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'Pre-train, prompt' Framework to Boost Graph Neural networks Performance in EEG Analysis

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

作者： Cui, Can-Ming Chen, Hong-Yi Chen, Man-Sheng Li, Jiahong Tong, Zhaopeng Fang, Chanmei Wang, Chang-Dong Cai, Yuexin Department of Otolaryngology Sun Yat-sen Memorial Hospital Sun Yat-sen University Guangdong Guangzhou510000 China Sun Yat-sen University School of Computer Science and Engineering Guangzhou510000 China Guangxi Zhuang Autonomous Region Information Center Guangxi Key Laboratory of Digital Infrastructure Nanning530000 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China

Electroencephalography (EEG) is a vital noninvasive technique used in neuroscience research and clinical diagnosis. However, EEG data have a complex nonEuclidean structure and are often scarce, making training effective graph neural network (GNN) models difficult. We propose a 'pre-train, prompt' framework in graph neural networks for EEG analysis, called GNN-based EEG Prompt Learning (GEPL). The framework first uses unsupervised contrastive learning to pre-train on a large-scale EEG dataset. It then transfers the generic EEG knowledge learned by the model to target EEG datasets through graph prompt learning, thereby enhancing the model's performance with a limited amount of EEG data from the target domain. We tested the framework on five EEG datasets, and the results showed that GEPL outperformed traditional fine-tuning methods in classification accuracy and area under the ROC curve (AUC). GEPL demonstrated improved generalization, robustness, and computational efficiency, thereby significantly reducing the overfitting risks associated with limited EEG data. Moreover, the model provided interpretable results, highlighting relevant brain regions during classification tasks. This research suggests that the 'pre-train, prompt' paradigm is well-suited for EEG analysis and offers potential applications in other domains where data are limited. © 2013 IEEE.

关键词： Contrastive Learning

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Improving Heterogeneous Model Reuse by Density Estimation

arXiv

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arXiv 2023年

作者： Tang, Anke Luo, Yong Hu, Han He, Fengxiang Su, Kehua Du, Bo Chen, Yixin Tao, Dacheng School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China Hubei Luojia Laboratory Wuhan China School of Information and Electronics Beijing Institute of Technology China JD Explore Academy *** Inc. China Department of CSE Washington University in St. Louis United States The University of Sydney Australia

This paper studies multiparty learning, aiming to learn a model using the private data of different participants. Model reuse is a promising solution for multiparty learning, assuming that a local model has been trained for each party. Considering the potential sample selection bias among different parties, some heterogeneous model reuse approaches have been developed. However, although pre-trained local classifiers are utilized in these approaches, the characteristics of the local data are not well exploited. This motivates us to estimate the density of local data and design an auxiliary model together with the local classifiers for reuse. To address the scenarios where some local models are not well pre-trained, we further design a multiparty cross-entropy loss for calibration. Upon existing works, we address a challenging problem of heterogeneous model reuse from a decision theory perspective and take advantage of recent advances in density estimation. Experimental results on both synthetic and benchmark data demonstrate the superiority of the proposed method. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision theory

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Critical analysis: Bat algorithm based investigation and application on several domains

arXiv

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arXiv 2021年

作者： Umar, Shahla U. Rashid, Tarik A. Technical College of Informatics Sulaimani Polytechnic University Sulaimani KRG Iraq Network Department College of Computer Science and Information Technology Kirkuk University Kirkuk Iraq Computer Science and Engineering School of Science and Engineering University of Kurdistan Hewler Erbil- KRG Iraq

