Photoswitching neutral homoaromatic hydrocarbons.

Homoaromatic compounds possess an interrupted π system but display aromatic properties due to through-space or through-bond interactions. Stable neutral homoaromatic hydrocarbons have remained rare and are typically unstable. Here we present the preparation of a class of stable neutral homoaromatic molecules, supported by experimental evidence (ring current observed by NMR spectroscopy and equalization of bond lengths by X-ray structure analysis) and computational analysis via nucleus-independent chemical shifts (NICS) and anisotropy of the induced current density (ACID). We also show that one homoaromatic hydrocarbon is a photoswitch through a reversible photochemical [1, 11] sigmatropic rearrangement. Our computational analysis suggests that, upon photoswitching, the nature of the homoaromatic state changes in its perimeter from a more pronounced local 6π homoaromatic state to a global 10π homoaromatic state. These demonstrations of stable and accessible homoaromatic neutral hydrocarbons and their photoswitching behaviour provide new understanding and insights into the study of homoconjugative interactions in organic molecules, and for the design of new responsive molecular materials.

On Data Augmentation for GAN Training.

Recent successes in Generative Adversarial Networks (GAN) have affirmed the importance of using more data in GAN training. Yet it is expensive to collect data in many domains such as medical applications. Data Augmentation (DA) has been applied in these applications. In this work, we first argue that the classical DA approach could mislead the generator to learn the distribution of the augmented data, which could be different from that of the original data. We then propose a principled framework, termed Data Augmentation Optimized for GAN (DAG), to enable the use of augmented data in GAN training to improve the learning of the original distribution. We provide theoretical analysis to show that using our proposed DAG aligns with the original GAN in minimizing the Jensen-Shannon (JS) divergence between the original distribution and model distribution. Importantly, the proposed DAG effectively leverages the augmented data to improve the learning of discriminator and generator. We conduct experiments to apply DAG to different GAN models: unconditional GAN, conditional GAN, self-supervised GAN and CycleGAN using datasets of natural images and medical images. The results show that DAG achieves consistent and considerable improvements across these models. Furthermore, when DAG is used in some GAN models, the system establishes state-of-the-art Fréchet Inception Distance (FID) scores. Our code is available (https://github.com/tntrung/dag-gans).

Immobilization of an Iridium Pincer Complex in a Microporous Polymer for Application in Room-Temperature Gas Phase Catalysis.

An iridium dihydride pincer complex [IrH2 (POCOP)] is immobilized in a hydroxy-functionalized microporous polymer network using the concepts of surface organometallic chemistry. The introduction of this novel, truly innocent support with remote OH-groups enables the formation of isolated active metal sites embedded in a chemically robust and highly inert environment. The catalyst maintained high porosity and without prior activation exhibited efficacy in the gas phase hydrogenation of ethene and propene at room temperature and low pressure. The catalyst can be recycled for at least four times.

A Simple Nickel Catalyst Enabling an E-Selective Alkyne Semihydrogenation.

Stereoselective alkyne semihydrogenations are attractive approaches to alkenes, which are key building blocks for synthesis. With regards to the most atom-economic reducing agent dihydrogen (H2 ), only few catalysts for the challenging E-selective alkyne semihydrogenation have been disclosed, each with a unique substrate scope profile. Here, we show that a commercially available nickel catalyst facilitates the E-selective alkyne semihydrogenation of a wide variety of substituted internal alkynes. This results in a simple and broadly applicable overall protocol to stereoselectively access E-alkenes employing H2 , which could serve as a general method for synthesis.

On-Device Scalable Image-Based Localization via Prioritized Cascade Search and Fast One-Many RANSAC.

We present the design of an entire on-device system for large-scale urban localization using images. The proposed design integrates compact image retrieval and 2D-3D correspondence search to estimate the location in extensive city regions. Our design is GPS agnostic and does not require network connection. In order to overcome the resource constraints of mobile devices, we propose a system design that leverages the scalability advantage of image retrieval and accuracy of 3D model-based localization. Furthermore, we propose a new hashing-based cascade search for fast computation of 2D-3D correspondences. In addition, we propose a new one-many RANSAC for accurate pose estimation. The new one-many RANSAC addresses the challenge of repetitive building structures (e.g. windows and balconies) in urban localization. Extensive experiments demonstrate that our 2D-3D correspondence search achieves the state-of-the-art localization accuracy on multiple benchmark datasets. Furthermore, our experiments on a large Google street view image dataset show the potential of large-scale localization entirely on a typical mobile device.