Results 61 to 70 of about 731,480 (301)

Abstraction-Based Planning for Uncertainty-Aware Legged Navigation

IEEE Open Journal of Control Systems, 2023
This article addresses the problem of temporal-logic-based planning for bipedal robots in uncertain environments. We first propose an Interval Markov Decision Process abstraction of bipedal locomotion (IMDP-BL). Motion perturbations from multiple sources
Jesse Jiang, Samuel Coogan, Ye Zhao
doaj   +1 more source

A Novel Prediction Approach for Short-Term Renewable Energy Consumption in China Based on Improved Gaussian Process Regression

Energies, 2019
Energy consumption issues are important factors concerning the achievement of sustainable social development and also have a significant impact on energy security, particularly for China whose energy structure is experiencing a transformation ...
Yuansheng Huang, Lei Yang, Chong Gao, Yuqing Jiang, Yulin Dong   +4 more
doaj   +1 more source

Monitoring Water Quality of Valle de Bravo Reservoir, Mexico, Using Entire Lifespan of MERIS Data and Machine Learning Approaches

Remote Sensing, 2020
Remote-sensing-based machine learning approaches for water quality parameters estimation, Secchi Disk Depth (SDD) and Turbidity, were developed for the Valle de Bravo reservoir in central Mexico. This waterbody is a multipurpose reservoir, which provides
Leonardo F. Arias-Rodriguez, Zheng Duan, Rodrigo Sepúlveda, Sergio I. Martinez-Martinez, Markus Disse   +4 more
doaj   +1 more source

Spatial Modeling of Precipitation Based on Data-Driven Warping of Gaussian Processes

Entropy, 2022
Modeling and forecasting spatiotemporal patterns of precipitation is crucial for managing water resources and mitigating water-related hazards. Globally valid spatiotemporal models of precipitation are not available.
Vasiliki D. Agou, Andrew Pavlides, Dionissios T. Hristopulos   +2 more
doaj   +1 more source

Fluid Biomarkers of Disease Burden and Cognitive Dysfunction in Progressive Supranuclear Palsy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Identifying objective biomarkers for progressive supranuclear palsy (PSP) is crucial to improving diagnosis and establishing clinical trial and treatment endpoints. This study evaluated fluid biomarkers in PSP versus controls and their associations with regional 18F‐PI‐2620 tau‐PET, clinical, and cognitive outcomes.
Roxane Dilcher, Charles B. Malpas, Stuart J. McDonald, William T. O’Brien, Craig Despott, Kelly L. Bertram, Matthew P. Pase, Lukas Frontzkowski, Meng Law, Terence J. O’Brien, Lucy Vivash   +10 more
wiley   +1 more source

Efficient ensemble uncertainty estimation in Gaussian processes regression

Machine Learning: Science and Technology
Reliable uncertainty measures are required when using data-based machine learning interatomic potentials (MLIPs) for atomistic simulations. In this work, we propose for sparse Gaussian process regression (GPR) type MLIPs a stochastic uncertainty measure ...
Mads-Peter Verner Christiansen, Nikolaj Rønne, Bjørk Hammer   +2 more
doaj   +1 more source

Random model trees: an effective and scalable regression method [PDF]

, 2010
We present and investigate ensembles of randomized model trees as a novel regression method. Such ensembles combine the scalability of tree-based methods with predictive performance rivaling the state of the art in numeric prediction.
Pfahringer, Bernhard
core   +1 more source

Structure–Function Decoupling of the Sensorimotor and Default Mode Networks in Black Americans With MS

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background and Objectives Multiple sclerosis (MS) exhibits racially disparate rates of disease progression. Black people with MS (B‐PwMS) experience a more severe disease course than non‐Hispanic White people with MS (NHW‐PwMS). Here we investigated structural and functional connectivity as well as structure–function decoupling in the ...
Emilio Cipriano, Giacomo Boffa, Maria Petracca, Marta Ponzano, Nicole Graziano, Claire Wigley, Claire Riley, Jonathan Howard, Pietro Bontempi, Sylvia Klineova, Fred Lublin, Matilde Inglese   +11 more
wiley   +1 more source

Coded Distributed Gaussian Process Regression

IEEE Communications Letters, 2023
In this letter, we propose a coded load balancing method for distributed Gaussian process regression over heterogeneous wireless networks, where users with diverse computational and communications capabilities may offload excessive training data onto a computationally stronger central server to reduce collaborative processing times.
Nikita Zeulin, Olga Galinina, Nageen Himayat, Sergey Andreev   +3 more
openaire   +3 more sources

Mapping Stellar Surfaces. II. An Interpretable Gaussian Process Model for Light Curves

The Astronomical Journal, 2021
The use of Gaussian processes (GPs) as models for astronomical time series data sets has recently become almost ubiquitous, given their ease of use and flexibility.
Rodrigo Luger, Daniel Foreman-Mackey, Christina Hedges   +2 more
doaj   +1 more source