MR-GNN: Multi-Resolution and Dual Graph Neural Network for Predicting Structured Entity Interactions

Predicting interactions between structured entities lies at the core of numerous tasks such as drug regimen and new material design. In recent years, graph neural networks have become attractive. They represent structured entities as graphs and then extract features from each individual graph using graph convolution operations. However, these methods have some limitations: i) their networks only extract features from a fix-sized subgraph structure (i.e., a fix-sized receptive field) of each node, and ignore features in substructures of different sizes, and ii) features are extracted by considering each entity independently, which may not effectively reflect the interaction between two entities. To resolve these problems, we present MR-GNN, an end-to-end graph neural network with the following features: i) it uses a multi-resolution based architecture to extract node features from different neighborhoods of each node, and, ii) it uses dual graph-state long short-term memory networks (L-STMs) to summarize local features of each graph and extracts the interaction features between pairwise graphs. Experiments conducted on real-world datasets show that MR-GNN improves the prediction of state-of-the-art methods.Comment: Accepted by IJCAI 201

Self-Calibrated Cross Attention Network for Few-Shot Segmentation

The key to the success of few-shot segmentation (FSS) lies in how to effectively utilize support samples. Most solutions compress support foreground (FG) features into prototypes, but lose some spatial details. Instead, others use cross attention to fuse query features with uncompressed support FG. Query FG could be fused with support FG, however, query background (BG) cannot find matched BG features in support FG, yet inevitably integrates dissimilar features. Besides, as both query FG and BG are combined with support FG, they get entangled, thereby leading to ineffective segmentation. To cope with these issues, we design a self-calibrated cross attention (SCCA) block. For efficient patch-based attention, query and support features are firstly split into patches. Then, we design a patch alignment module to align each query patch with its most similar support patch for better cross attention. Specifically, SCCA takes a query patch as Q, and groups the patches from the same query image and the aligned patches from the support image as K&V. In this way, the query BG features are fused with matched BG features (from query patches), and thus the aforementioned issues will be mitigated. Moreover, when calculating SCCA, we design a scaled-cosine mechanism to better utilize the support features for similarity calculation. Extensive experiments conducted on PASCAL-5^i and COCO-20^i demonstrate the superiority of our model, e.g., the mIoU score under 5-shot setting on COCO-20^i is 5.6%+ better than previous state-of-the-arts. The code is available at https://github.com/Sam1224/SCCAN.Comment: This paper is accepted by ICCV'2

Metamaterial absorber integrated microfluidic terahertz sensors

Spatial overlap between the electromagnetic fields and the analytes is a key factor for strong light-matter interaction leading to high sensitivity for label-free refractive index sensing. Usually, the overlap and therefore the sensitivity are limited by either the localized near field of plasmonic antennas or the decayed resonant mode outside the cavity applied to monitor the refractive index variation. In this paper, by constructing a metal microstructure array-dielectric-metal (MDM) structure, a novel metamaterial absorber integrated microfluidic (MAIM) sensor is proposed and demonstrated in terahertz (THz) range, where the dielectric layer of the MDM structure is hollow and acts as the microfluidic channel. Tuning the electromagnetic parameters of metamaterial absorber, greatly confined electromagnetic fields can be obtained in the channel resulting in significantly enhanced interaction between the analytes and the THz wave. A high sensitivity of 3.5 THz/RIU is predicted. The experimental results of devices working around 1 THz agree with the simulation ones well. The proposed idea to integrate metamaterial and microfluid with a large light-matter interaction can be extended to other frequency regions and has promising applications in matter detection and biosensing

Feature Representation Learning with Adaptive Displacement Generation and Transformer Fusion for Micro-Expression Recognition

Micro-expressions are spontaneous, rapid and subtle facial movements that can neither be forged nor suppressed. They are very important nonverbal communication clues, but are transient and of low intensity thus difficult to recognize. Recently deep learning based methods have been developed for micro-expression (ME) recognition using feature extraction and fusion techniques, however, targeted feature learning and efficient feature fusion still lack further study according to the ME characteristics. To address these issues, we propose a novel framework Feature Representation Learning with adaptive Displacement Generation and Transformer fusion (FRL-DGT), in which a convolutional Displacement Generation Module (DGM) with self-supervised learning is used to extract dynamic features from onset/apex frames targeted to the subsequent ME recognition task, and a well-designed Transformer Fusion mechanism composed of three Transformer-based fusion modules (local, global fusions based on AU regions and full-face fusion) is applied to extract the multi-level informative features after DGM for the final ME prediction. The extensive experiments with solid leave-one-subject-out (LOSO) evaluation results have demonstrated the superiority of our proposed FRL-DGT to state-of-the-art methods

Multistaged discharge constructing heterostructure with enhanced solid-solution behavior for long-life lithium-oxygen batteries.

