Causal discovery algorithms learn a network that describes the causal dependencies among random variables. A common workflow involves first utilizing conditional independence properties on observation...
In recent years, Marine Natural Products (MNPs) have emerged as a significant source for anticancer drug discovery, as many natural products can offer structural diversity, unique mechanisms of action...
As the Gulf of Maine warms, characterizing seasonal baseline levels of the human pathogen Vibrio parahaemolyticus (Vp) in Eastern oysters (Crassostrea virginica) is crucial, along with synoptic enviro...
The aim of this study is to empirically investigate the existence of a sectoral asset price channel of monetary policy in the region of the six republics of former Yugoslavia. The study constructs sec...
Modeling higher-order interactions (HOI) has emerged as a crucial challenge in complex systems analysis, as many phenomena cannot be fully captured by pairwise relationships alone. Hypergraphs, which ...
Causal discovery aims to infer causal relationships among variables from observational data, typically represented by a directed acyclic graph (DAG). Most existing methods assume independent and ident...
The Gulf of California sustains important fisheries, including those targeting spiny lobsters. Despite their economic importance, little is known about the interactions with their parasites, which pla...
This paper revisits and extends the 2013 development by Rockafellar and Uryasev of the Risk Quadrangle (RQ) as a unified scheme for integrating risk management, optimization, and statistical estimatio...
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