Advancing Hyperspectral and Multispectral Image Fusion: An Information-Aware Transformer-Based Unfolding Network.

In hyperspectral image (HSI) processing, the fusion of the high-resolution multispectral image (HR-MSI) and the low-resolution HSI (LR-HSI) on the same scene, known as MSI-HSI fusion, is a crucial step in obtaining the desired high-resolution HSI (HR-HSI). With the powerful representation ability, convolutional neural network (CNN)-based deep unfolding methods have demonstrated promising performances. However, limited receptive fields of CNN often lead to inaccurate long-range spatial features, and inherent input and output images for each stage in unfolding networks restrict the feature transmission, thus limiting the overall performance. To this end, we propose a novel and efficient information-aware transformer-based unfolding network (ITU-Net) to model the long-range dependencies and transfer more information across the stages. Specifically, we employ a customized transformer block to learn representations from both the spatial and frequency domains as well as avoid the quadratic complexity with respect to the input length. For spatial feature extractions, we develop an information transfer guided linearized attention (ITLA), which transmits high-throughput information between adjacent stages and extracts contextual features along the spatial dimension in linear complexity. Moreover, we introduce frequency domain learning in the feedforward network (FFN) to capture token variations of the image and narrow the frequency gap. Via integrating our proposed transformer blocks with the unfolding framework, our ITU-Net achieves state-of-the-art (SOTA) performance on both synthetic and real hyperspectral datasets.

Motion-aware Dynamic Graph Neural Network for Video Compressive Sensing.

Video snapshot compressive imaging (SCI) utilizes a 2D detector to capture sequential video frames and compress them into a single measurement. Various reconstruction methods have been developed to recover the high-speed video frames from the snapshot measurement. However, most existing reconstruction methods are incapable of efficiently capturing long-range spatial and temporal dependencies, which are critical for video processing. In this paper, we propose a flexible and robust approach based on the graph neural network (GNN) to efficiently model non-local interactions between pixels in space and time regardless of the distance. Specifically, we develop a motion-aware dynamic GNN for better video representation, i.e., represent each node as the aggregation of relative neighbors under the guidance of frame-by-frame motions, which consists of motion-aware dynamic sampling, cross-scale node sampling, global knowledge integration, and graph aggregation. Extensive results on both simulation and real data demonstrate both the effectiveness and efficiency of the proposed approach, and the visualization illustrates the intrinsic dynamic sampling operations of our proposed model for boosting the video SCI reconstruction results. The code and model will be released.

Pyrrolic Nitrogen Boosted H2 Generation from an Aqueous Solution of HCHO at Room Temperature by Metal-Free Carbon Catalysts.

Hydrogen production from organic hydrides represents a promising strategy for the development of safe and sustainable technologies for H2 storage and transportation. Nonetheless, the majority of existing procedures rely on noble metal catalysts and emit greenhouse gases such as CO2/CO. Herein, we demonstrated an alternative N-doped carbon (CN) catalyst for highly efficient and robust H2 production from an aqueous solution of formaldehyde (HCHO). Importantly, this process generated formic acid as a valuable byproduct instead of CO2/CO, enabling a clean H2 generation process with 100% atom economy. Mechanism investigations revealed that the pyrrolic N in the CN catalysts played a critical role in promoting H2 generation via enhancing the transformation of O2 to generate •OO- free radicals. Consequently, the optimized CN catalysts achieved a remarkable H2 generation rate of 13.6 mmol g-1 h-1 at 30 °C. This finding is anticipated to facilitate the development of liquid H2 storage and its large-scale utilization.

Recurrent Neural Networks for Snapshot Compressive Imaging.

Conventional high-speed and spectral imaging systems are expensive and they usually consume a significant amount of memory and bandwidth to save and transmit the high-dimensional data. By contrast, snapshot compressive imaging (SCI), where multiple sequential frames are coded by different masks and then summed to a single measurement, is a promising idea to use a 2-dimensional camera to capture 3-dimensional scenes. In this paper, we consider the reconstruction problem in SCI, i.e., recovering a series of scenes from a compressed measurement. Specifically, the measurement and modulation masks are fed into our proposed network, dubbed BIdirectional Recurrent Neural networks with Adversarial Training (BIRNAT) to reconstruct the desired frames. BIRNAT employs a deep convolutional neural network with residual blocks and self-attention to reconstruct the first frame, based on which a bidirectional recurrent neural network is utilized to sequentially reconstruct the following frames. Moreover, we build an extended BIRNAT-color algorithm for color videos aiming at joint reconstruction and demosaicing. Extensive results on both video and spectral, simulation and real data from three SCI cameras demonstrate the superior performance of BIRNAT.

Design, synthesis, anti-HIV evaluation and molecular modeling of piperidine-linked amino-triazine derivatives as potent non-nucleoside reverse transcriptase inhibitors.

A novel series of piperidine-linked amino-triazine derivatives were designed, synthesized and evaluated for in vitro anti-HIV activity as non-nucleoside reverse transcriptase inhibitors on the basis of our previous work. Screening results indicated that most compounds showed excellent activity against wild-type HIV-1 with EC(50) values in low nanomolar concentration range (especially compound 6b3, EC(50) = 4.61 nM, SI = 5945) and high activity against K103N/Y181C resistant mutant strain of HIV-1 with EC(50) values in low micromolar concentration range. In addition, preliminary structure-activity relationship and molecular modeling of these new analogs were detailed in this manuscript.