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Abstract: This paper studies the advanced methodologies of differential cryptanalysis with a particular emphasis on higher-order differentials and higher-order differential-linear cryptanalysis, along ...

For homogeneous linear first-order differential equations, it is shown that Picard’s method of successive approximations is effective to furnish a closed-form solution even if the coefficient is an ...

This paper presents In-Context Operator Networks (ICON), a neural network approach that can learn new operators from prompted data during the inference stage without requiring any weight updates.

The solutions to fractional differentials equations are very difficult to investigate. In particular, the solutions of fractional partial differential equations are challenging tasks for ...

It is shown that character functions of degenerate representations of the Virasoro algebra satisfy a linear differential equation in moduli space. In the case of a torus, we obtain an ordinary ...

1 College of Electrical and Information Engineering, Hunan Institute of Engineering, Xiangtan, China 2 School of Computer and Communication Engineering, Changsha University of Science and Technology, ...

Sometimes, it’s easy for a computer to predict the future. Simple phenomena, such as how sap flows down a tree trunk, are straightforward and can be captured in a few lines of code using what ...

1 Institute for Nonlinear Mechanics, Stuttgart, Germany. 2 Instituto Tecnológico Superior de Uruapan, Uruapan, México. The finite element method has established itself as an efficient numerical ...

Riccati equations are a natural next step after you’ve studied linear differential equations. Linear first-order ordinary differential equations look like this: I think I finally get why they’re ...