Multi-Agent Systems (MAS), which consist of multiple interacting agents, are crucial in Cyber-Physical Systems (CPS), because they improve system adaptability, efficiency, and robustness through parallel processing an...
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Multi-Agent Systems (MAS), which consist of multiple interacting agents, are crucial in Cyber-Physical Systems (CPS), because they improve system adaptability, efficiency, and robustness through parallel processing and collaboration. However, most existing unsupervised meta-learning methods are centralized and not suitable for multi-agent systems where data are distributed stored and inaccessible to all agents. Meta-GMVAE, based on variational autoencoder (VAE) and set-level variational inference, represents a sophisticated unsupervised meta-learning model that improves generative performance by efficiently learning data representations across various tasks, increasing adaptability and reducing sample requirements. Inspired by these advancements, we propose a novel Distributed Unsupervised Meta-Learning (DUML) framework based on Meta-GMVAE and a fusion strategy. Furthermore, we present a DUML algorithm based on Gaussian Mixture Model (DUMLGMM), where the parameters of the Gaussian-mixture is solved by an Expectation-Maximization algorithm. Simulations on Omniglot and MiniImageNet datasets show that DUMLGMM can achieve the performance of the corresponding centralized algorithm and outperform non-cooperative algorithm.
Molecular conformations generation (MCG) aims to efficiently obtain reasonable and stable three-dimensional (3D) atomic coordinates of the atoms in the molecule from scratch, providing a structural foundation for mole...
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Molecular conformations generation (MCG) aims to efficiently obtain reasonable and stable three-dimensional (3D) atomic coordinates of the atoms in the molecule from scratch, providing a structural foundation for molecular representation learning models and advanced downstream molecular design tasks such as molecular property prediction, molecular generation, and molecular docking. Existing MCG methods mostly rely on indirect distance-based strategies, which which can result in geometrically unrealistic conformations, or direct coordinate-based methods, which have larger search spaces and are prone to overfitting. Therefore, this study introduces Conf-GEM, a novel geometric information-assisted direct conformation generation model based on E-GeoGNN, a geometrically augmented 3D graph neural network with multiple scales. Pre-training and divide-and-conquer strategies, are integrated into the proposed model. Conf-GEM outperforms RDKit and nine deep-learning-based MCG models on the GEOM-QM9 and GEOM-Drugs datasets, achieving conformational coverage of 96.69% and 96.07%, respectively, without force field optimization. It also excels on the X-ray diffraction crystal structure dataset with up to 97.04% conformational coverage. In conclusion, Conf-GEM provides a novel solution for stabilizing 3D conformations generation. We provide an online prediction service ( https://*** ) with a user-friendly interface for researchers.
Collecting high-quality medical image data for machine learning applications remains a significant challenge due to data scarcity, privacy concerns, and high annotation costs. To address these issues, vision generativ...
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Collecting high-quality medical image data for machine learning applications remains a significant challenge due to data scarcity, privacy concerns, and high annotation costs. To address these issues, vision generative models, particularly Latent Diffusion Models (LDMs), have emerged as state-of-the-art solutions that reduce computational demands while maintaining superior performance in data generation tasks. In this study, we propose an enhanced LDM-based approach that integrates separable self-attention mechanisms within the diffusion process, positioned after residual blocks, to improve the capture of detailed features and maintain spatial consistency. This modification reduces memory usage by 82.94% and decreases the Fréchet Inception Distance (FID) by 25.01% compared to traditional self-attention models, all while preserving image quality. Our method addresses critical challenges such as data scarcity and computational efficiency in medical imaging by combining variational autoencoders (VAEs) for latent space mapping with U-Net for noise prediction. Evaluations on five datasets — PneumoniaMNIST, BloodMNIST, ChestMNIST, Dental4k, and HandMNIST — demonstrate significant improvements in computational efficiency, memory usage, and the quality of generated images, showcasing the potential of our approach for scalable and effective medical image synthesis.
We built a workflow for the fabrication analysis of thin films by applying machine-learning (ML) techniques directly to the measurement data. This will lower the problem in cost of synthesizing and analyzing samples t...
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We built a workflow for the fabrication analysis of thin films by applying machine-learning (ML) techniques directly to the measurement data. This will lower the problem in cost of synthesizing and analyzing samples to improve the fabrication conditions. The workflow combines two ML techniques: non-negative matrix factorization (NMF) and variational autoencoder (VAE). The measurement data were two-dimensional X-ray diffraction of indium-gallium oxide system thin films. The thin films were fabricated by physical vapor techniques under multiple conditions. First, the workflow was applied to the data of the thin films fabricated through pulsed laser deposition as a proof of concept. We found that our workflow extracted features that represented crystallinity differences in addition to substrate differences. Second, VAE was analyzed to determine whether it could generate new data from its latent space. The latent space of the VAE, which learned the extracted features, represented the relationship between the fabrication conditions such as laser intensities and crystallinity. Third, the inference ability of the new data fabricated through sputtering was evaluated. The capability of the workflow we confirmed will support researchers in improving fabrication conditions by visually comparing various fabricated samples.
Predicting movie ratings very precisely has become a vital aspect of personalized recommendation systems, which requires robust and high-performing models. for evaluating the effectiveness in predicting movie ratings,...
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