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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. Mean Field Games with common noise and degenerate idiosyncratic noise.

   Cardaliaguet, Pierre; Seeger, Benjamin; Souganidis, Panagiotis (January 2025, The Annals of applied probability)
3. Mean field games with common noise and degenerate idiosyncratic noise

   https://doi.org/10.1214/25-AAP2148  

   Cardaliaguet, Pierre; Seeger, Benjamin; Souganidis, Panagiotis (June 2025, The Annals of Applied Probability)
4. Magnetic monopole noise

   https://doi.org/10.1038/s41586-019-1358-1  

   Dusad, Ritika; Kirschner, Franziska K.; Hoke, Jesse C.; Roberts, Benjamin R.; Eyal, Anna; Flicker, Felix; Luke, Graeme M.; Blundell, Stephen J.; Davis, J. C. (July 2019, Nature)
5. QuantumNAT: quantum noise-aware training with noise injection, quantization and normalization

   https://doi.org/10.1145/3489517.3530400  

   Wang, Hanrui; Gu, Jiaqi; Ding, Yongshan; Li, Zirui; Chong, Frederic T.; Pan, David Z.; Han, Song (July 2022, In Proceedings of the 59th ACM/IEEE Design Automation Conference)

   Parameterized Quantum Circuits (PQC) are promising towards quantum advantage on near-term quantum hardware. However, due to the large quantum noises (errors), the performance of PQC models has a severe degradation on real quantum devices. Take Quantum Neural Network (QNN) as an example, the accuracy gap between noise-free simulation and noisy results on IBMQ-Yorktown for MNIST-4 classification is over 60%. Existing noise mitigation methods are general ones without leveraging unique characteristics of PQC; on the other hand, existing PQC work does not consider noise effect. To this end, we present QuantumNAT, a PQC-specific framework to perform noise-aware optimizations in both training and inference stages to improve robustness. We experimentally observe that the effect of quantum noise to PQC measurement outcome is a linear map from noise-free outcome with a scaling and a shift factor. Motivated by that, we propose post-measurement normalization to mitigate the feature distribution differences between noise-free and noisy scenarios. Furthermore, to improve the robustness against noise, we propose noise injection to the training process by inserting quantum error gates to PQC according to realistic noise models of quantum hardware. Finally, post-measurement quantization is introduced to quantize the measurement outcomes to discrete values, achieving the denoising effect. Extensive experiments on 8 classification tasks using 6 quantum devices demonstrate that QuantumNAT improves accuracy by up to 43%, and achieves over 94% 2-class, 80% 4-class, and 34% 10-class classification accuracy measured on real quantum computers. The code for construction and noise-aware training of PQC is available in the TorchQuantum library. 

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