Understanding the Role and Impact of FDI on Economic Development

Abstract China is emerging as a key global market of the 21st century, as its economy continues to expand rapidly, and as it progresses with accession to the World Trade Organisation. Many European companies are now approach China as a long ���¢��������term growth opportunity, and are increasingly using Foreign Direct Investment as their mode of entry into the country. Since 1979 the progressive economic reform has radically transformed the Chinese economy. China has gradually opened its economy and has attracted a large amount of foreign direct investment (FDI). The literature on the effects of FDI in developing countries provides a range of prospective benefits to the recipient country. However, many benefits of FDI are frequently challenged. This dissertation proposed a hypothesis, which was to be verified, that closely developed FDI are a key element, which influences the success of trade in the Chinese market and further enhances successful customer relationship building. Finally a simple approach to examine all the related variables and FDI would be implemented. This study also reveals the importance of China���¢��������s policies in attracting FDI in the analysis

Development of an internet based system for modeling biotin metabolism using Bayesian networks

Biotin is an essential water-soluble vitamin crucial for maintaining normal body functions. The importance of biotin for human health has been under-appreciated but there is plenty of opportunity for future research with great importance for human health. Currently, carrying out predictions of biotin metabolism involves tedious manual manipulations. In this paper, we report the development of BiotinNet, an internet based program that uses Bayesian networks to integrate published data on various aspects of biotin metabolism. Users can provide a combination of values on the levels of biotin related metabolites to obtain the predictions on other metabolites that are not specified. As an inherent feature of Bayesian networks, the uncertainty of the prediction is also quantified and reported to the user. This program enables convenient in silico experiments regarding biotin metabolism, which can help researchers design future experiments while new data can be continuously incorporated

JAWS: Just A Wild Shot for Cinematic Transfer in Neural Radiance Fields

This paper presents JAWS, an optimization-driven approach that achieves the robust transfer of visual cinematic features from a reference in-the-wild video clip to a newly generated clip. To this end, we rely on an implicit-neural-representation (INR) in a way to compute a clip that shares the same cinematic features as the reference clip. We propose a general formulation of a camera optimization problem in an INR that computes extrinsic and intrinsic camera parameters as well as timing. By leveraging the differentiability of neural representations, we can back-propagate our designed cinematic losses measured on proxy estimators through a NeRF network to the proposed cinematic parameters directly. We also introduce specific enhancements such as guidance maps to improve the overall quality and efficiency. Results display the capacity of our system to replicate well known camera sequences from movies, adapting the framing, camera parameters and timing of the generated video clip to maximize the similarity with the reference clip.Comment: CVPR 2023. Project page with videos and code: http://www.lix.polytechnique.fr/vista/projects/2023_cvpr_wan

Multi-granularity Backprojection Transformer for Remote Sensing Image Super-Resolution

Backprojection networks have achieved promising super-resolution performance for nature images but not well be explored in the remote sensing image super-resolution (RSISR) field due to the high computation costs. In this paper, we propose a Multi-granularity Backprojection Transformer termed MBT for RSISR. MBT incorporates the backprojection learning strategy into a Transformer framework. It consists of Scale-aware Backprojection-based Transformer Layers (SPTLs) for scale-aware low-resolution feature learning and Context-aware Backprojection-based Transformer Blocks (CPTBs) for hierarchical feature learning. A backprojection-based reconstruction module (PRM) is also introduced to enhance the hierarchical features for image reconstruction. MBT stands out by efficiently learning low-resolution features without excessive modules for high-resolution processing, resulting in lower computational resources. Experiment results on UCMerced and AID datasets demonstrate that MBT obtains state-of-the-art results compared to other leading methods

Dynamic Recognition of Driver’s Propensity Based on GPS Mobile Sensing Data and Privacy Protection

Driver’s propensity is a dynamic measurement of driver’s emotional preference characteristics in driving process. It is a core parameter to compute driver’s intention and consciousness in safety driving assist system, especially in vehicle collision warning system. It is also an important influence factor to achieve the Driver-Vehicle-Environment Collaborative Wisdom and Control macroscopically. In this paper, dynamic recognition model of driver’s propensity based on support vector machine is established taking the vehicle safety controlled technology and respecting and protecting the driver’s privacy as precondition. The experiment roads travel time obtained through GPS is taken as the characteristic parameter. The sensing information of Driver-Vehicle-Environment was obtained through psychological questionnaire tests, real vehicle experiments, and virtual driving experiments, and the information is used for parameter calibration and validation of the model. Results show that the established recognition model of driver’s propensity is reasonable and feasible, which can achieve the dynamic recognition of driver’s propensity to some extent. The recognition model provides reference and theoretical basis for personalized vehicle active safety systems taking people as center especially for the vehicle safety technology based on the networking

Task modules Partitioning, Scheduling and Floorplanning for Partially Dynamically Reconfigurable Systems Based on Modern Heterogeneous FPGAs

Modern field programmable gate array(FPGA) can be partially dynamically reconfigurable with heterogeneous resources distributed on the chip. And FPGA-based partially dynamically reconfigurable system(FPGA-PDRS) can be used to accelerate computing and improve computing flexibility. However, the traditional design of FPGA-PDRS is based on manual design. Implementing the automation of FPGA-PDRS needs to solve the problems of task modules partitioning, scheduling, and floorplanning on heterogeneous resources. Existing works only partly solve problems for the automation process of FPGA-PDRS or model homogeneous resource for FPGA-PDRS. To better solve the problems in the automation process of FPGA-PDRS and narrow the gap between algorithm and application, in this paper, we propose a complete workflow including three parts, pre-processing to generate the list of task modules candidate shapes according to the resources requirements, exploration process to search the solution of task modules partitioning, scheduling, and floorplanning, and post-optimization to improve the success rate of floorplan. Experimental results show that, compared with state-of-the-art work, the proposed complete workflow can improve performance by 18.7\%, reduce communication cost by 8.6\%, on average, with improving the resources reuse rate of the heterogeneous resources on the chip. And based on the solution generated by the exploration process, the post-optimization can improve the success rate of the floorplan by 14\%

GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-resolved EEG Motor Imagery Signals

Towards developing effective and efficient brain-computer interface (BCI) systems, precise decoding of brain activity measured by electroencephalogram (EEG), is highly demanded. Traditional works classify EEG signals without considering the topological relationship among electrodes. However, neuroscience research has increasingly emphasized network patterns of brain dynamics. Thus, the Euclidean structure of electrodes might not adequately reflect the interaction between signals. To fill the gap, a novel deep learning framework based on the graph convolutional neural networks (GCNs) was presented to enhance the decoding performance of raw EEG signals during different types of motor imagery (MI) tasks while cooperating with the functional topological relationship of electrodes. Based on the absolute Pearson's matrix of overall signals, the graph Laplacian of EEG electrodes was built up. The GCNs-Net constructed by graph convolutional layers learns the generalized features. The followed pooling layers reduce dimensionality, and the fully-connected softmax layer derives the final prediction. The introduced approach has been shown to converge for both personalized and group-wise predictions. It has achieved the highest averaged accuracy, 93.056% and 88.57% (PhysioNet Dataset), 96.24% and 80.89% (High Gamma Dataset), at the subject and group level, respectively, compared with existing studies, which suggests adaptability and robustness to individual variability. Moreover, the performance was stably reproducible among repetitive experiments for cross-validation. To conclude, the GCNs-Net filters EEG signals based on the functional topological relationship, which manages to decode relevant features for brain motor imagery