Results 91 to 100 of about 489,537 (279)
Inverse problems in covariate data analysis
Frontiers in Applied Mathematics and StatisticsThe fact that Pearson's correlation coefficient and effect size are perspective functions of covariance parameters demonstrates that how covariance is defined is one of the most important issues in data analysis.
Stanley Luck
doaj +1 more source
Continual Learning for Multimodal Data Fusion of a Soft Gripper
Advanced Robotics Research, EarlyView.Models trained on a single data modality often struggle to generalize when exposed to a different modality. This work introduces a continual learning algorithm capable of incrementally learning different data modalities by leveraging both class‐incremental and domain‐incremental learning scenarios in an artificial environment where labeled data is ...
Nilay Kushawaha, Egidio Falotico
wiley +1 more source
Mathematical Genesis of the Spatio-Temporal Covariance Functions [PDF]
Obtaining new and flexible classes of nonseparable spatio-temporal covariances have resulted in a key point of research in the last years within the context of spatiotemporal Geostatistics.
Fernández-Avilés, G +2 more
core +1 more source
Advanced Science, EarlyView.Using machine learning on a mega‐scale global dataset (n = 1,336,840) reveals a robust personality trait architecture beyond the Big Five. A Big Two model, broadly capturing social engagement and internal mentation, defines a geometric space that links personality to neurocognitive profiles.
Kaixiang Zhuang +7 more
wiley +1 more source
A Functional Estimate of Covariation
Journal of Computational and Graphical Statistics, 2017 ABSTRACTThe analysis of functional data calls for a bivariate functional covariance function σ(s, t) that may be evaluated at any discrete set of points to define a variance-covariance matrix Σ. This article uses finite element methodology to construct a representation of a functional Choleski factor λ(w, s) to define σ(s, t) = ∫λ(w, s)λ(w, t) dw.
openaire +2 more sources
Consensus Formation and Change are Enhanced by Neutrality
Advanced Science, EarlyView.Neutral agents are shown to enhance both the formation and overturning of consensus in collective decision‐making. A general mathematical model and experiments with locusts and humans reveal that neutrality enables robust consensus via simple interactions and accelerates consensus change by reducing effective population size.
Andrei Sontag +3 more
wiley +1 more source
Effect of neural connectivity on autocovariance and cross covariance estimates
BioMedical Engineering OnLine, 2007 Background Measurements of auto and cross covariance functions are frequently used to investigate neural systems. In interpreting this data, it is commonly assumed that the largest contribution to the recordings comes from sources near the electrode ...
Stecker Mark M
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Multivariate Realized Stock Market Volatility [PDF]
We present a new matrix-logarithm model of the realized covariance matrix of stock returns. The model uses latent factors which are functions of both lagged volatility and returns. The model has several advantages: it is parsimonious; it does not require
Gregory H. Bauer, Keith Vorkink
core
Advanced Science, EarlyView.This study investigates how the internal structure of fiber‐reinforced ceramic composites affects their resistance to damage. By combining 3D X‐ray imaging with acoustic emission monitoring during mechanical testing, it reveals how silicon distribution influences crack formation.
Yang Chen +7 more
wiley +1 more source
Linearizing and Forecasting: A Reservoir Computing Route to Digital Twins of the Brain
Advanced Science, EarlyView.A new approach uses simple neural networks to create digital twins of brain activity, capturing how different patterns unfold over time. The method generates and recovers key dynamics even from noisy data. When applied to fMRI, it predicts brain signals and reveals distinctive activity patterns across regions and individuals, opening possibilities for ...
Gabriele Di Antonio +3 more
wiley +1 more source