Showing papers in "Social Science Research Network in 2022"

Computer Vision and Image Understanding

Abstract: In this paper, we introduce methods to differentiate posed expressions from spontaneous ones by capturing global spatial patterns embedded in posed and spontaneous expressions, and by incorporating gender and expression categories as privileged information during spatial pattern modeling. Specifically, we construct multiple restricted Boltzmann machines (RBMs) with continuous visible units to model spatial patterns from facial geometric features given expression-related factors, i.e., gender and expression categories. During testing, only facial geometric features are provided, and the samples are classified into posed or spontaneous expressions according to the RBM with the largest likelihood. Furthermore, we propose efficient inference algorithm by extending annealing importance sampling to RBM with continuous visible units for calculating partition function of RBMs. Experimental results on benchmark databases demonstrate the effectiveness of the proposed approach in modelling global spatial patterns as well as its superior posed and spontaneous expression distinction performance over existing approaches.

Testing the Fluctuations of Oil Resource Price Volatility: A Hurdle for Economic Recovery

Abstract: The influence of oil price volatility on significant international macroeconomic indicators is examined empirically. The vector auto-regression (VAR) system is used to examine the influence of oil price volatility. According to the Granger causality test, impulse response functions, and variance decomposition, economic recovery and investment have been significantly affected by oil price volatility from 2000Q1 to 2020Q4. According to this research, business investment and oil prices have shown great power throughout the international economic meltdown. Volatility in economic activity and oil prices are expected during this crisis, according to the recent COVID-19 outbreak. Furthermore, in the international financial crisis and COVID-19 crises, oil prices and economic growth are strongly linked. We propose that the COVID-19 epidemic and the global financial problems have major effects on economic activity when oil prices fall. The COVID-19 epidemic had the greatest total connectedness between oil prices and economic activities, which suggests that the speed of information propagation between the oil market and financial initiatives was greater during the COVID-19 outbreak than during past global financial crises. There are important consequences for policymakers based on the findings of this research.

Static and Dynamic Connectedness Between NFTs, Defi and Other Assets: Portfolio Implication

Abstract: The paper examines the return and volatility transmission between NFTs, Defi assets, and other assets (oil, gold, Bitcoin, and S&P 500) using the TVP-VAR framework. The results report weak static return and volatility spillovers between NFTs and Defi assets and selected markets, showing that these new digital assets are still relatively decoupled from traditional asset classes. Bitcoin, oil, and half of the NFTs and Defi assets are net transmitters of return and volatility spillovers, whereas rest of the markets are net recipients of spillovers. Our findings show that the dynamic return and volatility connectedness become higher during the initial phase of the COVID-19 pandemic and the cryptocurrency bubble of 2021. We also compute the static and dynamic optimal weights, hedge ratios, and hedging effectiveness for the portfolios of NFTs/other asset and Defi asset/other asset and show that investors and portfolio managers should consider adding NFTs and Defi assets in their portfolios of gold, oil, and stock markets to achieve diversification benefits.

OpenAI ChatGPT Generated Literature Review: Digital Twin in Healthcare

Abstract: — Literature review articles are essential to summarize the related work in the selected field. However, covering all related studies takes too much time and effort. This study questions how Artificial Intelligence can be used in this process. We used ChatGPT to create a literature review article to show the stage of the OpenAI ChatGPT artificial intelligence application. As the subject, the applications of Digital Twin in the health field were chosen. Abstracts of the last three years (2020, 2021 and 2022) papers were obtained from the keyword "Digital twin in healthcare" search results on Google Scholar and paraphrased by ChatGPT. Later on, we asked ChatGPT questions. The results are promising; however, the paraphrased parts had significant matches when checked with the Ithenticate tool. This article is the first attempt to show the compilation and expression of knowledge will be accelerated with the help of artificial intelligence. We are still at the beginning of such advances. The future academic publishing process will require less human effort, which in turn will allow academics to focus on their studies. In future studies, we will monitor citations to this study to evaluate the academic validity of the content produced by the ChatGPT.

