scholarly journals Regularized Estimation in High-Dimensional Vector Auto-Regressive Models Using Spatio-Temporal Information

2023 ◽  
Author(s):  
Zhenzhong Wang ◽  
Abolfazl Safikhani ◽  
Zhengyuan Zhu ◽  
David S. Matteson
Keyword(s):  
Temporal Information ◽  
High Dimensional ◽  
Dimensional Vector ◽  
Regularized Estimation ◽  
Auto Regressive ◽  
Spatio Temporal

scholarly journals Tropical Balls and Its Applications to K Nearest Neighbor over the Space of Phylogenetic Trees

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 779
Author(s):  
Ruriko Yoshida
Keyword(s):  
Supervised Learning ◽  
Phylogenetic Trees ◽  
Nearest Neighbor ◽  
Nearest Neighbors ◽  
High Dimensional ◽  
Learning Method ◽  
Dimensional Vector ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors

A tropical ball is a ball defined by the tropical metric over the tropical projective torus. In this paper we show several properties of tropical balls over the tropical projective torus and also over the space of phylogenetic trees with a given set of leaf labels. Then we discuss its application to the K nearest neighbors (KNN) algorithm, a supervised learning method used to classify a high-dimensional vector into given categories by looking at a ball centered at the vector, which contains K vectors in the space.

scholarly journals The Role of Spatio-Temporal Information to Govern the COVID-19 Pandemic: A European Perspective

2021 ◽  
Vol 10 (3) ◽  
pp. 166
Author(s):  
Hartmut Müller ◽  
Marije Louwsma
Keyword(s):  
European Union ◽  
Spatial Distribution ◽  
Spatial Information ◽  
National Level ◽  
Temporal Information ◽  
The European Union ◽  
Member States ◽  
Heavy Burden ◽  
Spatio Temporal

The Covid-19 pandemic put a heavy burden on member states in the European Union. To govern the pandemic, having access to reliable geo-information is key for monitoring the spatial distribution of the outbreak over time. This study aims to analyze the role of spatio-temporal information in governing the pandemic in the European Union and its member states. The European Nomenclature of Territorial Units for Statistics (NUTS) system and selected national dashboards from member states were assessed to analyze which spatio-temporal information was used, how the information was visualized and whether this changed over the course of the pandemic. Initially, member states focused on their own jurisdiction by creating national dashboards to monitor the pandemic. Information between member states was not aligned. Producing reliable data and timeliness reporting was problematic, just like selecting indictors to monitor the spatial distribution and intensity of the outbreak. Over the course of the pandemic, with more knowledge about the virus and its characteristics, interventions of member states to govern the outbreak were better aligned at the European level. However, further integration and alignment of public health data, statistical data and spatio-temporal data could provide even better information for governments and actors involved in managing the outbreak, both at national and supra-national level. The Infrastructure for Spatial Information in Europe (INSPIRE) initiative and the NUTS system provide a framework to guide future integration and extension of existing systems.

MLS: An MAE-aware LiDAR sampling framework for On-road Environments using Spatio-Temporal Information

2021 ◽  
pp. 1-1
Author(s):  
Quan-Dung Pham ◽  
Xuan Truong Nguyen ◽  
Khac-Thai Nguyen ◽  
Hyun Kim ◽  
Hyuk-Jae Lee
Keyword(s):  
Temporal Information ◽  
Spatio Temporal

Kalmag: a high spatio temporal model of the Geomagnetic field

2021 ◽  
Author(s):  
Julien Baerenzung ◽  
Matthias Holschneider
Keyword(s):  
Geomagnetic Field ◽  
Learning Algorithm ◽  
Dynamical Evolution ◽  
Small Scale ◽  
Large Set ◽  
The Core ◽  
Resolution Model ◽  
Auto Regressive ◽  
Spatio Temporal ◽  
Marine Vessels

<p>We present a new high resolution model of the Geomagnetic field spanning the last 121 years. The model derives from a large set of data taken by low orbiting satellites, ground based observatories, marine vessels, airplane and during land surveys. It is obtained by combining a Kalman filter to a smoothing algorithm. Seven different magnetic sources are taken into account. Three of them are of internal origin. These are the core, the lithospheric  and the induced / residual ionospheric fields. The other four sources are of external origin. They are composed by a close, a remote and a fluctuating magnetospheric fields as well as a source associated with field aligned currents. The dynamical evolution of each source is prescribed by an auto regressive process of either first or second order, except for the lithospheric field which is assumed to be static. The parameters of the processes were estimated through a machine learning algorithm with a sample of data taken by the low orbiting satellites of the CHAMP and Swarm missions. In this presentation we will mostly focus on the rapid variations of the core field, and the small scale lithospheric field.  We will also discuss the nature of model uncertainties and the limitiations they imply.</p>

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