Recent advances in estimation techniques have underscored the growing importance of shrinkage estimation and balanced loss functions in the analysis of multivariate normal distributions. These ...
Researchers have proposed a unifying mathematical framework that helps explain why many successful multimodal AI systems work ...
We introduce a flexible parametric family of matrix-valued covariance functions for multivariate spatial random fields, where each con stituent component is a Mat rn process. The model parameters are ...
Abstract: The imagery speech (IS) is the speech that the human beings are thinking in their brain. A brain computer interface (BCI) system is employed to translate the speech thinking in the brain to ...
Tito A. Mijares, The Moments of Elementary Symmetric Functions of the Roots of a Matrix in Multivariate Analysis, The Annals of Mathematical Statistics, Vol. 32, No. 4 (Dec., 1961), pp. 1152-1160 ...
Principal component analysis summarizes high dimensional data into a few dimensions. Each dimension is called a principal component and represents a linear combination of the variables. The first ...
Abstract: Anomaly detection in multivariate time series (MTS) is crucial for various applications in data mining and industry. Current industrial methods typically approach anomaly detection as an ...
Nvidia unveiled the Vera Rubin AI computing platform at CES 2026, claiming up to 10x lower inference token costs and faster ...
A breakthrough deal to attempt to limit global temperature rises was agreed at a conference of world nations in December 2015. These charts from the time show how and why the Earth’s climate is ...
Context Aware RAG is a flexible library designed to seamlessly integrate into existing data processing workflows to build customized data ingestion and retrieval (RAG) pipelines. With Context Aware ...
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