Effectiveness of BNT162b2 Vaccine against Omicron in Children 5 to 11 Years of Age

Abstract: Abstract Background Since it was first identified in early November 2021, the B.1.1.529 (omicron) variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread quickly and replaced the B.1.617.2 (delta) variant as the dominant variant in many countries. Data on the real-world effectiveness of vaccines against the omicron variant in children are lacking. Methods In a study conducted from January 21, 2022, through April 8, 2022, when the omicron variant was spreading rapidly, we analyzed data on children in Singapore who were 5 to 11 years of age. We assessed the incidences of all reported SARS-CoV-2 infections (confirmed on polymerase-chain-reaction [PCR] assay, rapid antigen testing, or both), SARS-CoV-2 infections confirmed on PCR assay, and coronavirus disease 2019 (Covid-19)–related hospitalizations among unvaccinated, partially vaccinated (≥1 day after the first dose of vaccine and up to 6 days after the second dose), and fully vaccinated children (≥7 days after the second dose). Poisson regression was used to estimate vaccine effectiveness from the incidence rate ratio of outcomes. Results A total of 255,936 children were included in the analysis. Among unvaccinated children, the crude incidence rates of all reported SARS-CoV-2 infections, PCR-confirmed SARS-CoV-2 infections, and Covid-19–related hospitalizations were 3303.5, 473.8, and 30.0 per 1 million person-days, respectively. Among partially vaccinated children, vaccine effectiveness was 13.6% (95% confidence interval [CI], 11.7 to 15.5) against all SARS-CoV-2 infections, 24.3% (95% CI, 19.5 to 28.9) against PCR-confirmed SARS-CoV-2 infection, and 42.3% (95% CI, 24.9 to 55.7) against Covid-19–related hospitalization; in fully vaccinated children, vaccine effectiveness was 36.8% (95% CI, 35.3 to 38.2), 65.3% (95% CI, 62.0 to 68.3), and 82.7% (95% CI, 74.8 to 88.2), respectively. Conclusions During a period when the omicron variant was predominant, BNT162b2 vaccination reduced the risks of SARS-CoV-2 infection and Covid-19–related hospitalization among children 5 to 11 years of age.

Risk Transmissions between Bitcoin and Traditional Financial Assets during the COVID-19 Era: The Role of Global Uncertainties

Abstract: This paper examines return and volatility connectedness between Bitcoin, traditional financial assets (Crude Oil, Gold, Stocks, Bonds, and the United States Dollar-USD), and major global uncertainty measures (the Economic Policy Uncertainty-EPU, the Twitter-based Economic Uncertainty-TEU, and the Volatility Index-VIX) from April 29, 2013, to June 30, 2020. To this end, the Time-Varying Parameter Vector Autoregression (TVP-VAR) model, dynamic connectedness approaches, and network analyses are used. The results indicate that total spillover indices reached unprecedented levels during COVID-19 and have remained high since then. The evidence also confirms the high return and volatility spillovers across markets during the COVID-19 era. Regarding the return spillovers, Gold is the centre of the system and demonstrates the safe heaven properties. Bitcoin is a net transmitter of volatility spillovers to other markets, particularly during the COVID-19 period. Furthermore, the causality-in-variance Lagrange Multiplier (LM) and the Fourier LM tests' results confirm a unidirectional volatility transmission from Bitcoin to Gold, Stocks, Bonds, the VIX and Crude Oil. Interestingly the EPU is the only global factor that causes higher volatility in Bitcoin. Several potential implications of the results are also discussed.

The i-frame and the s-frame: How focusing on individual-level solutions has led behavioral public policy astray.

Abstract: An influential line of thinking in behavioral science, to which the two authors have long subscribed, is that many of society's most pressing problems can be addressed cheaply and effectively at the level of the individual, without modifying the system in which the individual operates. We now believe this was a mistake, along with, we suspect, many colleagues in both the academic and policy communities. Results from such interventions have been disappointingly modest. But more importantly, they have guided many (though by no means all) behavioral scientists to frame policy problems in individual, not systemic, terms: to adopt what we call the "i-frame," rather than the "s-frame." The difference may be more consequential than i-frame advocates have realized, by deflecting attention and support away from s-frame policies. Indeed, highlighting the i-frame is a long-established objective of corporate opponents of concerted systemic action such as regulation and taxation. We illustrate our argument briefly for six policy problems, and in depth with the examples of climate change, obesity, retirement savings, and pollution from plastic waste. We argue that the most important way in which behavioral scientists can contributed to public policy is by employing their skills to develop and implement value-creating system-level change.