Results 171 to 180 of about 609,384 (298)

Designing Memristive Materials for Artificial Dynamic Intelligence

Advanced Intelligent Discovery, EarlyView.
Key characteristics required of memristors for realizing next‐generation computing, along with modeling approaches employed to analyze their underlying mechanisms. These modeling techniques span from the atomic scale to the array scale and cover temporal scales ranging from picoseconds to microseconds. Hardware architectures inspired by neural networks
Youngmin Kim, Ho Won Jang
wiley   +1 more source

Reassessing the validity of using weighted linear models to implement multi-generational GWAS-by-subtraction: a response to Evans et al. [PDF]

BMC Res Notes
Woolf B, Gill D, Munafò M, Burgess S.
europepmc   +1 more source

Appendix A: Fundamental Probability Theory and Mathematical Statistics [PDF]

, 1994
Igor Ushakov
openalex   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Mathematical study of silicate and oxide networks through Revan topological descriptors for exploring molecular complexity and connectivity. [PDF]

Sci Rep
Zhang Q, Ahmad Z, Ullah A, Hamed YS, Saleem M, Belay MB.   +5 more
europepmc   +1 more source

Research on the Application of “Autonomous-Cooperative-Inquiry” Teaching Mode in the Course of Probability Theory and Mathematical Statistics under the Background of Informationization

, 2020
Yanhong Wu, Song Juanjuan
openalex   +1 more source

An Effective Teaching Method of the Course “Probability Theory and Mathematical Statistics” in Higher Education by Formative Evaluation [PDF]

, 2015
Shuai Liu, Weina Fu, Renyong Guo, Tieying Liu   +3 more
openalex   +1 more source

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

Advanced Intelligent Discovery, EarlyView.
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu, Rujia Liu, Xinyan Zhang, Xun Chen, Shuhua Yue   +4 more
wiley   +1 more source

Extended Levett trigonometric series. [PDF]

PLoS One
Reynolds R.
europepmc   +1 more source

A Machine Learning Model for Interpretable PECVD Deposition Rate Prediction

Advanced Intelligent Discovery, EarlyView.
This study develops six machine learning models (k‐nearest neighbors, support vector regression, decision tree, random forest, CatBoost, and backpropagation neural network) to predict SiNx deposition rates in plasma‐enhanced chemical vapor deposition using hybrid production and simulation data.
Yuxuan Zhai, Wenrui Wang, Rui Chen, Tong Yu, Xiaojuan Zheng, Hua Shao, Dashan Shang, Ling Li, Lado Filipovic   +8 more
wiley   +1 more source