Metanomics: Adaptive market and volatility behaviour in Metaverse

This study presents stylized facts of the fungible tokens/currencies (MANA/USD and SAND/USD) in the Metaverses (Decentraland and The Sandbox). Metaverse currency exchange rate market exhibits very high conditional volatility, albeit no leverage effect, less impact of the real-world crisis (Global Lockdown due to COVID 19 pandemic) and low correlation with either cryptocurrency index (CCi30) or real-world equity index (S&P 500). Surprisingly, MANA and SAND – fungible tokens/ currencies in different Metaverses exhibit significant and increasing correlation between each other. The relative market efficiency of Metaverse currency market is comparable to that observed in the cryptocurrency and equity markets in the real-world

Bacillus Calmette–Guérin-Induced Human Mast Cell Activation Relies on IL-33 Priming

Bacillus Calmette–Guérin (BCG) vaccine is an attenuated strain of Mycobacterium bovis that provides weak protection against tuberculosis (TB). Mast cells (MCs) are tissue-resident immune cells strategically that serve as the first line of defence against pathogenic threats. In this study, we investigated the response of human MCs (hMCs) to BCG. We found that naïve hMCs exposed to BCG did not secrete cytokines, degranulate, or support the uptake and intracellular growth of bacteria. Since we could show that in hMCs IL-33 promotes the transcription of host-pathogen interaction, cell adhesion and activation genes, we used IL-33 for cell priming. The treatment of hMCs with IL-33, but not IFN-γ, before BCG stimulation increased IL-8, MCP-1 and IL-13 secretion, and induced an enhanced expression of the mycobacteria-binding receptor CD48. These effects were comparable to those caused by the recombinant Mycobacterium tuberculosis (Mtb) 19-KDa lipoprotein. Finally, stimulation of hMCs with IL-33 incremented MC-BCG interactions. Thus, we propose that IL-33 may improve the immunogenicity of BCG vaccine by sensitising hMCs

Tokenomics: How “Risky” are the Stablecoins?

This study proposes a new risk measure for stablecoins, that is based on the probability of the stablecoin’s price hitting a threshold exchange rate post which the stablecoin is subjected to the risk of “break the buck/ death spiral”. We also juxtapose the risk measure computed using different models - Vasicek, CIR, ARMA+GARCH and Vasicek+GARCH and suggest the policy implication of the estimated model parameters - rate of reversion (a) and long term mean exchange rate (b) for stablecoin issuers. The study compares the volatility behaviour of the stablecoins with that of the traditional cryptocurrency, Bitcoin, equity index, NASDAQ composite and fiat currency, EURO. Stablecoins tend to be “stable” barring the events such as Terra – Luna crisis, FTX Bankruptcy and Silicon Valley Bank crisis. Traditional asset backed stablecoins – Tether, USD Coin, Binance USD and True USD are less risky than the decentralized algorithmic stablecoin, FRAX and decentralized cryptoasset backed stablecoin, DAI. The proposed risk measure could be of utility to the stablecoin issuers of algorithmic and cryptoasset backed stablecoins and the regulators for setting the capital requirement to guard against the break the buck/ death spiral risk

Beyond the imitation game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting.Comment: 27 pages, 17 figures + references and appendices, repo: https://github.com/google/BIG-benc