IMPACT OF THE CORONAVIRUS PANDEMIC ON DE-GLOBALIZATION

In this chapter, we argue that the economic devastation caused by the coronavirus pandemic has forced governments to attach a higher weight to social welfare over other political objectives in determining trade policies. We make this point by looking at the pandemic package announced by the Indian government during the initial stages of the coronavirus episode. We then analyse this argument in the context of an extended version of the protection for sale model to conjecture about the possible short-run and long-run implications of the pandemic on globalization and its reverse. In our framework, in the short run governments are free to choose unilateral tariff levels according to the changed circumstances. In the long run, these unilateral tariff levels are set by a process of bargaining with trading partners. Three factors emerge as important in determining the future course of globalization: the continued importance given to social welfare, the extent of labour intensity of the import-competing sector and the relative bargaining power of the country. In general, countries attaching more importance to social welfare will reduce tariffs and ease out of the de-globalization process if they have a relatively capital-intensive import-competing sector and low bargaining power in trade negotiations. However, countries with labour-intensive import-competing sectors and some bargaining power in trade negotiations may increase tariffs and further contribute to the de-globalization process. The analysis also gives some rationale to the Indian government’s self-reliance slogan during the announcement of the pandemic economic package and its relative inaction in actual policy front. © 2022 selection and editorial matter, Rajib Bhattacharyya, Ananya Ghosh Dastidar and Soumyen Sikdar;individual chapters, the contributors.

Classification of COVID-19 using Deep Learning and Radiomic Texture Features extracted from CT scans of Patients Lungs

COVID-19 is an air-borne viral infection, which infects the respiratory system in the human body, and it became a global pandemic in early March 2020. The damage caused by the COVID-19 disease in a human lung region can be identified using Computed Tomography (CT) scans. We present a novel approach in classifying COVID-19 infection and normal patients using a Random Forest (RF) model to train on a combination of Deep Learning (DL) features and Radiomic texture features extracted from CT scans of patient's lungs. We developed and trained DL models using CNN architectures for extracting DL features. The Radiomic texture features are calculated using CT scans and its associated infection masks. In this work, we claim that the RFs classification using the DL features in conjunction with Radiomic texture features enhances prediction performance. The experiment results show that our proposed models achieve a higher True Positive rate with the average Area Under the Receiver Curve (AUC) of 0.9768, 95% Confidence Interval (CI) [0.9757, 0.9780]. © 2021 IEEE.

Toward Generating Synthetic CT Volumes using a 3D-Conditional Generative Adversarial Network

Network (cGAN) architecture that is capable of generating 3D Computed Tomography scans in voxels from noisy and/or pixelated approximations and with the potential to generate full synthetic 3D scan volumes. We believe conditional cGAN to be a tractable approach to generate 3D CT volumes, even though the problem of generating full resolution deep fakes is presently impractical due to GPU memory limitations. We present results for autoencoder, denoising, and depixelating tasks which are trained and tested on two novel COVID19 CT datasets. Our evaluation metrics, Peak Signal to Noise ratio (PSNR) range from 12.53 - 46.46 dB, and range from 0.89 to 1. © 2020 IEEE.

Forecasting the Gulf Stream Path Using Buoyancy and Wind Forcing Over the North Atlantic

Fluctuations in the path of the Gulf Stream (GS) have been previously studied by primarily connecting to either the wind-driven subtropical gyre circulation or buoyancy forcing via the subpolar gyre. Here we present a statistical model for 1 year predictions of the GS path (represented by the GS northern wall-GSNW) between 75 degrees W and 65 degrees W incorporating both mechanisms in a combined framework. An existing model with multiple parameters including the previous year's GSNW index, center location, and amplitude of the Icelandic Low and the Southern Oscillation Index was augmented with basin-wide Ekman drift over the Azores High. The addition of the wind is supported by a validation of the simpler two-layer Parsons-Veronis model of GS separation over the last 40 years. A multivariate analysis was carried out to compare 1-year-in-advance forecast correlations from four different models. The optimal predictors of the best performing model include: (a) the GSNW index from the previous year, (b) gyre-scale integrated Ekman Drift over the past 2 years, and (c) longitude of the Icelandic Low center lagged by 3 years. The forecast correlation over the 27 years (1994-2020) is 0.65, an improvement from the previous multi-parameter model's forecast correlation of 0.52. The improvement is attributed to the addition of the wind-drift component. The sensitivity of forecasting the GS path after extreme atmospheric years is quantified. Results indicate the possibility of better understanding and enhanced predictability of the dominant wind-driven variability of the Atlantic Meridional Overturning Circulation and of fisheries management models that use the GS path as a metric.

Generating Realistic COVID-19 x-rays with a Mean Teacher plus Transfer Learning GAN

COVID-19 is a novel infectious disease responsible for over 1.2 million deaths worldwide as of November 2020. The need for rapid testing is a high priority and alternative testing strategies including x-ray image classification are a promising area of research. However, at present, public datasets for COVID-19 x-ray images have low data volumes, making it challenging to develop accurate image classifiers. Several recent papers have made use of Generative Adversarial Networks (GANs) in order to increase the training data volumes. But realistic synthetic COVID-19 x-rays remain challenging to generate. We present a novel Mean Teacher + Transfer GAN (MTT-GAN) that generates COVID-19 chest x-ray images of high quality. In order to create a more accurate GAN, we employ transfer learning from the Kaggle pneumonia x-ray dataset, a highly relevant data source orders of magnitude larger than public COVID-19 datasets. Furthermore, we employ the Mean Teacher algorithm as a constraint to improve stability of training. Our qualitative analysis shows that the MTT-GAN generates x-ray images that are greatly superior to a baseline GAN and visually comparable to real x-rays. Although board-certified radiologists can distinguish MTT-GAN fakes from real COVID-19 x-rays, quantitative analysis shows that MTT-GAN greatly improves the accuracy of both a binary COVID-19 classifier as well as a multi-class pneumonia classifier as compared to a baseline GAN. Our classification accuracy is favorable as compared to recently reported results in the literature for similar binary and multi-class COVID-19 screening tasks.

Vision powered conversational AI for easy human dialogue systems

In this paper, we propose an end to end goal-oriented conversational AI agent that can provide contextual information from a potential hazard site. We posit the conversational agent as a FloodBot capable of seeing, sensing, assessing hazard condition, and ultimately conversing about them. We present our domain-specific FloodBot design-solution and learning-experience from the real-time deployment in a flash flood devastated city that uses state-of-the-art deep learning models. We specifically used computer vision and pertinent natural language processing technologies to empower the conversation power of the FloodBot. To deliver such practical and usable AI, we chain multiple deep learning frameworks and create a human-friendly question-answer based dialogue system. We present our deployment details from the last five months and validate the results using ongoing COVID19's impact on the area as well. © 2020 IEEE.