Inferior charge transport in insulating and bulk discharge products is one of the main factors resulting in poor cycling stability of lithium-oxygen batteries with high overpotential and large capacity decay. Here we report a two-step oxygen reduction approach by pre-depositing a potassium carbonate layer on the cathode surface in a potassium-oxygen battery to direct the growth of defective film-like discharge products in the successive cycling of lithium-oxygen batteries. The formation of defective film with improved charge transport and large contact area with a catalyst plays a critical role in the facile decomposition of discharge products and the sustained stability of the battery. Multistaged discharge constructing lithium peroxide-based heterostructure with band discontinuities and a relatively low lithium diffusion barrier may be responsible for the growth of defective film-like discharge products. This strategy offers a promising route for future development of cathode catalysts that can be used to extend the cycling life of lithium-oxygen batteries

In vitro cultivation of rat bone marrow mesenchymal stem cells and establishment of pEGFP/Ang-1 transfection method

ABSTRACTObjectiveTo obtain the bone marrow mesenchymal stem cells (BMSCs), complete phenotypic identification and successfully transfect rat BMSCs by recombinant plasmid pEGFP/Ang-1.MethodsBMSCs were isolated from bone marrow using density gradient centrifugation method and adherence screening method, and purified. Then the recombinant plasmid pEGFP/Ang-1 was used to transfect BMSCs and the positive clones were obtained by the screen of G418 and observed under light microscopy inversely. Green fluorescent exhibited by protein was enhanced to measure the change time of the expression amount of Ang-1.ResultsBMSCs cell lines were obtained successfully by adherence screening method and density gradient centrifugation. Ang-1 recombinant plasmid was transfected smoothly into rat BMSCs, which can express Ang-1 for 3 d and decreased after 7 d.ConclusionsAdherence screening method and density gradient centrifugation can be effective methods to obtain BMSCs with high purity and rapid proliferation. Besides, the expression of transfected recombinant plasmid pEGFP/Ang-1 in rat BMSCs is satisfactory

In-situ cosmogenic ³⁶Cl denudation rates of carbonates in Guizhou karst area

This study quantifies surface denudation of carbonate rocks by the first application of in-situ cosmogenic <sup>36</sup>Cl in China. Concentrations of natural Cl and in-situ cosmogenic <sup>36</sup>Cl in bare carbonates from Guizhou karst areas were measured with isotope dilution by accelerator mass spectrometer. The Cl concentration varied from 16 to 206 ppm. The <sup>36</sup>Cl concentrations were in range of (0.8–2.4)×106 atom g−1, resulting in total denudation rates of 20–50 mm ka−1 that averaged over a 104–105 a timescale. The <sup>36</sup>Cl-denudation rates showed roughly a negative correlation with the local mean temperature. This preliminary observation may suggest the variations of proportions of chemical weathering and physical erosion in denudation process, depending upon local climatic conditions

Rapid determination of trace Cu 2+ by an in-syringe membrane SPE and membrane solid-phase spectral technique

A new in-syringe membrane SPE and solid-phase visible spectral method was proposed for the rapid extraction and visible spectral determination of trace Cu2+. The chelation and membrane SPE can be accomplished in a syringe. The yellow Cu(DDTC)2 complex was separated using a polyethersulfone membrane from the sample solution. Then, the complex can be detected directly on the polyethersulfone membrane utilizing solid-phase visible absorbance spectra without elution. The proposed method simplified the experimental procedure and improved the sensitivity to the μg L-1 level. Furthermore, this method is environmentally friendly since it avoids the use of organic solvents. After the investigation of the influence of different variables on the membrane SPE procedure, water and blood plasma were analyzed to validate the proposed method. A LOD of 0.04 μg L-1 and recoveries of 96.0-103.7% confirmed that the present work can be applied for the determination of trace Cu2+ in water and blood plasma samples