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South Asia has been an important destination of foreign aid over the past decades. Since a large part of aid is disbursed for social and economic infrastructure…
South Asia has been an important destination of foreign aid over the past decades. Since a large part of aid is disbursed for social and economic infrastructure development in South Asian countries, and the volume of aid has tremendously increased in recent years, the purpose of this study is to investigate how far various categories of foreign aid affects economic growth rate in these countries. In addition, as the trend of each category of aid transfer appears to have been volatile, this study also investigates whether the volatilities inhibit growth rate in these countries.
In this study, South Asia refers to India, Bangladesh, Pakistan and Sri Lanka. The Random effects approach is employed incorporating panel data for the period of 1995‐2008. The aggregate foreign aid is classified into various categories to have a comprehensive investigation.
Foreign aid positively associated with growth whereas the volatility of aid hurts it. Long‐impact aid promotes growth more than short‐impact aid does. The volatility of short‐impact aid hurts growth, whereas the volatility of long‐impact aid has no effect on it. Pure aid and its volatility have no effect on growth.
This study has identified the structure of foreign aid disbursed in these countries, and explored how far each category and respective volatility affects growth. These findings would be useful to the scholars and policy makers in the recipient countries as well as donors, to make foreign aid much more effective in future.
The purpose of this paper is to use the most popular deep learning algorithm to complete the vehicle detection in the urban area of MiniSAR image, and provide reliable…
The purpose of this paper is to use the most popular deep learning algorithm to complete the vehicle detection in the urban area of MiniSAR image, and provide reliable means for ground monitoring.
An accurate detector called the rotation region-based convolution neural networks (CNN) with multilayer fusion and multidimensional attention (M2R-Net) is proposed in this paper. Specifically, M2R-Net adopts the multilayer feature fusion strategy to extract feature maps with more extensive information. Next, the authors implement the multidimensional attention network to highlight target areas. Furthermore, a novel balanced sampling strategy for hard and easy positive-negative samples and a global balanced loss function are applied to deal with spatial imbalance and objective imbalance. Finally, rotation anchors are used to predict and calibrate the minimum circumscribed rectangle of vehicles.
By analyzing many groups of experiments, the validity and universality of the proposed model are verified. More importantly, comparisons with SSD, LRTDet, RFCN, DFPN, CMF-RCNN, R3Det, SCRDet demonstrate that M2R-Net has state-of-the-art detection performance.
The progress in the field of MiniSAR application has been slow due to strong speckle noise, phase error, complex environments and a low signal-to-noise ratio. In addition, four kinds of imbalances, i.e. spatial imbalance, scale imbalance, class imbalance and objective imbalance, in object detection based on the CNN greatly inhibit the optimization of detection performance.
This research can not only enrich the means of daily traffic monitoring but also be used for enemy intelligence reconnaissance in wartime.