This proposal outlines a machine learning-based approach aimed at improving productivity in haulage operations within ...

The AERCA algorithm performs robust root cause analysis in multivariate time series data by leveraging Granger causal discovery methods. This implementation in PyTorch facilitates experimentation on ...

Halva—‘grapHical Analysis with Latent VAriables’—is a Python package dedicated to statistical analysis of multivariate ordinal data, designed specifically to handle missing values and latent variables ...

As Americans continue to witness senseless violence throughout their communities, the rise of nihilistic violence is raising alarms for law enforcement as officials try to prevent attacks that often ...

Abstract: Clustering multivariate time-series data is crucial for uncovering complex temporal patterns in dynamic environments, such as building indoor conditions and behavior where variables like ...

Abstract: The past decade has witnessed the success of deep learning-based multivariate time series forecasting in Internet of Things (IoT) systems. However, dynamic variable correlation remains a ...

Under different environmental conditions, crop yields differ primarily due to G and E interactions. The Global Rice Array (GRA-IV) is IRRI's fourth flagship project to identify climate-resilient rice ...

In recent years, the advent of great technological advances has produced a wealth of very high-dimensional data, and combining high-dimensional information from multiple sources is becoming ...