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1. Learning in the Frequency Domain

   https://doi.org/10.1109/CVPR42600.2020.00181  

   Xu, Kai; Qin, Minghai; Sun, Fei; Wang, Yuhao; Chen, Yen-Kuang; Ren, Fengbo (January 2020, IEEE Conference on Computer Vision and Pattern Recognition)

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   Deep neural networks have achieved remarkable success in computer vision tasks. Existing neural networks mainly operate in the spatial domain with fixed input sizes. For practical applications, images are usually large and have to be downsampled to the predetermined input size of neural networks. Even though the downsampling operations reduce computation and the required communication bandwidth, it removes both redundant and salient information obliviously, which results in accuracy degradation. Inspired by digital signal processing theories, we analyze the spectral bias from the frequency perspective and propose a learning-based frequency selection method to identify the trivial frequency components which can be removed without accuracy loss. The proposed method of learning in the frequency domain leverages identical structures of the well-known neural networks, such as ResNet-50, MobileNetV2, and Mask R-CNN, while accepting the frequency-domain information as the input. Experiment results show that learning in the frequency domain with static channel selection can achieve higher accuracy than the conventional spatial downsampling approach and meanwhile further reduce the input data size. Specifically for ImageNet classification with the same input size, the proposed method achieves 1.60% and 0.63% top-1 accuracy improvements on ResNet-50 and MobileNetV2, respectively. Even with half input size, the proposed method still improves the top-1 accuracy on ResNet-50 by 1.42%. In addition, we observe a 0.8% average precision improvement on Mask R-CNN for instance segmentation on the COCO dataset. 

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2. Domain-indexing variational Bayes: Interpretable domain index for domain adaptation

   Xu, Zihao; Hao, Guang-Yuan; He, Hao; Wang, Hao (April 2023, Eleventh International Conference on Learning Representations (ICLR))
3. On the Robustness of Domain Constraints

   https://doi.org/10.1145/3460120.3484570  

   Sheatsley, Ryan; Hoak, Blaine; Pauley, Eric; Beugin, Yohan; Weisman, Michael J.; McDaniel, Patrick (November 2021, The ACM Conference on Computer and Communications Security)
4. The marionette mechanism of domain–domain communication in the antagonist, agonist, and coactivator responses of the estrogen receptor

   https://doi.org/10.1073/pnas.2216906120  

   Chen, Xun; Jin, Shikai; Chen, Mingchen; Bueno, Carlos; Wolynes, Peter G. (February 2023, Proceedings of the National Academy of Sciences)

   The human estrogen receptor α (hER α ) is involved in the regulation of growth, development, and tissue homeostasis. Agonists that bind to the receptor’s ligand-binding domain (LBD) lead to recruitment of coactivators and the enhancement of gene expression. In contrast, antagonists bind to the LBD and block the binding of coactivators thus decreasing gene expressions. In this work, we carry out simulations using the AWSEM (Associative memory, Water mediated, Structure and Energy Model)-Suite force field along with the 3SPN.2C force field for DNA to predict the structure of hER α and study its dynamics when binding to DNA and coactivators. Using simulations of antagonist-bound hER α and agonist-bound hER α by themselves and also along with bound DNA and coactivators, principal component analyses and free energy landscape analyses capture the pathway of domain–domain communication for agonist-bound hER α . This communication is mediated through the hinge domains that are ordinarily intrinsically disordered. These disordered segments manipulate the hinge domains much like the strings of a marionette as they twist in different ways when antagonists or agonists are bound to the ligand-binding domain. 

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5. Intrinsically Disordered Regions of the DNA-Binding Domain of Human FoxP1 Facilitate Domain Swapping

   https://doi.org/10.1016/j.jmb.2020.07.017  

   Medina, Exequiel; Villalobos, Pablo; Hamilton, George L.; Komives, Elizabeth A.; Sanabria, Hugo; Ramírez-Sarmiento, César A.; Babul, Jorge (September 2020, Journal of Molecular Biology)

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