Results 61 to 70 of about 21,260,567 (325)
Direct cell reprogramming is a stochastic process amenable to acceleration
Nature, 2009 Direct reprogramming of somatic cells into induced pluripotent stem (iPS) cells can be achieved by overexpression of Oct4, Sox2, Klf4 and c-Myc transcription factors, but only a minority of donor somatic cells can be reprogrammed to pluripotency. Here we
J. Hanna +7 more
semanticscholar +1 more source
On the Location of the Maximum of a Continuous Stochastic Process [PDF]
Journal of Applied Probability, 2012 In this short article we will provide a sufficient and necessary condition to have uniqueness of the location of the maximum of a stochastic process over an interval.
Leandro P. R. Pimentel
semanticscholar +1 more source
Advanced Engineering Materials, EarlyView.Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt +8 more
wiley +1 more source
On the Trackability of Stochastic Processes
CoRR, 2020 We consider the problem of tracking an unstable stochastic process $X_t$ by using causal knowledge of another stochastic process $Y_t$. We obtain necessary conditions and sufficient conditions for maintaining a finite tracking error. We provide necessary conditions as well as sufficient conditions for the success of this estimation, which is defined as
Baran Tan Bacinoglu, Yin Sun, Elif Uysal
openaire +2 more sources
What Do Large Language Models Know About Materials?
Advanced Engineering Materials, EarlyView.If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer +2 more
wiley +1 more source
Densities for Stochastic Processes
The Annals of Mathematical Statistics, 1959 Let $\{x_\theta(t), \theta \varepsilon \Omega\}$ be a family of stochastic processes defined by their finite dimensional distributions; that is, $\{F_\theta\lbrack x(t_1), \cdots, x(t_n)\rbrack; \theta \varepsilon \Omega\}$ is given for all finite sets of time points $t_1, \cdots, t_n$.
openaire +3 more sources
The Heston stochastic volatility model in Hilbert space
, 2017 We extend the Heston stochastic volatility model to a Hilbert space framework. The tensor Heston stochastic variance process is defined as a tensor product of a Hilbert-valued Ornstein-Uhlenbeck process with itself. The volatility process is then defined
Benth, Fred Espen +1 more
core +1 more source
Advanced Engineering Materials, EarlyView.This study presents an infrared monitoring approach for direct laser interference patterning (DLIP) combined with a convolutional neural network (CNN). Thermal emission data captured during structuring are used to predict surface topography parameters.
Lukas Olawsky +5 more
wiley +1 more source
EntropyThree-phase induction motors are widely used in various industrial sectors and are responsible for a significant portion of the total electrical energy consumed.
Marlio Antonio Silva +7 more
doaj +1 more source
Frontiers in Integrative Neuroscience, 2018 The variability inherently present in biophysical data is partly contributed by disparate sampling resolutions across instrumentations. This poses a potential problem for statistical inference using pooled data in open access repositories.
Carla Caballero +5 more
doaj +1 more source