Res2Net: A New Multi-scale Backbone Architecture

Representing features at multiple scales is of great importance for numerous vision tasks. Recent advances in backbone convolutional neural networks (CNNs) continually demonstrate stronger multi-scale representation ability, leading to consistent performance gains on a wide range of applications. However, most existing methods represent the multi-scale features in a layer-wise manner. In this paper, we propose a novel building block for CNNs, namely Res2Net, by constructing hierarchical residual-like connections within one single residual block. The Res2Net represents multi-scale features at a granular level and increases the range of receptive fields for each network layer. The proposed Res2Net block can be plugged into the state-of-the-art backbone CNN models, e.g., ResNet, ResNeXt, and DLA. We evaluate the Res2Net block on all these models and demonstrate consistent performance gains over baseline models on widely-used datasets, e.g., CIFAR-100 and ImageNet. Further ablation studies and experimental results on representative computer vision tasks, i.e., object detection, class activation mapping, and salient object detection, further verify the superiority of the Res2Net over the state-of-the-art baseline methods. The source code and trained models are available on https://mmcheng.net/res2net/.Comment: 11 pages, 7 figure

A New Business Model of Electronic Commerce with Innovative Strategies

There are a lot of problems that make the business of electronic stores very difficult, especially for those firms that lack the required expertise and resources for running an electronic business. This study proposes a new business model of electronic commerce (EC), which aims to tackle those problems and help enterprises run electronic stores well. This model applies the franchise system of chain store, a very successful modern business model, to the management of electronic stores to take advantage of the chain’s competitive power by integrating individual affiliate sites as a whole. There are eight components in the model. Implementation strategies of the model, which are quite different from those generic strategies commonly used in implementing business models, are also proposed. The feasibility of the model and its implementation strategies were validated using the Nominal Group Technique (NGT), the case study, and the questionnaire survey approaches. Finally, practical implications for applying the model are discussed, and directions for further study are also suggested

Super-resolution surface reconstruction from few low-resolution slices

In many imaging applications where segmented features (e.g. blood vessels) are further used for other numerical simulations (e.g. finite element analysis), the obtained surfaces do not have fine resolutions suitable for the task. Increasing the resolution of such surfaces becomes crucial. This paper proposes a new variational model for solving this problem, based on an Euler-Elastica-based regulariser. Further, we propose and implement two numerical algorithms for solving the model, a projected gradient descent method and the alternating direction method of multipliers. Numerical experiments using real-life examples (including two from outputs of another variational model) have been illustrated for effectiveness. The advantages of the new model are shown through quantitative comparisons by the standard deviation of Gaussian curvatures and mean curvatures from the viewpoint of discrete geometry

Breaking the limitations with sparse inputs by variational frameworks (BLIss) in terahertz super-resolution 3D reconstruction

Data acquisition, image processing, and image quality are the long-lasting issues for terahertz (THz) 3D reconstructed imaging. Existing methods are primarily designed for 2D scenarios, given the challenges associated with obtaining super-resolution (SR) data and the absence of an efficient SR 3D reconstruction framework in conventional computed tomography (CT). Here, we demonstrate BLIss, a new approach for THz SR 3D reconstruction with sparse 2D data input. BLIss seamlessly integrates conventional CT techniques and variational framework with the core of the adapted Euler-Elastica-based model. The quantitative 3D image evaluation metrics, including the standard deviation of Gaussian, mean curvatures, and the multi-scale structural similarity index measure (MS-SSIM), validate the superior smoothness and fidelity achieved with our variational framework approach compared with conventional THz CT modal. Beyond its contributions to advancing THz SR 3D reconstruction, BLIss demonstrates potential applicability in other imaging modalities, such as X-ray and MRI. This suggests extensive impacts on the broader field of imaging applications

Fast Terahertz 3D Super-Resolution Surface Reconstruction by Variational Model from Limited Low-Resolution Sampling

Integrating with the signal processing, inverse Radon transform, and the variational model, the framework at least saving 83% data acquisition time for fast, smooth three-dimensional (3D) reconstruction from the limited dataset is elucidated in the field of terahertz imaging applications. In hot pursuit, under the viewpoint of discrete geometry, the quantifiable comparison for 3D surfaces by computing the standard deviation of mean curvatures is also proposed to show the reconstructed effectiveness from less input with gaps

The divided brain : Functional brain asymmetry underlying self-construal

Acknowledgments This research is partly supported by the National Natural Science Foundation of China (62071049, 61801026) & Capital Medical University Advanced Innovation Center for Big Data-Based Precision Medicine Plan (BHME-201907), and the Leverhulme Trust (RPG-2019-010).Peer reviewedPublisher PD

Euler-Elastica Variational Model for Pulsed Terahertz 3D Imaging

The variational Euler-Elastica model is developed for high-precision terahertz 3D tomographic applications. Our method provides a new approach to mitigate diffraction-limited terahertz reconstructed images with noises and demonstrates the efficient practicality with limited THz datasets