In recent years several swarm optimization algorithms, such as Bat Algorithm (BA) have emerged, which was proposed by Xin-She Yang in 2010. The idea of the algorithm was taken from the echolocation ability of bats. Purpose- The purpose of this study is to provide the reader with a full study of the Bat Algorithm, including its limitations, the fields that the algorithm has been applied, versatile optimization problems in different domains, and all the studies that assess its performance against other meta-heuristic algorithms. Design/Methodology/Approach- Bat Algorithm is given in-depth in terms of backgrounds, characteristics, limitations, it has also displayed the algorithms that hybridized with BA (K-Medoids, Back-propagation neural network, Harmony Search Algorithm, Differential Evaluation Strategies, Enhanced Particle Swarm Optimization, and Cuckoo Search Algorithm) and their theoretical results, as well as to the modifications that have been performed of the algorithm (Modified Bat Algorithm (MBA), Enhanced Bat Algorithm (EBA), Bat Algorithm with Mutation (BAM), Uninhabited Combat Aerial Vehicle-Bat algorithm with Mutation (UCAV-BAM), Nonlinear Optimization). It also provides a summary review that focuses on improved and new Bat Algorithm (Directed Artificial Bat Algorithm (DABA), Complex-valued Bat Algorithm (CBA), Principal Component Analyses- Bat Algorithm (PCA-BA), multiple strategies coupling Bat Algorithm (MixBAT), Directional Bat Algorithm (dBA)). Findings- Shed light on the advantages and disadvantages of this algorithm through all the researches that dealt with the algorithm in addition to the fields and applications it has addressed in the hope that it will help scientists understand and develop it. Originality/value- As far as the research community knowledge, there is no comprehensive survey study conducted on this algorithm coverıng all its aspects. © 2021, CC BY-NC-ND.

关键词： Swarm intelligence

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InGaP X(2)integrated photonics platform for broadband,ultra-efficient nonlinear conversion and entangled photon generation

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光：科学与应用（英文版） 2024年第12期13卷 3090-3098页

作者： Joshua Akin Yunlei Zhao Yuvraj Misra A.K.M.Naziul Haque Kejie Fang Holonyak Micro and Nanotechnology Laboratory and Department of Electrical and Computer Engineering University of Illinois at Urbana-ChampaignUrbanaIL 61801USA Illinois Quantum Information Science and Technology Center University of Illinois at Urbana-ChampaignUrbanaIL 61801USA

Nonlinear optics plays an important role in many areas of science and *** advance of nonlinear optics is empowered by the discovery and utilization of materials with growing optical *** we demonstrate an indium gallium phosphide(InGaP)integrated photonics platform for broadband,ultra-efficient second-order nonlinear *** InGaP nanophotonic waveguide enables second-harmonic generation with a normalized efficiency of 128,000％/W/cm2 at 1.55 μm pump wavelength,nearly two orders of magnitude higher than the state of the art in the telecommunication C ***,we realize an ultra-bright,broadband time-energy entangled photon source with a pair generation rate of 97 GHz/mW and a bandwidth of 115 nm centered at the telecommunication C *** InGaP entangled photon source shows high coincidence-to-accidental counts ratio CAR＞104 and two-photon interference visibility＞98％.The InGaP second-order nonlinear photonics platform will have wide-ranging implications for non-classical light generation,optical signal processing,and quantum networking.

关键词： generation integrated platform ingap broadband conversion entangled nonlinear photonics ultra-efficient

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Age Optimal Sampling for Unreliable Channels Under Unknown Channel Statistics

Age Optimal Sampling for Unreliable Channels Under Unknown C...

引用

International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless networks (WiOpt)

作者： Hongyi He Haoyue Tang Jiayu Pan Jintao Wang Jian Song Leandros Tassiulas Beijing National Research Center for Information Science and Technology (BNRist) Department of Electronic Engineering Tsinghua University Beijing China Department of Electronic Engineering Yale University New Haven USA Department of Electrical and Computer Engineering The Ohio State University Columbus OH USA Research Institute of Tsinghua University in Shenzhen Shenzhen China Shenzhen International Graduate School Tsinghua University Shenzhen China

In this work, we study a system with a sensor forwarding status update to the receiver through an error-prone channel, and the receiver sends the transmission results to the sensor via a reliable link. We assume both transmission links suffer from random delays. We use Age of information (AoI) to measure the freshness of the status information at the receiver. Our goal is to design a sampling policy that minimizes the expected time average AoI when the channel statistics are unknown. The problem is reformulated into a renewal-reward process optimization, and an online algorithm based on the Robbins-Monro algorithm is proposed. We prove that when the forward and backward transmission delays are bounded, the AoI difference between the online algorithm and the optimal policy decays with rate $\mathcal{O}(\ln K/K)$ , where $K$ is the number of successful transmissions. Simulation results validate the performance of our proposed algorithm.

