Results 131 to 140 of about 164,931 (293)

Unifying Composition and Process Design: A Heterogeneous Graph Neural Network for Discovering High‐Performance Cu Alloys

Advanced Science, EarlyView.
By overcoming the fixed‐path limitations of conventional machine learning, a heterogeneous graph neural network fundamentally reconstructs material data representation. Integrating variable processing sequences with intrinsic elemental features, this framework enables exploratory optimization across high‐dimensional spaces.
Jie Yin, Qian Lei, Wei Yan, Yue Li, Tong Xie, Shuang Zhou, Chong Wang, Bram Hoex, Qiang Long, Caoyang Jiang, Min Song, Zhou Li, Zhangwei Wang   +12 more
wiley   +1 more source

A Bayesian statistics tutorial for clinical research: Prior distributions and meaningful results for small clinical samples. [PDF]

J Clin Psychol, 2023
Larson C, Kaplan D, Girolamo T, Kover ST, Eigsti IM.   +4 more
europepmc   +1 more source

Generation of Probabilistic Bits by Exploiting Orthogonal Spin Currents in Magnetic Trilayers

Advanced Science, EarlyView.
Fe/Ti/CoFeB trilayers generate orthogonal spin currents that drive stochastic spin–orbit‐torque switching for probabilistic‐bit operation. The switching probability is continuously controlled by the in‐plane magnetic field and drive current, enabling tunable random bit generation.
Donghyeon Han, Chaehyeon Shin, Seok‐Jong Kim, Yunho Jang, Daekyu Koh, Taehwan Kim, Jaeheon Jung, Geon‐Woo Baek, Minseok Kang, Eunseok Kim, Geun‐Hee Lee, Jeongchun Ryu, Makoto Kohda, Junsaku Nitta, Kab‐Jin Kim, Jongsun Park, Kyung‐Jin Lee, Byong‐Guk Park   +17 more
wiley   +1 more source

Bayesian control of the number of servers in a GI/M/c queuing system [PDF]

, 2004
In this paper we consider the problem of designing a GI/M/c queueing system. Given arrival and service data, our objective is to choose the optimal number of servers so as to minimize an expected cost function which depends on quantities, such as the ...
Lillo Rodríguez, Rosa Elvira, Ausín Olivera, María Concepción, Lillo, Rosa E., Wiper, Michael Peter, Wiper, Michael P., Ausín, Concepción   +5 more
core  

Challenges and Opportunities for Bayesian Statistics in Proteomics. [PDF]

J Proteome Res, 2022
Crook OM, Chung CW, Deane CM.
europepmc   +1 more source

STAID: A Self‐Refining Deep Learning Framework for Spatial Cell‐Type Deconvolution with Biologically Informed Modeling

Advanced Science, EarlyView.
STAID is a unified deep learning framework that couples iterative pseudo‐spot refinement with neural network training through a feedback loop and exploits gene co‐expression information to model higher‐order interactions, achieving accurate and robust cell‐type deconvolution in spatial transcriptomics.
Jixin Liu, Shuli Sun, Zhengliang Lv, Xinyu Liu, Yihua Wang, Bingqiang Liu   +5 more
wiley   +1 more source

Large incomplete sample robustness in Bayesian networks [PDF]

, 2008
Under local DeRobertis (LDR) separation measures, the posterior distances between two densities is the same as between the prior densities. Like Kullback - Leibler separation they also are additive under factorization. These two properties allow us the
Smith, J. Q., Daneshkhah, Alireza
core  

Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics. [PDF]

Philos Trans A Math Phys Eng Sci, 2023
Bon JJ, Bretherton A, Buchhorn K, Cramb S, Drovandi C, Hassan C, Jenner AL, Mayfield HJ, McGree JM, Mengersen K, Price A, Salomone R, Santos-Fernandez E, Vercelloni J, Wang X.   +14 more
europepmc   +1 more source

A Data‐Driven Inverse Design Methodology for Magnetic Soft Millirobots Navigating in Confined Spaces

Advanced Science, EarlyView.
A data‐efficient inverse design framework automates the optimization of magnetic soft millirobots for confined‐space navigation. Integrating a physics‐based Cosserat rod model with Bayesian optimization efficiently identifies high‐performance geometries.
Ziyu Ren, Hong Wang, Chak Wang Tse, Yi Zheng, Xi Chen, Wenqi Hu   +5 more
wiley   +1 more source

Bayesian statistics in anesthesia practice: a tutorial for anesthesiologists. [PDF]

J Anesth, 2022
Introna M, van den Berg JP, Eleveld DJ, Struys MMRF.   +3 more
europepmc   +1 more source