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 ...
Abstract: Representing and exploiting multivariate signals requires capturing relations between variables, which we can represent by graphs. Graph dictionaries allow to describe complex relational ...
Abstract: Knowledge graphs have been widely used in various domains such as intelligent question answering, information retrieval, transportation, medicine, e-commerce, and others, and have achieved ...
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 ...
Detecting anomalies in multivariate time series (MTS) is essential for maintaining system safety in industrial environments. Due to the challenges associated with acquiring labeled data, unsupervised ...
If you find this repo useful, please cite our paper. @inproceedings{yi2023fouriergnn, title={Fourier{GNN}: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective}, author={Kun ...
The final, formatted version of the article will be published soon. Continuous traumatic stress has wide-ranging implications for important life outcomes across multiple domains. We present the design ...
At present, with the progress and innovation of technology, the radar communication dual function wave has become a hot research at home and abroad. Excellent integrated waveform can make full use ...
Purpose: This study aimed to develop a predictive model for assessing the efficacy of neoadjuvant chemotherapy (NAC) in patients with Human Epidermal Growth Factor Receptor 2 (HER2)-low breast cancer, ...
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