Research on Repatriates’ Incentive Mechanism: Based on Knowledge Transfer Perspective

Repatriates’ experience and knowledge is important competition advantage for the parent company in international business. This paper discussed repatriates’ incentive mechanism based on the perspective of knowledge transfer, established theoretic model of repatriates’ knowledge transfer incentive mechanism, and pointed out that knowledge transfer was a process of repeated game between the parent company and the repatriates, the establishing of knowledge transfer incentive mechanism was trying to reach the equilibrium of the game, it provided theoretic basis for promoting repatriates’ knowledge transfer effectivel

MVF-Net: Multi-View 3D Face Morphable Model Regression

We address the problem of recovering the 3D geometry of a human face from a set of facial images in multiple views. While recent studies have shown impressive progress in 3D Morphable Model (3DMM) based facial reconstruction, the settings are mostly restricted to a single view. There is an inherent drawback in the single-view setting: the lack of reliable 3D constraints can cause unresolvable ambiguities. We in this paper explore 3DMM-based shape recovery in a different setting, where a set of multi-view facial images are given as input. A novel approach is proposed to regress 3DMM parameters from multi-view inputs with an end-to-end trainable Convolutional Neural Network (CNN). Multiview geometric constraints are incorporated into the network by establishing dense correspondences between different views leveraging a novel self-supervised view alignment loss. The main ingredient of the view alignment loss is a differentiable dense optical flow estimator that can backpropagate the alignment errors between an input view and a synthetic rendering from another input view, which is projected to the target view through the 3D shape to be inferred. Through minimizing the view alignment loss, better 3D shapes can be recovered such that the synthetic projections from one view to another can better align with the observed image. Extensive experiments demonstrate the superiority of the proposed method over other 3DMM methods.Comment: 2019 Conference on Computer Vision and Pattern Recognitio

Self-supervised Learning of Detailed 3D Face Reconstruction

In this paper, we present an end-to-end learning framework for detailed 3D face reconstruction from a single image. Our approach uses a 3DMM-based coarse model and a displacement map in UV-space to represent a 3D face. Unlike previous work addressing the problem, our learning framework does not require supervision of surrogate ground-truth 3D models computed with traditional approaches. Instead, we utilize the input image itself as supervision during learning. In the first stage, we combine a photometric loss and a facial perceptual loss between the input face and the rendered face, to regress a 3DMM-based coarse model. In the second stage, both the input image and the regressed texture of the coarse model are unwrapped into UV-space, and then sent through an image-toimage translation network to predict a displacement map in UVspace. The displacement map and the coarse model are used to render a final detailed face, which again can be compared with the original input image to serve as a photometric loss for the second stage. The advantage of learning displacement map in UV-space is that face alignment can be explicitly done during the unwrapping, thus facial details are easier to learn from large amount of data. Extensive experiments demonstrate the superiority of the proposed method over previous work.Comment: Accepted by IEEE Transactions on Image Processing (TIP

Learning to Construct 3D Building Wireframes from 3D Line Clouds

Line clouds, though under-investigated in the previous work, potentially encode more compact structural information of buildings than point clouds extracted from multi-view images. In this work, we propose the first network to process line clouds for building wireframe abstraction. The network takes a line cloud as input , i.e., a nonstructural and unordered set of 3D line segments extracted from multi-view images, and outputs a 3D wireframe of the underlying building, which consists of a sparse set of 3D junctions connected by line segments. We observe that a line patch, i.e., a group of neighboring line segments, encodes sufficient contour information to predict the existence and even the 3D position of a potential junction, as well as the likelihood of connectivity between two query junctions. We therefore introduce a two-layer Line-Patch Transformer to extract junctions and connectivities from sampled line patches to form a 3D building wireframe model. We also introduce a synthetic dataset of multi-view images with ground-truth 3D wireframe. We extensively justify that our reconstructed 3D wireframe models significantly improve upon multiple baseline building reconstruction methods. The code and data can be found at https://github.com/Luo1Cheng/LC2WF.Comment: 10 pages, 6 figure

Smooth image-to-image translations with latent space interpolations

Multi-domain image-to-image (I2I) translations can transform a source image according to the style of a target domain. One important, desired characteristic of these transformations, is their graduality, which corresponds to a smooth change between the source and the target image when their respective latent-space representations are linearly interpolated. However, state-of-the-art methods usually perform poorly when evaluated using inter-domain interpolations, often producing abrupt changes in the appearance or non-realistic intermediate images. In this paper, we argue that one of the main reasons behind this problem is the lack of sufficient inter-domain training data and we propose two different regularization methods to alleviate this issue: a new shrinkage loss, which compacts the latent space, and a Mixup data-augmentation strategy, which flattens the style representations between domains. We also propose a new metric to quantitatively evaluate the degree of the interpolation smoothness, an aspect which is not sufficiently covered by the existing I2I translation metrics. Using both our proposed metric and standard evaluation protocols, we show that our regularization techniques can improve the state-of-the-art multi-domain I2I translations by a large margin. Our code will be made publicly available upon the acceptance of this article

Changes in HIV-1 Subtypes B and C Genital Tract RNA in Women and Men After Initiation of Antiretroviral Therapy

Background. Combination antiretroviral therapy (cART) reduces genital tract human immunodeficiency virus type 1 (HIV-1) load and reduces the risk of sexual transmission, but little is known about the efficacy of cART for decreasing genital tract viral load (GTVL) and differences in sex or HIV-1 subtype

Assessment of the energy consumption of the biogas upgrading process with pressure swing adsorption using novel adsorbents

Biogas consisting mainly of methane and carbon dioxide can be utilized as renewable energy source in combined heat and power plant, vehicle fuel, or substitute for natural gas after upgrading process. In this work, biogas upgrading with pressure swing adsorption is studied with dynamic process simulation method using the modified Skarstrom cycle, which gives the variation of the pressure and stream concentration with cycle time in the column, while the variation of these variables with cycle time are difficult to be observed by experiments. Thus the simulation results would supply more dynamic information of the process. The pressure swing adsorption performance of the novel adsorbent metal-organic framework 508b is simulated and compared with the processes using traditional adsorbents such as zeolite 13X and carbon molecular sieve 3 K. A two-column six-step pressure swing adsorption simulation model involving parametric sensitivity analysis is established. The Langmuir adsorption isotherm models of the three adsorbents are built by fitting experiment data. The energy consumption of the pressure swing adsorption process with different adsorbents is calculated by fixing a 98% purity and 85% recovery ratio of methane product after investigating the key parameters, i.e., adsorption pressure, desorption pressure and purge to feed ratio. Simulation results reveal that the order of the energy consumption in the process is zeolite 13X > carbon molecular sieve 3 K> metal-organic framework 508b. For metal-organic framework 508b has linear carbon dioxide isotherm compared with zeolite 13X, which makes the desorption of the carbon dioxide easier, and its adsorption capacity is higher than with carbon molecular sieve 3 K under the same partial pressure. In conclusion, the energy consumption of pressure swing adsorption process using metal-organic framework 508b adsorbent is 56% lower than that using zeolite 13X, and the column packed with metal-organic framework 508b is estimated to be 13% smaller in diameter than that with zeolite 13X. The simulation results can help to provide guidelines for development of new adsorbents to meet higher product purity and recovery ratio along with lower process energy consumption. (C) 2015 Elsevier Ltd. All rights reserved