-1155页

作者： Muhammad Ali Ismail Maria Waqas Farah Sadiq National Center in Big Data and Cloud Computing Department of Computer and Info Systems EngineeringNED University of Engineering and TechnologyKarachiPakistan

The N-body problem in classical physics, is the calculation of force ofgravitational attraction of heavenly bodies towards each other. Solving this problem for many heavenly bodies has always posed a challenge to physicists andmathematicians. Large number of bodies, huge masses, long distances and exponentially increasing number of equations of motion of the bodies have been themajor hurdles in solving this problem for large and complex galaxies. Adventof high performance computational machines have mitigated the problem to muchextent, but still for large number of bodies it consumes huge amount of resourcesand days for computation. Conventional algorithms have been able to reduce thecomputational complexity from O n2 ð Þ to O nlogn ð Þ by splitting the space into atree or mesh network, researchers are still looking for improvements. In thisresearch work we propose a novel solution to N-body problem inspired by metaheuristics algorithms. The proposed algorithm is simulated for various time periods of selected heavenly bodies and analyzed for speed and accuracy. Theresults are compared with that of conventional algorithms. The outcomes showabout 50% time saving with almost no loss in accuracy. The proposed approachbeing a metaheuristics optimization technique, attempts to find optimal solution tothe problem, searching the entire space in a unique and efficient manner in a verylimited amount of time.

关键词： N-body problem metaheuristics optimization particle swarm optimization heavenly bodies

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The 33311 left-right bilepton model

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Physics Letters, Section B: Nuclear, Elementary Particle and High-Energy Physics 2025年 866卷

作者： Corianò, Claudio Frampton, Paul H. Melle, Dario Dipartimento di Matematica e Fisica Università del Salento Italy INFN Sezione di Lecce Via Arnesano Lecce 73100 Italy National Center for HPC Big Data and Quantum Computing Italy CNR-Nanotec Lecce Italy

We discuss extension of the electroweak gauge group to SU(3)L×SU(3)R×U(1)L×U(1)R, based on the principle that anomaly cancellation may inherently involve three fermion generations. The explanation for t...

We discuss extension of the electroweak gauge group to SU(3)_L×SU(3)_R×U(1)_L×U(1)_R, based on the principle that anomaly cancellation may inherently involve three fermion generations. The explanation for three light quark-lepton families remains, but new particles are introduced which have no TeV scale upper limit on their masses. This approach, originally realized in the framework of the 331 Model, has been consistently defined up to the TeV scale. Our approach maintains the 331 Model's natural explanation for three light quark-lepton families, while introducing new physics with no upper bound on its energy scale. © 2025 The Author(s)

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Comprehensive Architecture Search for Deep Graph Neural Networks

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IEEE Transactions on big data 2025年

作者： Dong, Yukang Pan, Fanxing Gui, Yi Jiang, Wenbin Wan, Yao Zheng, Ran Jin, Hai National Engineering Research Center for Big Data Technology Huazhong University of Science and Technology Wuhan430074 China Huazhong University of Science and Technology Service Computing Technology and System Laboratory Wuhan430074 China Huazhong University of Science and Technology Cluster and Grid Computing Laboratory Wuhan430074 China Huazhong University of Science and Technology School of Computer Science and Technology Wuhan430074 China Zhejiang Lab Hangzhou311121 China

In recent years, Neural Architecture Search (NAS) has emerged as a promising approach for automatically discovering superior model architectures for deep Graph Neural Networks (GNNs). Different methods have paid attention to different types of search spaces. However, due to the time-consuming nature of training deep GNNs, existing NAS methods often fail to explore diverse search spaces sufficiently, which constrains their effectiveness. To crack this hard nut, we propose CAS-DGNN, a novel comprehensive architecture search method for deep GNNs. It encompasses four kinds of search spaces that are the composition of aggregate and update operators, different types of aggregate operators, residual connections, and hyper-parameters. To meet the needs of such a complex situation, a phased and hybrid search strategy is proposed to accommodate the diverse characteristics of different search spaces. Specifically, we divide the search process into four phases, utilizing evolutionary algorithms and Bayesian optimization. Meanwhile, we design two distinct search methods for residual connections (All-connected search and Initial Residual search) to streamline the search space, which enhances the scalability of CAS-DGNN. The experimental results show that CAS-DGNN achieves higher accuracy with competitive search costs across ten public datasets compared to existing methods. © 2015 IEEE.

关键词： Neural network models

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Morphology and structural stability of Pt–Pd bimetallic nanoparticles

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Chinese Physics B 2015年第3期24卷 149-156页

作者：刘暾东郑骥文邵桂芳范天娥文玉华 Center for Cloud Computing and Big Data Department of AutomationXiamen University Institute of Theoretical Physics and Astrophysics Department of PhysicsXiamen University

The morphologies and structures of Pt–Pd bimetallic nanoparticles determine their chemical and physical ***, a fundamental understanding of their morphologies and structural stabilities is of crucial importance to their applications. In this article, we have performed Monte Carlo simulations to systematically explore the structural stability and structural features of Pt–Pd alloy nanoparticles. Different Pt/Pd ratios, and particle sizes and shapes were *** simulated results reveal that the truncated octahedron, which has the remarkably lowest energy among all the considered shapes, exhibits the best structural stability while the tetrahedron has the worst invariably. Furthermore, all the structures of Pt–Pd alloy nanoparticles present Pd-rich in the outmost layer but Pt-rich in the sub-outmost layer. Especially, atomic distribution and chemical short-range order parameter were applied to further characterize the structural features of Pt–Pd alloy nanoparticles. This study provides a significant insight not only into the structural stability of Pt–Pd alloy nanoparticles with different compositions, and particle sizes and shapes but also to the design of bimetallic nanoparticles.

