This work introduces the use of Gaussian processes (GPs) for estimation and understanding crop development yield using multisensor satellite observations meteorological data. The proposed methodology combines synergistic information on canopy greenness, biomass, soil, plant water content from optical microwave sensors with atmospheric variables typically measured at stations. A composite covari...

Most of the work we do in signal processing these days is data driven. The shift from more traditional and model-driven approaches to those that are driven has also underlined importance explainability our solutions. Because most start with a number modeling assumptions, they comprehensible by very nature their construction. However, this not necessarily case when choose rely heavily on minimiz...

MOTIVATION The analysis of differentially expressed gene sets became a routine in the analyses of gene expression data. There is a multitude of tests available, ranging from aggregation tests that summarize gene-level statistics for a gene set to true multivariate tests, accounting for intergene correlations. Most of them detect complex departures from the null hypothesis but when the null hypo...

Abstract This Comment explores the implications of a lack tools that facilitate an explicable utilization epistemologically richer, but also more involved white-box approaches in AI. In contrast, advances explainable artificial intelligence for black-box have led to availability semi-standardized and attractive toolchains offer seemingly competitive edge over inherently interpretable models te...

Complex classifiers may exhibit “embarassing” failures in cases that would be easily classified and justified by a human. Avoiding such failures is obviously paramount, particularly in domains where we cannot accept this unexplained behavior. In this work, we focus on one such setting, where a label is perfectly predictable if the input contains certain features, and otherwise, it is predictabl...

Nowadays decision tree learning is one of the most popular classification and regression techniques. Though decision trees are not accurate on their own, they make very good base learners for advanced tree-based methods such as random forests and gradient boosted trees. However, applying ensembles of trees deteriorates interpretability of the final model. Another problem is that decision tree l...

We would like to congratulate the authors for producing this interesting and novel methodology for comparison of spatial fields. It is important that this problem be discussed and expanded, especially in the context of large data sets, which is undoubtedly one of the main challenges that any statistician working with environmental space-time data has to face in contemporary research. We have di...