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Predicting Substrate Reactivity in Oxidative Homocoupling of Phenols Using Positive and Unlabeled Machine Learning
Get PDFA positive and unlabeled machine learning (PU learning) model was trained to predict substrate reactivity in the oxidative homocoupling of phenols under different conditions. We demonstrated its effectiveness by conducting validation using two descriptor sets: 28-dimensional descriptors considered to influence reactivity and extended-connectivity fingerprints. We performed parameter tuning of the model using our experimental data and determined that the optimized parameters provided excellent prediction accuracy for the existing experimental data, regardless of the reaction conditions. Furthermore, the prediction results obtained using 30 types of unlabeled data matched the experimental results for approximately 83.3–86.7% of substrates, and the prediction accuracy of the PU learning model was shown to be superior to that of a model trained with both positive and negative reactivity data. Because negative data are not required to train a PU learning model, it can be applied to reactions reported in many previous studies, informing the cost-effective synthesis of molecules based on model-predicted results
VAE-GAN-Based Semantic Communication for High-Quality Image Transmission
Get PDFIn semantic communication systems, deep learningbased joint source-channel coding (DeepJSCC) has demonstrated superior performance, particularly in low signal-to-noise ratio (SNR) scenarios and under limited bandwidth conditions, compared to traditional communication technologies. However, most existing studies rely on communication models based on autoencoders, where the distribution of transmission symbols is treated as a complete opaque, causing inefficient use of limited transmission symbols. Moreover, many existing methods for image transmission focus on optimizing pixel-wise metrics, without considering the semantic information of the images. This pixellevel optimization often compromises the semantic fidelity and perceptual quality of the reconstructed images. To address these problems, we propose a novel semantic communication system that combines a variational autoencoder (VAE) and a generative adversarial network (GAN). Specifically, a VAE is used to arbitrarily control the distribution of transmission symbols according to channel conditions, while a GAN is used to maximize the similarity of semantic information. Simulation results demonstrate that the proposed method improves the perceptual quality of the reconstructed images compared to conventional approaches
Design, Implementation, and Evaluation of Collaborative Relationship Building in School Organizations
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