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While Diffusion Monte Carlo (DMC) is in principle an exact stochastic method for ab initio electronic structure calculations, in practice, the fermionic sign problem necessitates the use of the fixed-node approximation and trial wavefunctions with approximate nodes (or zeros). This approximation introduces a variational error in the energy that potentially can be tested and systematically improved. Here, we present a computational method that produces trial wavefunctions with systematically improvable nodes for DMC calculations of pe

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Recent progress in understanding phosphorothioate antisense oligonucleotide (PS-ASO) interactions with proteins has revealed that proteins play deterministic roles in the absorption, distribution, cellular uptake, subcellular distribution, molecular mechanisms of action, and toxicity of PS-ASOs. Similarly, such interactions can alter the fates of many intracellular proteins. These and other advances have opened new avenues for the medicinal chemistry of PS-ASOs and research on all elements of the molecular pharmacology of these mol

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Generative adversarial networks (GAN) are widely used in medical image analysis tasks, such as medical image segmentation and synthesis. In these works, adversarial learning is directly applied to the original supervised segmentation (synthesis) networks. The usage of adversarial learning is effective in improving visual perception performance since adversarial learning works as realistic regularization for supervised generators. However, the quantitative performance often cannot improve as much as the qualitative performance, and it c

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