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1. Noise schemas aid hearing in noise

   https://doi.org/10.1073/pnas.2408995121  

   Hicks, Jarrod_M; McDermott, Josh_H (November 2024, Proceedings of the National Academy of Sciences)

   Human hearing is robust to noise, but the basis of this robustness is poorly understood. Several lines of evidence are consistent with the idea that the auditory system adapts to sound components that are stable over time, potentially achieving noise robustness by suppressing noise-like signals. Yet background noise often provides behaviorally relevant information about the environment and thus seems unlikely to be completely discarded by the auditory system. Motivated by this observation, we explored whether noise robustness might instead be mediated by internal models of noise structure that could facilitate the separation of background noise from other sounds. We found that detection, recognition, and localization in real-world background noise were better for foreground sounds positioned later in a noise excerpt, with performance improving over the initial second of exposure to a noise. These results are consistent with both adaptation-based and model-based accounts (adaptation increases over time and online noise estimation should benefit from acquiring more samples). However, performance was also robust to interruptions in the background noise and was enhanced for intermittently recurring backgrounds, neither of which would be expected from known forms of adaptation. Additionally, the performance benefit observed for foreground sounds occurring later within a noise excerpt was reduced for recurring noises, suggesting that a noise representation is built up during exposure to a new background noise and then maintained in memory. These findings suggest that noise robustness is supported by internal models—“noise schemas”—that are rapidly estimated, stored over time, and used to estimate other concurrent sounds. 

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2. Phase Noise Analysis of Clock Generator by Using Phase Noise Sensitivity

   https://doi.org/10.1109/TSIPI.2022.3222747  

   Liu, Yuanzhuo; Guo, Yuandong; Li, Chaofeng; Bai, Siqi; Chen, Bichen; Venkataraman, Srinivas; Wang, Xu; Fan, Jun; Kim, DongHyun (January 2022, IEEE Transactions on Signal and Power Integrity)
3. Stabilizing Machine Learning Prediction of Dynamics: Noise and Noise-inspired Regularization

   Wikner, Alexander; Harvey, Joseph; Girvan, Michelle; Hunt, Brian R.; Pomerance, Andrew; Antonsen, Thomas; Ott, Edward (December 2022, arXivorg)

   Recent work has shown that machine learning (ML) models can be trained to accurately forecast the dynamics of unknown chaotic dynamical systems. Short-term predictions of the state evolution and long-term predictions of the statistical patterns of the dynamics (``climate'') can be produced by employing a feedback loop, whereby the model is trained to predict forward one time step, then the model output is used as input for multiple time steps. In the absence of mitigating techniques, however, this technique can result in artificially rapid error growth. In this article, we systematically examine the technique of adding noise to the ML model input during training to promote stability and improve prediction accuracy. Furthermore, we introduce Linearized Multi-Noise Training (LMNT), a regularization technique that deterministically approximates the effect of many small, independent noise realizations added to the model input during training. Our case study uses reservoir computing, a machine-learning method using recurrent neural networks, to predict the spatiotemporal chaotic Kuramoto-Sivashinsky equation. We find that reservoir computers trained with noise or with LMNT produce climate predictions that appear to be indefinitely stable and have a climate very similar to the true system, while reservoir computers trained without regularization are unstable. Compared with other regularization techniques that yield stability in some cases, we find that both short-term and climate predictions from reservoir computers trained with noise or with LMNT are substantially more accurate. Finally, we show that the deterministic aspect of our LMNT regularization facilitates fast hyperparameter tuning when compared to training with noise. 

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4. Modeling of Power Distrubition Network (PDN) Noise Coupling Induced Clock Phase Noise

   https://doi.org/10.1109/EMCSIPI49824.2024.10705606  

   Peng, Zhekun; Park, Junyong; Li, Chaofeng; Stecher, Joey; Venkataraman, Srinivas; Wang, Xu; Rangaswamy, Granthana; Kim, DongHyun (August 2024, IEEE)
5. Current Noise of Hydrodynamic Electrons

   https://doi.org/10.1103/PhysRevLett.130.256301  

   Hui, Aaron; Skinner, Brian (June 2023, Physical Review Letters)