Zobrazeno 1 - 10
of 460
pro vyhledávání: '"SHAHABI, CYRUS"'
Accurately modeling and analyzing time series data is crucial for downstream applications across various fields, including healthcare, finance, astronomy, and epidemiology. However, real-world time series often exhibit irregularities such as misalign
Externí odkaz:
http://arxiv.org/abs/2412.10621
Understanding human mobility behavior is crucial for numerous applications, including crowd management, location-based recommendations, and the estimation of pandemic spread. Machine learning models can predict the Points of Interest (POIs) that indi
Externí odkaz:
http://arxiv.org/abs/2411.15285
Autor:
Zeighami, Sepanta, Shahabi, Cyrus
Machine learning models have demonstrated substantial performance enhancements over non-learned alternatives in various fundamental data management operations, including indexing (locating items in an array), cardinality estimation (estimating the nu
Externí odkaz:
http://arxiv.org/abs/2411.06243
Human mobility modeling from GPS-trajectories and synthetic trajectory generation are crucial for various applications, such as urban planning, disaster management and epidemiology. Both of these tasks often require filling gaps in a partially specif
Externí odkaz:
http://arxiv.org/abs/2411.04381
The abundance of vehicle trajectory data offers a new opportunity to compute driving routes between origins and destinations. Current graph-based routing pipelines, while effective, involve substantial costs in constructing, maintaining, and updating
Externí odkaz:
http://arxiv.org/abs/2411.01325
Poly2Vec: Polymorphic Encoding of Geospatial Objects for Spatial Reasoning with Deep Neural Networks
Encoding geospatial data is crucial for enabling machine learning (ML) models to perform tasks that require spatial reasoning, such as identifying the topological relationships between two different geospatial objects. However, existing encoding meth
Externí odkaz:
http://arxiv.org/abs/2408.14806
Simulating human mobility data is essential for various application domains, including transportation, urban planning, and epidemic control, since real data are often inaccessible to researchers due to expensive costs and privacy issues. Several exis
Externí odkaz:
http://arxiv.org/abs/2408.13918
Smart grids are a valuable data source to study consumer behavior and guide energy policy decisions. In particular, time-series of power consumption over geographical areas are essential in deciding the optimal placement of expensive resources (e.g.,
Externí odkaz:
http://arxiv.org/abs/2408.16017
Publikováno v:
Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'24). Singapore: Springer Nature Singapore, 2024
Diagnosing epilepsy requires accurate seizure detection and classification, but traditional manual EEG signal analysis is resource-intensive. Meanwhile, automated algorithms often overlook EEG's geometric and semantic properties critical for interpre
Externí odkaz:
http://arxiv.org/abs/2405.09568
Despite location being increasingly used in decision-making systems employed in many sensitive domains such as mortgages and insurance, astonishingly little attention has been paid to unfairness that may seep in due to the correlation of location wit
Externí odkaz:
http://arxiv.org/abs/2403.14040