Hypergraph Convolutional Network based Weakly Supervised Point Cloud Semantic Segmentation with Scene-Level Annotations

Point cloud segmentation with scene-level annotations is a promising but challenging task. Currently, the most popular way is to employ the class activation map (CAM) to locate discriminative regions and then generate point-level pseudo labels from scene-level annotations. However, these methods always suffer from the point imbalance among categories, as well as the sparse and incomplete supervision from CAM. In this paper, we propose a novel weighted hypergraph convolutional network-based method, called WHCN, to confront the challenges of learning point-wise labels from scene-level annotations. Firstly, in order to simultaneously overcome the point imbalance among different categories and reduce the model complexity, superpoints of a training point cloud are generated by exploiting the geometrically homogeneous partition. Then, a hypergraph is constructed based on the high-confidence superpoint-level seeds which are converted from scene-level annotations. Secondly, the WHCN takes the hypergraph as input and learns to predict high-precision point-level pseudo labels by label propagation. Besides the backbone network consisting of spectral hypergraph convolution blocks, a hyperedge attention module is learned to adjust the weights of hyperedges in the WHCN. Finally, a segmentation network is trained by these pseudo point cloud labels. We comprehensively conduct experiments on the ScanNet and S3DIS segmentation datasets. Experimental results demonstrate that the proposed WHCN is effective to predict the point labels with scene annotations, and yields state-of-the-art results in the community. The source code is available at http://zhiyongsu.github.io/Project/WHCN.html

Socio-Economic Management Theory Related to BPM: A Case Study of Dysfunctions in Digital Transformation Strategy

This research claims that dynamic strategies demanded by today’s digital environment exacerbate inconsistency between an organization’s digital transformation efforts and its enterprise architecture (EA) planning process. This phenomenon leads to redundant investments, delayed implementation, and frequent failures in digital transformation projects. In order to investigate this inconsistency, we apply the socioeconomic approach to management (SEAM) theory. Through critical analysis of four case studies in a large manufacturing organization, we clarify the relationship between digital transformation and EA and reveal the dysfunction in strategic implementation from a SEAM and business process management (BPM) perspective. In practice, this research integrates digital transformation and EA to provide a context-specific approach for planning and designing enterprise digital transformation strategies

RXFOOD: Plug-in RGB-X Fusion for Object of Interest Detection

The emergence of different sensors (Near-Infrared, Depth, etc.) is a remedy for the limited application scenarios of traditional RGB camera. The RGB-X tasks, which rely on RGB input and another type of data input to resolve specific problems, have become a popular research topic in multimedia. A crucial part in two-branch RGB-X deep neural networks is how to fuse information across modalities. Given the tremendous information inside RGB-X networks, previous works typically apply naive fusion (e.g., average or max fusion) or only focus on the feature fusion at the same scale(s). While in this paper, we propose a novel method called RXFOOD for the fusion of features across different scales within the same modality branch and from different modality branches simultaneously in a unified attention mechanism. An Energy Exchange Module is designed for the interaction of each feature map's energy matrix, who reflects the inter-relationship of different positions and different channels inside a feature map. The RXFOOD method can be easily incorporated to any dual-branch encoder-decoder network as a plug-in module, and help the original backbone network better focus on important positions and channels for object of interest detection. Experimental results on RGB-NIR salient object detection, RGB-D salient object detection, and RGBFrequency image manipulation detection demonstrate the clear effectiveness of the proposed RXFOOD.Comment: 10 page

Emodin targets the β-hydroxyacyl-acyl carrier protein dehydratase from Helicobacter pylori: enzymatic inhibition assay with crystal structural and thermodynamic characterization

<p>Abstract</p> <p>Background</p> <p>The natural product Emodin demonstrates a wide range of pharmacological properties including anticancer, anti-inflammatory, antiproliferation, vasorelaxant and anti-<it>H. pylori </it>activities. Although its <it>H. pylori </it>inhibition was discovered, no acting target information against Emodin has been revealed to date.</p> <p>Results</p> <p>Here we reported that Emodin functioned as a competitive inhibitor against the recombinant β-hydroxyacyl-ACP dehydratase from <it>Helicobacter pylori </it>(HpFabZ), and strongly inhibited the growth of <it>H. pylori </it>strains SS1 and ATCC 43504. Surface plasmon resonance (SPR) and isothermal titration calorimetry (ITC) based assays have suggested the kinetic and thermodynamic features of Emodin/HpFabZ interaction. Additionally, to inspect the binding characters of Emodin against HpFabZ at atomic level, the crystal structure of HpFabZ-Emodin complex was also examined. The results showed that Emodin inhibition against HpFabZ could be implemented either through its occupying the entrance of the tunnel or embedding into the tunnel to prevent the substrate from accessing the active site.</p> <p>Conclusion</p> <p>Our work is expected to provide useful information for illumination of Emodin inhibition mechanism against HpFabZ, while Emodin itself could be used as a potential lead compound for further anti-bacterial drug discovery.</p