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Performance Ranking Based on Bézier Ranking Principal Curve 1st

Performance Ranking Based on Bézier Ranking Principal Curve

引用

1st International Conference on Spatial Data and Intelligence, SpatialDI 2020

作者： Jin, Wei Guo, Danhuai Zhao, Li-kun Li, Ji-Chao Beijing Academy of Science and Technology Beijing100094 China Beijing Institute of New Technology Applications Beijing100094 China Computer Network Information Center Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Civil Engineering North China University of Technology Beijing100144 China

ISBN: (纸本)9783030698720

As for the problem of subjectivity and lack of scientificity in performance evaluation, the paper introduces a RPC model with cube Bézier curve being principal curve, and designs the learning algorithm of Bézier curve. First, evaluation indicators are chosen using Pearson correlation coefficient, RPC two-dimensional projection is conducted on relevant data and the ranking result of multiple indicator observation data is obtained. The experiment is done with the performance data of 12345 hotline telephone operators in one month of 2019 in one district of Beijing, and the ranking result is in line with the popular perception. The experimental result proves that as for the ranking in performance evaluation, RPC method is more scientific and interpretable than the method of weighted average and Elmap model. © 2021, Springer Nature Switzerland AG.

关键词： Interpolation

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Ameliorated Golden Jackal Optimization (Agjo) with Enhanced Movement and Multi-Angle Position Updating Strategy

SSRN

引用

SSRN 2023年

作者： Wahab, Magd Abdel Bai, Jianfu Khatir, Samir Abualigah, Laith Soete Laboratory Department of Electrical Energy Metals Mechanical Constructions and Systems Faculty of Engineering and Architecture Ghent University Belgium Computer Science Department Prince Hussein Bin Abdullah Faculty for Information Technology Al al-Bayt University Mafraq25113 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia College of Engineering Yuan Ze University Taiwan

Golden jackal optimization (GJO), a lately published meta-heuristic optimization algorithm, is inspired by the foraging behavior of pairs of golden jackals and shows an acceptable optimization performance. However, GJO exists a shortage in balancing exploration and exploitation, as it completely focused on exploitation in the later iterations. This study proposes a new variant of GJO, named Ameliorated Golden jackal optimization (AGJO). AGJO uses three strategies to alleviate the imbalance between the exploration and exploitation of GJO: the enhanced movement strategy, the global search strategy, and the multi-angle position update strategy for prey. An environmental disturbance factor is added to the third strategy to strengthen GJO's ability to evade the local optimal solution. The performance of AGJO is tested and compared with GJO and seven well-known meta-heuristic algorithms for 23 classical benchmark functions, CEC 2017 and CEC 2019. These results determined that AGJO performs better over 90% of these functions compared to GJO. AGJO is also highly competitive in terms of optimization capability and convergence speed compared with other algorithms. Finally, AGJO is applied to optimize five engineering design problems with constraints. Results show that AGJO is a more potential algorithm for solving real problems with constraints than GJO and other algorithms. © 2023, The Authors. All rights reserved.

关键词： Optimization

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Minimal Length of Nontrivial Solutions of the Isometry Equation and MacWilliams Extension Property with Respect to Weighted Poset Metric

Minimal Length of Nontrivial Solutions of the Isometry Equat...

引用

IEEE International Symposium on information Theory

作者： Yang Xu Haibin Kan Guangyue Han Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Department of Mathematics The University of Hong Kong Shanghai Engineering Research Center of Blockchain Yiwu Research Institute of Fudan University

