Results 81 to 90 of about 16,277 (238)

On non-parametric estimation of the Lévy kernel of Markov processes

Stochastic Processes and their Applications, 2013
We consider a recurrent Markov process which is an Itô semi-martingale. The Lévy kernel describes the law of its jumps. Based on observations X(0),X(Δ),...,X(nΔ), we construct an estimator for the Lévy kernel's density. We prove its consistency (as nΔ->\infty and Δ->0) and a central limit theorem.
openaire   +2 more sources

Explicit Methods for Inverse Source Identification in Transient Diffusion via Truncated Eigenfunction Regularization of Temperature Measurements

Heat Transfer, EarlyView.
ABSTRACT This work presents and compares two explicit approaches for estimating space‐ and time‐dependent source terms in parabolic diffusion equations, derived from analytical solutions obtained through the Classical Integral Transform Technique (CITT).
André J. P. de Oliveira, Diego C. Knupp, Luiz A. S. Abreu, Mousa J. Huntul, David A. Pelta, Antônio J. da Silva Neto   +5 more
wiley   +1 more source

Clustering-Based Construction of Hidden Markov Models for Generative Kernels [PDF]

, 2009
Generative kernels represent theoretically grounded tools able to increase the capabilities of generative classification through a discriminative setting. Fisher Kernel is the first and mostly-used representative, which lies on a widely investigated mathematical background.
Pekalska, Elzbieta, Bicego, Manuele, Cristani, Marco, Murino, Vittorio, Pȩkalska, Elzbieta, Duin, Robert P W   +5 more
openaire   +1 more source

Identifiable regional and neural network abnormalities in early childhood anxiety

JCPP Advances, EarlyView.
Abstract Background Although the neural underpinnings of anxiety have been investigated, most research has been confined to adolescence and adulthood, with less of a focus on the early childhood period. Little is known about the role of anticipatory fear processing early childhood, which is a sensitive period for anxiety development.
Khalil I. Thompson, Yiwen Zhang, Denny Schaedig, Anna Bankston, Anoushka Parnerkar, Shrinidhi Jeyaram, Nadine Melhem, Susan B. Perlman   +7 more
wiley   +1 more source

Probabilistic Multivariate Statistical Process Control via Kernel Parameter Uncertainty Propagation

IEEE Access
Kernel-based multivariate statistical process control (K-MSPC) extends classical latent-variable monitoring to nonlinear industrial processes, but its performance depends strongly on kernel parameters such as lengthscales and variance terms.
Zina-Sabrina Duma, Victoria Jorry, Ayesha Sarfraz, Maria Paola Di Crosta, Tuomas Sihvonen, Lassi Roininen, Satu-Pia Reinikainen   +6 more
doaj   +1 more source

Breaking Extrapolation Barriers: Dual Innovations for Property and Component Design of Multi‐Component Alloy

Materials Genome Engineering Advances, EarlyView.
This study breaks extrapolation barriers in alloy design by merging symbolic regression with latent space sampling. The dual‐strategy framework enables accurate prediction of high‐hardness properties and navigates uncharted compositional spaces. The approach successfully designs novel high‐entropy alloys with hardness exceeding 863.5 HV, demonstrating ...
Zhigang Yu, Boyu Zhao, Jianhua Chen, Jun Luan, Yunying Fan, Xingli Zou, Xionggang Lu   +6 more
wiley   +1 more source

Extending the hyper‐logistic model to the random setting: New theoretical results with real‐world applications

Mathematical Methods in the Applied Sciences, EarlyView.
We develop a full randomization of the classical hyper‐logistic growth model by obtaining closed‐form expressions for relevant quantities of interest, such as the first probability density function of its solution, the time until a given fixed population is reached, and the population at the inflection point.
Juan Carlos Cortés, Ana Navarro‐Quiles, Sorina Madalina Sferle   +2 more
wiley   +1 more source

Supervised Protein Structure Classification Using Topological Persistence With DeltaFold

Proteins: Structure, Function, and Bioinformatics, EarlyView.
ABSTRACT Recent advances in protein structure prediction have considerably increased the number of available structures, underscoring the need for scalable and accurate methods to compare and classify this huge amount of data. Current approaches based on sequence alignment or structural superposition often lack sensitivity and/or are computationally ...
Joseph Nardin‐Gennequin, Gabriela Ciuperca, Céline Brochier‐Armanet, Philippe Malbos   +3 more
wiley   +1 more source

Tensor‐Based Reduction of Linear Parameter‐Varying State‐Space Models

International Journal of Robust and Nonlinear Control, EarlyView.
ABSTRACT The linear parameter‐varying (LPV) framework is a powerful tool for controlling nonlinear (NL) and complex systems, but the conversion of NL models into LPV forms often results in high‐dimensional and overly conservative LPV models. To be able to apply control strategies, there is often a need for model reduction in order to reduce ...
Bogoljub Terzin, E. Javier Olucha, Amritam Das, Siep Weiland, Roland Tóth   +4 more
wiley   +1 more source

Integrating Image Segmentation and Deep Learning to Improve Radio Frequency Propagation Models

International Journal of Satellite Communications and Networking, EarlyView.
ABSTRACT This paper proposes a multi‐sensor approach to improve radio frequency (RF) propagation models, which play a key role in the rapidly expanding field of connected vehicle technology. Focusing on the 1‐ to 20‐GHz frequency range, which is critical for both satellite‐to‐vehicle and base station‐to‐vehicle communications, our study introduces a ...
Jonathan Israel, Xavier Boulanger, Vegard Arneson   +2 more
wiley   +1 more source