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Hearing impairment (HI) disrupts social interaction by hindering the ability to follow conversations in noisy environments. While hearing aids (HAs) with noise reduction (NR) partially address this, the "cocktailparty problem" persists, where individuals struggle to attend to specific voices amidst background noise. This study investigated how NR and an advanced signal processing method for compensating for nonlinearities in EEG signals can improve neural speech processing in HI listeners.

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Simplified self-supervised learning for hybrid propagation graph-based recommendation.

Neural Netw

January 2025

School of Big Data & Software Engineering, Chongqing University, Chongqing, 401331, China. Electronic address:

Recent progress in Graph Convolutional Networks (GCNs) has facilitated their extensive application in recommendation, yielding notable performance gains. Nevertheless, existing GCN-based recommendation approaches are confronted with several challenges: (1) how to effectively leverage multi-order graph connectivity to derive meaningful node embeddings; (2) faced with sparse raw data, how to augment supervision signals without relying on auxiliary information; (3) given that GCNs necessitate the aggregation of neighborhood nodes, and the sparsity of these nodes can exacerbate the impact of noise data, how to mitigate the noise problem inherent in the raw data. For tackling aforementioned challenges, we devise a new hybrid propagation GCN-based method named S3HGN, incorporating a simplified self-supervised learning paradigm for recommendation.

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The electron-bombarded active pixel sensor (EBAPS) is a highly sensitive vacuum-solid hybrid low-light imaging device capable of functioning in ultra-low illumination environments as low as 10-4 lx. However, this high sensitivity also causes problems, such as a low signal-to-noise ratio and complex noise. To enhance the quality of low-light night vision images captured by EBAPS and achieve effective imaging in ultra-low illumination, this study proposes a noise reduction algorithm based on the noise characteristics of EBAPS images.

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Orbital angular momentum (OAM), with its unique orthogonality, is widely applied in optical holographic encryption and information storage. Theoretically, the topological charge of OAM holography is infinite. However, in practice, it is restricted by the Nyquist-Shannon sampling theorem and experimental equipment, resulting in a relatively small number of practically usable channels.

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Distributed acoustic sensing (DAS) is a technology that uses optical fiber as a sensing unit to detect external vibration signals. Due to the high resolution and high sensitivity of DAS, it has great application potential in the detection of vibration events. However, high detection performance will bring limitations to DAS in multi-source detection.

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