ISBN: (数字)9781665421591

ISBN: (纸本)9781665421607

For $R \triangleq Ma{t_m}({\mathbb{F}})$, the ring of all m × m matrices over the finite field ${\mathbb{F}}$ with $|{\mathbb{F}}| = q$, and the left R-module $A \triangleq Ma{t_{m,k}}({\mathbb{F}})$ with m + 1 ⩽ k, by deriving the minimal length of solutions of the related isometry equation, Dyshko has proved in [3], [4] that the minimal code length n for A n not satisfying the MacWilliams extension property (MEP) with respect to Hamming weight is equal to $\prod\nolimits_{i = 1}^m {\left( {{q^i} + 1} \right)}$. In this paper, using the Möbius functions, we derive the minimal length of nontrivial solutions of the isometry equation for a finite lattice. For the finite vector space ${\mathbf{H}} \triangleq \prod\nolimits_{i \in \Omega } {{{\mathbb{F}}^{{k_i}}}}$, a poset P = (Ω, ≼ P ) and a map ω: Ω → ℝ + give rise to the (P, ω)-weight on H, which has been proposed by Hyun, Kim and Park in [18]. For such a weight, we study the relations between the MEP and other properties including admitting MacWilliams identity, Fourier-reflexivity of involved partitions and the Unique Decomposition Property (UDP) defined for (P, ω). We give necessary and sufficient conditions for H to satisfy the MEP with the additional assumption that either P is hierarchical or ω is identically 1, i.e., (P, ω)-weight coincides with P-weight, which further allow us to partly answer a conjecture proposed by Machado and Firer in [22].

关键词： Measurement Sufficient conditions Codes Lattices Hamming weight Galois fields

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Comparison Model for Forecasting the Total Population in Bangkok

Comparison Model for Forecasting the Total Population in Ban...

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International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering (ECTI-NCON)

作者： Nattanicha Tipwong Anuchit Jitpattanakul Wikanda Phaphan Chalermrat Nontapa Sakorn Mekruksavanich Department of Applied Statistics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Statistics Faculty of Science Chiang Mai University Chiang Mai Thailand Research Group in Statistical Learning and Inference Department of Computer Engineering School of Information and Communication University of Phayao Phayao Thailand

The primary aim of this article is to investigate, contrast, and formulate a time series model to predict Bangkok’s overall population. In this study, we intend to propose a hybrid model that combines the Autoregressive Moving Integrated Average (ARIMA) model and machine learning techniques, utilizing a Multilayer Perceptron (MLP). Our data source comprises monthly data from 2002 to 2022, totaling 252 data points, obtained from the official registration statistics system of the Registration Administration Office, department of Provincial Administration. This dataset has been divided into two subsets: the first subset covers the years from 2002 to 2021, encompassing 240 data points, which will be used for constructing predictive models. The second subset consists of the data for the year 2022, comprising 12 data points, which will serve as a means to compare the accuracy of various forecasting methods. The evaluation criteria employed for this purpose are the minimum mean absolute error (MAE), the mean square error (MSE) and the mean absolute percentage error (MAPE). Furthermore, we will utilize the Python programming language to facilitate data analysis.

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Workflow Scheduling for Remote Sense Application Using Data Transformation Graph in Inter-cloud

Workflow Scheduling for Remote Sense Application Using Data ...

引用

2021 International Conference on Intelligent Computing, Automation and Applications, ICAA 2021

作者： Tian, Zhuojing Huang, Zhenchun Zhang, Yinong Beijing Union University Beijing Key Laboratory of Information Service Engineering Beijing100101 China Tsinghua University Department of Computer Science and Technology Beijing100084 China National Supercomputing Center in Wuxi Wuxi214072 China Beijing National Research Center for Information Science and Technology Beijing100084 China Beijing Union University School of Urban Rail Transit and Logistics Beijing100101 China

ISBN: (纸本)9781665437301

Inter-cloud environment provides massive computing resources to meet the increasing amount of data and computation for remote sensing applications. However, how to effectively map sub-Tasks to different cloud service providers to achieve QoS(Quality of Service) index optimization is still a problem that needs to be studied. Remote sensing applications need to process trillions of bytes of data each time, unreasonable scheduling will lead to a large amount of data transmission across the cloud, which will seriously affect the performance of QoS indicators such as makespan and cost. By using data transformation graph(DTG) to study the data transmission process of global drought detection application, which is a remote sensing application, an optimized scientific workflow scheduling based on genetic algorithm is proposed for inter-cloud environment, which can minimize makespan and cost simultaneously. Experimental results show that this method can significantly optimize QoS index for data-intensive application like remote sense application and can effectively reduce the impact of performance bottlenecks. © 2021 IEEE.

关键词： Quality of service

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