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Traditionally quants have learnt to pick data apart. Soon they might spend more time making it up Print this page
It wasn’t a surprise that videos of Tom Cruise playing golf and doing magic tricks should rack up millions of views on TikTok earlier this year. The real surprise was that the clips didn’t feature Tom Cruise at all. They were fakes.
The technology behind these ultra-realistic ‘deep fake’ videos, now common on social media platforms, has already found a home in finance where quants are using it to create parallel universes of data to test investment strategies.
Paiblock, a Global FinTech company today announced that it has launched a fully-revamped personal assistant to help consumers make smarter decisions about saving, spending, borrowing and investing.
“Paiblock’s digital assistant is designed to help consumers make good financial decisions.” says Mark Arthur, Founder and CEO of Paiblock. “Keeping a tight leash on spending is the first step toward achieving financial sanity”.
Paiblock unique value proposition cuts across the increasing need to understand and manage debt, investments and savings in ways that are effective on the one hand, and the high demand for automated end-to-end processes that can help unlock new insights that allow consumers to understand their options, on the other hand.
Paiblock, a Global FinTech company today announced that it has launched a fully-revamped personal assistant to help consumers make smarter decisions about saving, spending, borrowing and investing. "Paiblock's
The Go programming language is deemed to be
the most promising programming language today due to its speed and simplicity, and I recommend you to at least get acquainted with it.
Quick Introduction
Generally speaking, Monte Carlo methods (or simulations) consist of a broad class of computational algorithms that rely on repeated random sampling to obtain numerical results. This technique is used throughout areas such as physics, finance, engineering, project management, insurance, and transportation, where a numerical result is needed and the underlying theory is difficult and/or unavailable.
It was invented by John von Neumann, Stanisław Ulam, and Nicholas Metropolis, who were employed on a secret assignment in the Los Alamos National Laboratory, while working on a nuclear weapon project called the Manhattan Project. It was named after a well-known casino town called Monaco, since chance and randomness are core to the modeling approach, similar to a game of roulette.
Abstract
Wastewater-based epidemiology (WBE) is a promising approach for estimating population-wide COVID-19 prevalence through detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in wastewater. However, various methodological challenges associated with WBE would affect the accuracy of prevalence estimation. To date, the overall uncertainty of WBE and the impact of each step on the prevalence estimation are largely unknown. This study divided the WBE approach into five steps (i.e., virus shedding; in-sewer transportation; sampling and storage; analysis of SARS-CoV-2 RNA concentration in wastewater; back-estimation) and further summarized and quantified the uncertainties associated with each step through a systematic review. Although the shedding of SARS-CoV-2 RNA varied greatly between COVID-19 positive patients, with more than 10 infected persons in the catchment area, the uncertainty caused by the excretion rate became limited for the prevalence estimatio