In the last decade, there has been a growing body of literature addressing the utilization of
complex network methods for the characterization of dynamical systems based on time …

Multifractality is ubiquitously observed in complex natural and socioeconomic systems.
Multifractal analysis provides powerful tools to understand the complex nonlinear nature of …

Modern machine learning has subverted and bypassed the theoretical framework of
Chomsky's generative approach to linguistics, including its core claims to particular insights …

We develop a general approach to distill symbolic representations of a learned deep model
by introducing strong inductive biases. We focus on Graph Neural Networks (GNNs). The …

Anomaly detection for time-series data has been an important research field for a long time.
Seminal work on anomaly detection methods has been focussing on statistical approaches …

The widespread success of large language models (LLMs) has been met with skepticism
that they possess anything like human concepts or meanings. Contrary to claims that LLMs …

Precise wind speed forecasting contributes to wind power consumption and power grid
schedule as well as promotes the implementation of global carbon neutrality policy …

This paper presents a novel framework for generating new chaotic signals for image
encryption that is based on the finite precision of computers. First, we select a system from a …

Nearly all nontrivial real-world systems are nonlinear dynamical systems. Chaos describes
certain nonlinear dynamical systems that have a very sensitive dependence on initial …

We investigate anomaly detection in an unsupervised framework and introduce long short-
term memory (LSTM) neural network-based algorithms. In particular, given variable length …