关键词： nanoparticles structural stability Monte Carlo simulation

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Towards high-throughput and low-latency billion-scale vector search via CPU/GPU collaborative filtering and re-ranking 25

Towards high-throughput and low-latency billion-scale vector...

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Proceedings of the 23rd USENIX Conference on File and Storage Technologies

作者： Bing Tian Haikun Liu Yuhang Tang Shihai Xiao Zhuohui Duan Xiaofei Liao Hai Jin Xuecang Zhang Junhua Zhu Yu Zhang National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Huawei Technologies Co. Ltd Towards high-throughput and low-latency billion-scale vector search via CPU/GPU collaborative filtering and re-ranking

ISBN: (纸本)9781939133458

Approximate nearest neighbor search (ANNS) has emerged as a crucial component of database and AI infrastructure. Ever-increasing vector datasets pose significant challenges in terms of performance, cost, and accuracy for ANNS services. None of modern ANNS systems can address these issues simultaneously. In this paper, we present Fusion-ANNS, a high-throughput, low-latency, cost-efficient, and high-accuracy ANNS system for billion-scale datasets using SSDs and only one entry-level GPU. The key idea of Fusion-ANNS lies in CPU/GPU collaborative filtering and reranking mechanisms, which significantly reduce I/O operations across CPUs, GPU, and SSDs to break through the I/O performance bottleneck. Specifically, we propose three novel designs: (1) multi-tiered indexing to avoid data swapping between CPUs and GPU, (2) heuristic re-ranking to eliminate unnecessary I/Os and computations while guaranteeing high accuracy, and (3) redundant-aware I/O deduplication to further improve I/O efficiency. We implement FusionANNS and compare it with the state-of-the-art SSD-based ANNS system-SPANN and GPU-accelerated in-memory ANNS system-RUMMY. Experimental results show that FusionANNS achieves 1) 9.4-13.1× higher query per second (QPS) and 5.7-8.8× higher cost efficiency compared with SPANN; 2) and 2-4.9× higher QPS and 2.3-6.8× higher cost efficiency compared with RUMMY, while guaranteeing low latency and high accuracy.

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SSAM: a span spatial attention model for recognizing named entities

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Scientific Reports 2025年第1期15卷 1-13页

作者： Wang, Kai Wen, Kunjian Chen, Yanping Qin, Yongbin Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and Technology Guizhou University Guiyang China State Key Laboratory of Public Big Data Guizhou University Guiyang China ByteDance Ltd. Beijing China

Mapping a sentence into a two-dimensional (2D) representation can flatten nested semantic structures and build multi-granular span dependencies in named entity recognition. Existing approaches to recognizing named entities often classify each entity span independently, which ignores the spatial structures between neighboring spans. To address this issue, we propose a Span Spatial Attention Model (SSAM) that consists of a token encoder, a span generation module, and a 2D spatial attention network. The SSAM employs a two-channel span generation strategy to capture multi-granular features. Unlike traditional attention implemented on a sequential sentence representation, spatial attention is applied to a 2D sentence representation, enabling the model to learn the spatial structures of the sentence. This allows the SSAM to adaptively encode important features and suppress non-essential information in the 2D sentence representation. Experimental results on the GENIA, ACE2005, and ACE2004 datasets demonstrate that our proposed model achieves state-of-the-art performance, with F1-scores of 81.82%, 89.04%, and 89.24%, respectively. The code is available at https://***/Gzuwkj/SpatialAttentionForNer. © The Author(s) 2025.

关键词： Named entity recognition Natural language processing Spatial attention

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A unified multi-granulation hierarchy structure: Construction, Selection, Fusion and Reduction

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Information Fusion 2025年 123卷

作者： Zhang, Xiaoxia Zhang, Jiangtao Wang, Guoyin Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China National Center for Applied Mathematics in Chongqing Chongqing Normal University Chongqing401331 China

Granular computing is a new method that can simulate human brain thinking to solve complex problems, and it has gradually become an important theory for addressing uncertainty issues. The core of granular computing lies in its use of hierarchical, classification, or clustering methods to process uncertain information. However, the broad applicability of this approach has led to its blurred theoretical boundaries, with varying definitions of "granules" and computational paradigms across studies based on differing requirements. Currently, the absence of a unified granular computing model prevents the consistent handling of uncertain information under a standardized scale and paradigm. This paper attempts to fill this gap from a unified perspective by providing a multi-granulation hierarchical structure within a unified framework. Based on this, different studies can adaptively select appropriate granular layers according to their specific time constraints or precision requirements. Here, we adopt quantum genetic algorithms (QG) for optimal granular layer selection. After the granular layers are selected, a fusion algorithm is developed to combine them into a super layer, preserving essential information for subsequent attribute reduction. The attribute reduction process is guided by conditional entropy, which further refines the feature set, enhancing the model's efficiency and accuracy. Experimental results on twelve public datasets and four real datasets both demonstrate the effectiveness of the proposed method, showing superior performance in terms of classification accuracy and feature reduction compared to seven existing approaches. This study establishes a unified framework for various granular computing models, offering new insights into the integration of granular computing models and adaptive optimization techniques for real-world data analysis applications. © 2025 Elsevier B.V.

关键词： Granular computing

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