Divergent Radical Cyclization and Hydroaminoalkylation of N-Arylacrylamides Using Photoredox Catalysis.

The ability to selectively synthesize multiple products from the same sets of substrates is a highly appealing and challenging concept in synthetic chemistry. In this manuscript, we describe the visible-light photoredox intermolecular catalysis of N-arylacrylamides that are α-C-H functionalized with aryl tertiary amines. The photocatalyst acts as a chemical switch to trigger two different reaction pathways and to obtain two different products from the same starting material. Simple adjustments to the reaction conditions enable the divergent synthesis of the oxidative cyclizations or the addition products in good to high yields with excellent atom economy.

Umpolung Reactivity of Imine Ester: Visible-Light Mediated Transfer Hydrogenation of α-Aryl Imino Esters by Phenylsilane and Water.

A visible-light mediated chemoselective transfer hydrogenation of α-aryl imino esters was demonstrated. The methodology allowed the efficient and practical preparation of α-amino acid esters. The mechanism of the reaction was probed by DFT calculations, and deuteration experiments indicated deuterium was introduced into amino acid esters efficiently (up to 99 % D ratio), enabling a feasible way to obtain deuterated amino acids using D2 O as a cheap deuterium source.

Evaluating the Generalization Ability of Super-Resolution Networks.

Performance and generalization ability are two important aspects to evaluate the deep learning models. However, research on the generalization ability of Super-Resolution (SR) networks is currently absent. Assessing the generalization ability of deep models not only helps us to understand their intrinsic mechanisms, but also allows us to quantitatively measure their applicability boundaries, which is important for unrestricted real-world applications. To this end, we make the first attempt to propose a Generalization Assessment Index for SR networks, namely SRGA. SRGA exploits the statistical characteristics of the internal features of deep networks to measure the generalization ability. Specially, it is a non-parametric and non-learning metric. To better validate our method, we collect a patch-based image evaluation set (PIES) that includes both synthetic and real-world images, covering a wide range of degradations. With SRGA and PIES dataset, we benchmark existing SR models on the generalization ability. This work provides insights and tools for future research on model generalization in low-level vision.