Online tensor learning: Computational and statistical trade-offs, adaptivity and optimal regret
Large tensor learning algorithms are typically computationally expensive and require storing
a vast amount of data. In this paper, we propose a unified online Riemannian gradient …
a vast amount of data. In this paper, we propose a unified online Riemannian gradient …
Spatiotemporal upscaling of sparse air-sea pCO2 data via physics-informed transfer learning
Global measurements of ocean p CO 2 are critical to monitor and understand changes in the
global carbon cycle. However, p CO 2 observations remain sparse as they are mostly …
global carbon cycle. However, p CO 2 observations remain sparse as they are mostly …
Solar Imaging Data Analytics: A Selective Overview of Challenges and Opportunities
We give a gentle introduction to solar imaging data, focusing on the challenges and
opportunities of data-driven approaches for solar eruptions. We present various solar …
opportunities of data-driven approaches for solar eruptions. We present various solar …
An Introduction on Solar Imaging Data Analytic Challenges and Opportunities for Statisticians
We give a gentle introduction to solar imaging data, focusing on challenges and
opportunities of data-driven approaches for solar eruptions. The various solar phenomena …
opportunities of data-driven approaches for solar eruptions. The various solar phenomena …
Development of super plasma bubbles during the 7 September 2017 geomagnetic storm revealed by coupled GITM‐SAMI3 simulations
In this study, we used the coupled GITM (Global Ionosphere Thermosphere Model)‐SAMI3
(Sami3 is Also a Model of the Ionosphere) model to simulate the response of the ionosphere …
(Sami3 is Also a Model of the Ionosphere) model to simulate the response of the ionosphere …
Channel mixer layer: Multimodal fusion toward machine reasoning for spatiotemporal predictive learning of ionospheric total electron content
P Liu, T Yokoyama, T Sori, M Yamamoto - Space Weather, 2024 - Wiley Online Library
The spatiotemporal distribution of Total Electron Content (TEC) in ionosphere determines
the refractive index of electromagnetic wave leading to the radio signal scintillation and …
the refractive index of electromagnetic wave leading to the radio signal scintillation and …
ED‐AttConvLSTM: An ionospheric TEC map prediction model using adaptive weighted spatiotemporal features
In this paper, we propose a novel Total Electron Content (TEC) map prediction model,
named ED‐AttConvLSTM, using a Convolutional Long Short‐Term Memory (ConvLSTM) …
named ED‐AttConvLSTM, using a Convolutional Long Short‐Term Memory (ConvLSTM) …
Conformalized Tensor Completion with Riemannian Optimization
Tensor data, or multi-dimensional array, is a data format popular in multiple fields such as
social network analysis, recommender systems, and brain imaging. It is not uncommon to …
social network analysis, recommender systems, and brain imaging. It is not uncommon to …
Comparison of Global TEC Prediction Performance with two Deep Learning Frameworks
K Yang, Y Liu - Proceedings of the 36th International Technical …, 2023 - ion.org
The ionosphere is a crucial component of Earth's atmosphere and plays a significant role in
radio communication, broadcasting, and radar positioning. Assessing the ionosphere's …
radio communication, broadcasting, and radar positioning. Assessing the ionosphere's …
Spatiotemporal Forecasting of Ionospheric VTEC Using Transformer Model in the Global Scale
S Inturi - 2024 - search.proquest.com
Abstract This Global Navigation Satellite Systems (GNSS), including the United States
Global Positioning System (GPS), are critical for global navigation and timing. The accuracy …
Global Positioning System (GPS), are critical for global navigation and timing. The accuracy …