China's Cultural Communication with the Middle East under the BRI: Assessment and Prospects

Since China put forward the Belt & Road Initiative (BRI) in 2013, the BRI has achieved significant progress, especially in the Middle East. China's cultural communication with the Middle East is the emotional glue that brings both sides together by building trust and dispelling doubts. Evidence in the statements from the Ministry of Foreign Affairs of China, the communication by the media, the development of Confucius Institutes, and the cooperation between different NGOs, China shows the fact that the BRI is being built jointly with the Middle East. Moreover, the Middle East's unique geopolitical situation and cultural differences require China's cultural communication with states in the region to be done with Chinese characteristics. With descriptive case study methodology, this article revolves around these questions: How do China's cultural communication mechanisms work? Why does China's cultural communication play an essential role in the Middle East under the BRI? What is its characteristic? To answer these questions, the article is structured in three parts. The first part will outline the role of cultural communication in Chinese diplomacy, based on cultural communication, to jointly build the BRI. The second part will focus on Chinese communication with the Middle East through cooperation related to the pandemic, tourism, Confucius Institutes, and the China-Arab States Cooperation Forum. The third part will highlight China's cultural communication with Chinese characteristics in the Middle East

Study on nonlinear dynamic of ball bearing-offset disk rotor system with whirling-swing coupling vibration

A dynamic model of ball bearing-offset disk rotor system with whirling-swing coupling vibration is presented, in which the rotor disk offset position and the swing vibration of disk are concerned. In the model of ball bearing, the bearing radial clearance, nonlinear Hertzian contact force and the varying compliance (VC) vibration are considered. Numerical methods are used to obtain the nonlinear dynamic response of the system under different disk offset values for considering the disk swing vibration or not. Effects of bearing radial clearance variation on the dynamic performance of the system under different rotor offset values are investigated. It is shown that the nonlinear dynamic of the offset disk rotor system enhances obviously when rotor disk swing vibration is considered. As rotor disk offset increasing, the sensitivity of critical speed to variation of the bearing radial clearance improves

Attention-based Pyramid Aggregation Network for Visual Place Recognition

Visual place recognition is challenging in the urban environment and is usually viewed as a large scale image retrieval task. The intrinsic challenges in place recognition exist that the confusing objects such as cars and trees frequently occur in the complex urban scene, and buildings with repetitive structures may cause over-counting and the burstiness problem degrading the image representations. To address these problems, we present an Attention-based Pyramid Aggregation Network (APANet), which is trained in an end-to-end manner for place recognition. One main component of APANet, the spatial pyramid pooling, can effectively encode the multi-size buildings containing geo-information. The other one, the attention block, is adopted as a region evaluator for suppressing the confusing regional features while highlighting the discriminative ones. When testing, we further propose a simple yet effective PCA power whitening strategy, which significantly improves the widely used PCA whitening by reasonably limiting the impact of over-counting. Experimental evaluations demonstrate that the proposed APANet outperforms the state-of-the-art methods on two place recognition benchmarks, and generalizes well on standard image retrieval datasets.Comment: Accepted to ACM Multimedia 201

Focal Inverse Distance Transform Maps for Crowd Localization and Counting in Dense Crowd

In this paper, we propose a novel map for dense crowd localization and crowd counting. Most crowd counting methods utilize convolution neural networks (CNN) to regress a density map, achieving significant progress recently. However, these regression-based methods are often unable to provide a precise location for each person, attributed to two crucial reasons: 1) the density map consists of a series of blurry Gaussian blobs, 2) severe overlaps exist in the dense region of the density map. To tackle this issue, we propose a novel Focal Inverse Distance Transform (FIDT) map for crowd localization and counting. Compared with the density maps, the FIDT maps accurately describe the people's location, without overlap between nearby heads in dense regions. We simultaneously implement crowd localization and counting by regressing the FIDT map. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art localization-based methods in crowd localization tasks, achieving very competitive performance compared with the regression-based methods in counting tasks. In addition, the proposed method presents strong robustness for the negative samples and extremely dense scenes, which further verifies the effectiveness of the FIDT map. The code and models are available at https://github.com/dk-liang/FIDTM.Comment: The code and models are available at https://github.com/dk-liang/FIDT

Discrete dynamics analysis for nonlinear collocated multivariable mass-damper-spring intelligent mechanical vibration systems

A new time-discretization method for the development of nonlinear collocated multivariable mass-damper-spring (MDS) intelligent mechanical vibration systems is proposed. It is based on the Runge-Kutta series expansion method and zero-order hold assumption. In this paper, we show that the mathematical structure of the new discretization scheme is explored and characterized in order to represent the discrete dynamics properties for nonlinear collocated multivariable MDS intelligent mechanical vibration systems. In particular, the decent effects of the time-discretization method on key properties of nonlinear multivariable MDS mechanical vibration systems, such as discrete zero dynamics and asymptotic stability, are examined. The resulting time-discretization provides discrete dynamics behavior for nonlinear MDS mechanical vibration systems, which enabling the application of existing controller design techniques. The ideas presented here generalize well-known results from the linear case to nonlinear plants

