The Changing Shopping Space in Georgia: The Influence from the Chinese ‘Belt and Road Initiative’

The Caucasus region is where the east meets the west and is known as an area in whichconflictsandopportunitiesoccursimultaneously.Thedevelopmentofthisregion used to be constricted by its geographic and political conditions. However, due to advancements in transport and the dissolution of Soviet Union, the Caucasus region has become a strategic region for development. ThisresearchfocusesonGeorgiainCaucasusduetothecountry’sintimaterelationships with many European and Asian countries. In addition, China announced that it will invest more infrastructure and capital in Georgia. Due to the influence of the Chinese ‘BeltandRoadInitiative’(B&R),Georgiaiscurrentlyundergoingmanychanges,despite discussions focusing on political or economic perspectives. With the global free market and the Chinese ‘Belt and Road Initiative’, Georgia seeks to increase its economic development. Shopping space is one of the most affected spaces of development. Attributes such as the placement of shops can be analysed to indicate the change of internal public space, as shops become very Chinese style and the traditional Georgian shopping spaces gradually disappear due to the constant chasing of cash flows. As a pilot study, this research hopes to uncover potentials and threats of shopping space development in Georgia, and it aims to discover the underlying principles for the changes. Mapping and case studies are employed in the investigation. The research hopes to contribute to a healthy urban development by finding the balance between economic and spatial development

Synthetic Sample Selection via Reinforcement Learning

Synthesizing realistic medical images provides a feasible solution to the shortage of training data in deep learning based medical image recognition systems. However, the quality control of synthetic images for data augmentation purposes is under-investigated, and some of the generated images are not realistic and may contain misleading features that distort data distribution when mixed with real images. Thus, the effectiveness of those synthetic images in medical image recognition systems cannot be guaranteed when they are being added randomly without quality assurance. In this work, we propose a reinforcement learning (RL) based synthetic sample selection method that learns to choose synthetic images containing reliable and informative features. A transformer based controller is trained via proximal policy optimization (PPO) using the validation classification accuracy as the reward. The selected images are mixed with the original training data for improved training of image recognition systems. To validate our method, we take the pathology image recognition as an example and conduct extensive experiments on two histopathology image datasets. In experiments on a cervical dataset and a lymph node dataset, the image classification performance is improved by 8.1% and 2.3%, respectively, when utilizing high-quality synthetic images selected by our RL framework. Our proposed synthetic sample selection method is general and has great potential to boost the performance of various medical image recognition systems given limited annotation.Comment: MICCAI202

A Sparsity-Based InSAR Phase Denoising Algorithm Using Nonlocal Wavelet Shrinkage

An interferometric synthetic aperture radar (InSAR) phase denoising algorithm using the local sparsity of wavelet coefficients and nonlocal similarity of grouped blocks was developed. From the Bayesian perspective, the double-l1 norm regularization model that enforces the local and nonlocal sparsity constraints was used. Taking advantages of coefficients of the nonlocal similarity between group blocks for the wavelet shrinkage, the proposed algorithm effectively filtered the phase noise. Applying the method to simulated and acquired InSAR data, we obtained satisfactory results. In comparison, the algorithm outperformed several widely-used InSAR phase denoising approaches in terms of the number of residues, root-mean-square errors and other edge preservation indexes