Advances in high‐throughput omics technologies are leading plant biology research into the
era of big data. Machine learning (ML) performs an important role in plant systems biology …

Surrogate modeling has been popularized as an alternative to full-scale models in complex
engineering processes such as manufacturing and computer-assisted engineering. The …

In computer vision, anomaly detection (AD) is a challenging task. AD presents additional
difficulties, especially in the realm of medical imaging, for several reasons, one of which …

Computational fluid dynamics (CFD) is known for producing high-dimensional
spatiotemporal data. Recent advances in machine learning (ML) have introduced a myriad …

This is a tutorial and survey paper on kernels, kernel methods, and related fields. We start
with reviewing the history of kernels in functional analysis and machine learning. Then …

Understanding the kinetics and thermodynamics profile of biomolecules is necessary to
understand their functional roles which has a major impact in mechanism driven drug …

Dimensionality reduction is the essence of many data processing problems, including
filtering, data compression, reduced-order modeling and pattern analysis. While traditionally …

This is a tutorial and survey paper on metric learning. Algorithms are divided into spectral,
probabilistic, and deep metric learning. We first start with the definition of distance metric …

Featured Application The solutions presented in the work, based on the use of a multi-
sensor matrix and the analysis of multidimensional data, can be used in practice for …

In this paper we make the case for the emergence of novel kind of bias with the use of
algorithmic decision-making systems. We argue that the distinctive generative process of …