Results 101 to 110 of about 1,142,003 (329)

π VAE: a stochastic process prior for Bayesian deep learning with MCMC. [PDF]

Stat Comput, 2022
Mishra S, Flaxman S, Berah T, Zhu H, Pakkanen M, Bhatt S.   +5 more
europepmc   +1 more source

Characterizations of completely nondeterministic stochastic processes [PDF]

, 1983
Peter Bloomfield, Nicolas Jewell, Eric Hayashi   +2 more
openalex   +1 more source

Spectrally Tunable 2D Material‐Based Infrared Photodetectors for Intelligent Optoelectronics

Advanced Functional Materials, EarlyView.
Intelligent optoelectronics through spectral engineering of 2D material‐based infrared photodetectors. Abstract The evolution of intelligent optoelectronic systems is driven by artificial intelligence (AI). However, their practical realization hinges on the ability to dynamically capture and process optical signals across a broad infrared (IR) spectrum.
Junheon Ha, Yingshan Ma, Yong Nam An, Sung‐Un An, Hyeon Hak Jung, Suvi‐Tuuli Varjamo, Jiwon Yoo, Junho Min, Hanvit Kim, Faisal Ahmed, Sang Hoon Chae, Young Min Song, Weiwei Cai, Tawfique Hasan, Zhipei Sun, Dong‐Ho Kang, Hyeon‐Jin Shin, Yunyun Dai, Hoon Hahn Yoon   +18 more
wiley   +1 more source

Understanding Alzheimer's disease in the context of aging: Findings from applications of stochastic process models to the Health and Retirement Study. [PDF]

Mech Ageing Dev, 2023
Arbeev KG, Bagley O, Yashkin AP, Duan H, Akushevich I, Ukraintseva SV, Yashin AI.   +6 more
europepmc   +1 more source

Smarter Sensors Through Machine Learning: Historical Insights and Emerging Trends across Sensor Technologies

Advanced Functional Materials, EarlyView.
This review highlights how machine learning (ML) algorithms are employed to enhance sensor performance, focusing on gas and physical sensors such as haptic and strain devices. By addressing current bottlenecks and enabling simultaneous improvement of multiple metrics, these approaches pave the way toward next‐generation, real‐world sensor applications.
Kichul Lee, Osman Gul, Yeongjae Kwon, Jaeseok Jeong, Seokjoo Cho, Jihyeon Ahn, Jehee Yu, Cheolmin Kim, Donho Lee, Hyeonseok Han, Byeongju Lee, Jungrak Choi, Ji‐Hwan Ha, Yongrok Jeong, Kyungnam Kang, Ali Javey, Junseong Ahn, Inkyu Park   +17 more
wiley   +1 more source

Rethinking statistical learning as a continuous dynamic stochastic process, from the motor systems perspective. [PDF]

Front Neurosci, 2022
Vaskevich A, Torres EB.
europepmc   +1 more source

Living Liquid Metal Composites Embedded with Electrogenic Endospores for Next‐Generation Bioelectronics

Advanced Functional Materials, EarlyView.
A new class of living liquid metal composites is introduced, embedding Bacillus subtilis endospores into eutectic gallium–indium (EGaIn). The spores enhance droplet coalescence, strengthen interfacial conductivity, and provide on‐demand electrogenic functionality after germination. The composites exhibit high conductivity, self‐healing, patternability,
Maryam Rezaie, Yang Gao, Seokheun Choi
wiley   +1 more source

Measuring Turbulent Flows: Analyzing a Stochastic Process with Stochastic Tools

Fluids
Assessing drag force and Reynolds stresses in turbulent flows is crucial for evaluating the stability and longevity of hydraulic structures. Yet, this task is challenging due to the complex nature of turbulent flows.
Evangelos Rozos, Jörg Wieland, Jorge Leandro   +2 more
doaj   +1 more source

Mathematical model of the cell signaling pathway based on the extended Boolean network model with a stochastic process. [PDF]

BMC Bioinformatics, 2022
Kim M, Kim E.
europepmc   +1 more source

Mathematical model of biological tissue surface based on one dimensional stochastic process of fractional Brownian motion

, 2021
А В Смирнов, Н. А. Коваленко, O Ryabushkin   +2 more
openalex   +1 more source