The scarcity of historical financial data has been a huge hindrance for the development of
algorithmic trading models ever since the first models were devised. Most financial models …

Overfitting is an inevitable phenomenon when applying deep learning techniques to financial
data, given the relative scarcity of available historical data and the ever-changing nature of …

In this article, we develop a modular framework for the application of Reinforcement Learning
to the problem of Optimal Trade Execution. The framework is designed with flexibility in …

Generative adversarial networks (GANs) have been shown to be able to generate samples
of complex financial time series, particularly by employing the concept of path signatures, a …

Most of the planet's diversity is concentrated in the tropics, which includes many regions
undergoing rapid climate change. Yet, while climate‐induced biodiversity changes are widely …

In this article, we develop a modular framework for the application of Reinforcement Learning
to the problem of Optimal Trade Execution. The framework is designed with flexibility in …

One of the most crucial challenges of Reinforcement Learning applied to market applications
is that the true environment (the market) is inaccessible and thus we have to decide on how …

The carbon sink capacity of tropical forests is substantially affected by tree mortality. However,
the main drivers of tropical tree death remain largely unknown. Here we present a pan-…

Aim Water availability is the major driver of tropical forest structure and dynamics. Most
research has focused on the impacts of climatic water availability, whereas remarkably little is …

… Objetivo: analizar los resultados clínicos y patológicos de 15 pacientes diagnosticados
de NPMI e intervenidos según las recomendaciones de conferencia de consenso. Material y …