Abstract: With its inherent causal reasoning and superior capacity for handling uncertainty, the belief rule base (BRB) has been widely applied in complex systems modeling. As a generalization of ...

This is the preface for the book by E. N. Dzhafarov, J. V. Kujala, and V. H. Cervantes, titled Contextuality in Random Variables: A Systematic Introduction. It is to be published by Cambridge ...

Splitting variables out is not possible when they are reused as input parameters. Sometimes code changes the type of a local if a pointer to it is passed as a out parameter. Additionally it is not ...

Among the most powerful tools we have as programmers—perhaps the most powerful tools—are functions. We’ve already seen some built-in Python functions, for example, print() and type(). We’ll see many ...

Abstract: The deviation settlement mechanism (DSM) scheme enforces strict regulations on microgrid operators to comply with generation commitment norms set by grid operators. These norms are essential ...

Amid efforts to address energy consumption in modern computing systems, one promising approach takes advantage of random networks of non-linear nanoscale junctions formed by nanoparticles as ...

In recent years, neural networks have once again triggered an increased interest among researchers in the machine learning community. So-called deep neural networks model functions using a composition ...

ABSTRACT: This paper presents a new dimension reduction strategy for medium and large-scale linear programming problems. The proposed method uses a subset of the original constraints and combines two ...

There's an existing issue for Declarative custom functions, which forms the basis for much of this proposal. I'm opening a separate issue here, in order to make a broader proposal building on that, ...

Linear functions are an important concept for students to understand in math class. They can be represented using tables, graphs or equations. Teachers can use various activities to teach students how ...