Results 161 to 170 of about 110,828 (307)
Obstacle-aware Gaussian Process Regression
CoRRObstacle-aware trajectory navigation is crucial for many systems. For example, in real-world navigation tasks, an agent must avoid obstacles, such as furniture in a room, while planning a trajectory. Gaussian Process (GP) regression, in its current form, fits a curve to a set of data pairs, with each pair consisting of an input point 'x' and its ...
openaire +2 more sources
Advanced Science, EarlyView.ABSTRACT Acute pancreatitis (AP) begins with pancreatic local inflammation, leading to the onset of systemic inflammatory response syndrome (SIRS), followed by compensatory anti‐inflammatory response syndrome (CARS), which causes immune paralysis and higher mortality rate.
Liwei Liu +15 more
wiley +1 more source
In Vivo Microplastic Detection With Photoacoustic Imaging
Advanced Science, EarlyView.ABSTRACT Microplastics are posing an escalating threat to both ecological systems and human health. Yet, current methods for investigating their bioaccumulation are highly invasive, requiring destructive analysis of ex vivo tissues via mass spectrometry, dye labelling, or Raman microspectroscopy.
Joseph C. Bear +9 more
wiley +1 more source
This document describes the new features in version 2.x of the tgp package for R, implementing treed Gaussian process (GP) models. The topics covered include methods for dealing with categorical inputs and excluding inputs from the tree or GP part of the
Robert B. Gramacy, Matthew Alan Taddy
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Unraveling the Molecular Mechanisms Underlying Spontaneous Multipolar Mitosis Through CIN‐seq
Advanced Science, EarlyView.Multipolar mitosis, a hallmark of chromosomal instability (CIN), drives tumor heterogeneity but is challenging to study in live cells. Using CIN‐seq, a single‐cell multiomics method, we profiled rare CIN events and identified mechanisms associated with viable multipolar mitosis, including PTEN attenuation, Rho GTPase‐driven cytokinesis failure, and ...
Pin‐Rui Su +10 more
wiley +1 more source
Prior elicitation and variable selection for bayesian quantile regression [PDF]
, 2013 This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Bayesian subset selection suffers from three important difficulties: assigning priors over model space, assigning priors to all components of the regression
Al-Hamzawi, Rahim Jabbar Thaher
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Advanced Science, EarlyView.ABSTRACT Brown and beige adipocytes dissipate energy as heat, yet effective strategies to enhance their mitochondrial efficiency remain limited. Here, we identify Agnuside (AGN) as a selective stabilizer of the complex I assembly factor NDUFAF6. AGN directly binds cytosolic NDUFAF6, suppresses its ubiquitination, prolongs its half‐life, and facilitates
Qingwen Zhao +7 more
wiley +1 more source
Gaussian Process Regression Networks
We introduce a new regression framework, Gaussian process regression networks (GPRN), which combines the structural properties of Bayesian neural networks with the non-parametric flexibility of Gaussian processes.
Andrew Gordon Wilson +2 more
core +1 more source
Machine Learning for Green Solvents: Assessment, Selection and Substitution
Advanced Science, EarlyView.Environmental regulations have intensified demand for green solvents, but discovery is limited by Solvent Selection Guides (SSGs) that quantify solvent sustainability. Training a machine learning model on GlaxoSmithKline SSG, a database of sustainability metrics for 10,189 solvents, GreenSolventDB is developed. Integrated with Hansen solubility metrics,
Rohan Datta +4 more
wiley +1 more source
Fast gaussian process regression using kd-trees
, 2006 The computation required for Gaussian process regression with n training examples is about O(n 3) during training and O(n) for each prediction. This makes Gaussian process regression too slow for large datasets.
Yirong Shen +2 more
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