Learning Incremental Triplet Margin for Person Re-identification

Person re-identification (ReID) aims to match people across multiple non-overlapping video cameras deployed at different locations. To address this challenging problem, many metric learning approaches have been proposed, among which triplet loss is one of the state-of-the-arts. In this work, we explore the margin between positive and negative pairs of triplets and prove that large margin is beneficial. In particular, we propose a novel multi-stage training strategy which learns incremental triplet margin and improves triplet loss effectively. Multiple levels of feature maps are exploited to make the learned features more discriminative. Besides, we introduce global hard identity searching method to sample hard identities when generating a training batch. Extensive experiments on Market-1501, CUHK03, and DukeMTMCreID show that our approach yields a performance boost and outperforms most existing state-of-the-art methods.Comment: accepted by AAAI19 as spotligh

DCANet: Dual Convolutional Neural Network with Attention for Image Blind Denoising

Noise removal of images is an essential preprocessing procedure for many computer vision tasks. Currently, many denoising models based on deep neural networks can perform well in removing the noise with known distributions (i.e. the additive Gaussian white noise). However eliminating real noise is still a very challenging task, since real-world noise often does not simply follow one single type of distribution, and the noise may spatially vary. In this paper, we present a new dual convolutional neural network (CNN) with attention for image blind denoising, named as the DCANet. To the best of our knowledge, the proposed DCANet is the first work that integrates both the dual CNN and attention mechanism for image denoising. The DCANet is composed of a noise estimation network, a spatial and channel attention module (SCAM), and a CNN with a dual structure. The noise estimation network is utilized to estimate the spatial distribution and the noise level in an image. The noisy image and its estimated noise are combined as the input of the SCAM, and a dual CNN contains two different branches is designed to learn the complementary features to obtain the denoised image. The experimental results have verified that the proposed DCANet can suppress both synthetic and real noise effectively. The code of DCANet is available at https://github.com/WenCongWu/DCANet

New testing and calculation method for determination viscoelasticity of optical glass

Viscoelastic properties of glass within molding temperatures, such as shear relaxation modulus and bulk relaxation modulus, are key factors to build successful numerical model, predict forming process, and determine optimal process parameters for precision glass molding. However, traditional uniaxial compression creep tests with large strains are very limited in obtaining high-accuracy viscoelastic data of glass, due to the declining compressive stress caused by the increasing cross-sectional area of specimen in testing process. Besides, existing calculation method has limitation in transforming creep data to viscoelasticity data, especially when Poisson's ratio is unknown at molding temperature, which further induces a block to characterize viscoelastic parameter. This study proposes a systematic acquisition method tbr high-precision viscoelastic data, including creep testing, viscoelasticity calculation, and finite element verification. A minimal uniaxial creep testing (MUCT) method based on thermo-mechanical analysis (TMA) instrument is first built to obtain ideal and accurate creep data, by keeping compressive stress as a constant. A new calculation method on viscoelasticity determination is then proposed to derive shear relaxation modulus without the need of knowing bulk modulus or Poisson's ratio, which, compared with traditional method, extends the application range of viscoelasticity calculation. After determination, the obtained viscoelastic data are further incorporated into a numerical simulation model of MUCT to verify the accuracy of the determined viscoelasticity. Base on the great consistence between simulated and measured results (uniaxial creep displacement), the proposed systematic acquisition method can be used as a high accuracy viscoelasticity determination method.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

LATFormer: Locality-Aware Point-View Fusion Transformer for 3D Shape Recognition

Recently, 3D shape understanding has achieved significant progress due to the advances of deep learning models on various data formats like images, voxels, and point clouds. Among them, point clouds and multi-view images are two complementary modalities of 3D objects and learning representations by fusing both of them has been proven to be fairly effective. While prior works typically focus on exploiting global features of the two modalities, herein we argue that more discriminative features can be derived by modeling ``where to fuse''. To investigate this, we propose a novel Locality-Aware Point-View Fusion Transformer (LATFormer) for 3D shape retrieval and classification. The core component of LATFormer is a module named Locality-Aware Fusion (LAF) which integrates the local features of correlated regions across the two modalities based on the co-occurrence scores. We further propose to filter out scores with low values to obtain salient local co-occurring regions, which reduces redundancy for the fusion process. In our LATFormer, we utilize the LAF module to fuse the multi-scale features of the two modalities both bidirectionally and hierarchically to obtain more informative features. Comprehensive experiments on four popular 3D shape benchmarks covering 3D object retrieval and classification validate its effectiveness