Higher-order Graph Attention Network for Stock Selection with Joint Analysis

Stock selection is important for investors to construct profitable portfolios. Graph neural networks (GNNs) are increasingly attracting researchers for stock prediction due to their strong ability of relation modelling and generalisation. However, the existing GNN methods only focus on simple pairwise stock relation and do not capture complex higher-order structures modelling relations more than two nodes. In addition, they only consider factors of technical analysis and overlook factors of fundamental analysis that can affect the stock trend significantly. Motivated by them, we propose higher-order graph attention network with joint analysis (H-GAT). H-GAT is able to capture higher-order structures and jointly incorporate factors of fundamental analysis with factors of technical analysis. Specifically, the sequential layer of H-GAT take both types of factors as the input of a long-short term memory model. The relation embedding layer of H-GAT constructs a higher-order graph and learn node embedding with GAT. We then predict the ranks of stock return. Extensive experiments demonstrate the superiority of our H-GAT method on the profitability test and Sharp ratio over both NSDAQ and NYSE datasetsComment: 12 pages, 6 figures

Rapid glycation with D-ribose induces globular amyloid-like aggregations of BSA with high cytotoxicity to SH-SY5Y cells

<p>Abstract</p> <p>Background</p> <p>D-ribose in cells and human serum participates in glycation of proteins resulting in advanced glycation end products (AGEs) that affect cell metabolism and induce cell death. However, the mechanism by which D-ribose-glycated proteins induce cell death is still unclear.</p> <p>Results</p> <p>Here, we incubated D-ribose with bovine serum albumin (BSA) and observed changes in the intensity of fluorescence at 410 nm and 425 nm to monitor the formation of D-ribose-glycated BSA. Comparing glycation of BSA with xylose (a control for furanose), glucose and fructose (controls for pyranose), the rate of glycation with D-ribose was the most rapid. Protein intrinsic fluorescence (335 nm), Nitroblue tetrazolium (NBT) assays and Western blotting with anti-AGEs showed that glycation of BSA incubated with D-ribose occurred faster than for the other reducing sugars. Protein intrinsic fluorescence showed marked conformational changes when BSA was incubated with D-ribose. Importantly, observations with atomic force microscopy showed that D-ribose-glycated BSA appeared in globular polymers. Furthermore, a fluorescent assay with Thioflavin T (ThT) showed a remarkable increase in fluorescence at 485 nm in the presence of D-ribose-glycated BSA. However, ThT fluorescence did not show the same marked increase in the presence of xylose or glucose. This suggests that glycation with D-ribose induced BSA to aggregate into globular amyloid-like deposits. As observed by Hoechst 33258 staining, 3-(4, 5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) and cell counting kit-8 (CCK-8) assay, lactate dehydrogenase (LDH) activity assay, flow cytometry using Annexin V and Propidium Iodide staining and reactive oxygen species (ROS) measurements, the amyloid-like aggregation of glycated BSA induced apoptosis in the neurotypic cell line SH-SY5Y.</p> <p>Conclusion</p> <p>Glycation with D-ribose induces BSA to misfold rapidly and form globular amyloid-like aggregations which play an important role in cytotoxicity to neural cells.</p

Depth Restoration in Under-Display Time-of-Flight Imaging

Under-display imaging has recently received considerable attention in both academia and industry. As a variation of this technique, under-display ToF (UD-ToF) cameras enable depth sensing for full-screen devices. However, it also brings problems of image blurring, signal-to-noise ratio and ranging accuracy reduction. To address these issues, we propose a cascaded deep network to improve the quality of UD-ToF depth maps. The network comprises two subnets, with the first using a complex-valued network in raw domain to perform denoising, deblurring and raw measurements enhancement jointly, while the second refining depth maps in depth domain based on the proposed multi-scale depth enhancement block (MSDEB). To enable training, we establish a data acquisition device and construct a real UD-ToF dataset by collecting real paired ToF raw data. Besides, we also build a large-scale synthetic UD-ToF dataset through noise analysis. The quantitative and qualitative evaluation results on public datasets and ours demonstrate that the presented network outperforms state-of-the-art algorithms and can further promote full-screen devices in practical applications

Highly active nickel–cobalt/nanocarbon thin films as efficient water splitting electrodes

Developing low cost, highly active and stable electrocatalysts for both the hydrogen evolution reaction (HER) and the oxygen evolution reaction (OER) using the same electrolyte has remained a major challenge. Herein, we report a novel and robust material comprised of Nickel-Cobalt nanoparticles coated on a porous nitrogen-doped carbon (NC) thin film synthesized via a two-step pulsed laser deposition technique. The optimized sample (Ni0.5Co0.5/NC) achieved lowest overpotentials of 176 mV and 300 mV at a current density of 10 mAcm-2 for HER and OER, respectively. The optimized OER activity might be attributed to the available metal oxide nanoparticles with effective electronic structure configuration and enhanced mass/charge transport capability. At the same time, the porous nitrogen doped carbon incorporated with cobalt and nickel species can serve as an excellent HER catalyst. As a result, the newly developed electrocatalysts manifest high current densities and strong electrochemical stability in overall water splitting, outperforming most of the previously reported non-precious metal-based catalysts

Depth Super-Resolution from Explicit and Implicit High-Frequency Features

We propose a novel multi-stage depth super-resolution network, which progressively reconstructs high-resolution depth maps from explicit and implicit high-frequency features. The former are extracted by an efficient transformer processing both local and global contexts, while the latter are obtained by projecting color images into the frequency domain. Both are combined together with depth features by means of a fusion strategy within a multi-stage and multi-scale framework. Experiments on the main benchmarks, such as NYUv2, Middlebury, DIML and RGBDD, show that our approach outperforms existing methods by a large margin (~20% on NYUv2 and DIML against the contemporary work DADA, with 16x upsampling), establishing a new state-of-the-art in the guided depth super-resolution task