Results 221 to 230 of about 171,350 (313)

FMint: Bridging Human Designed and Data Pretrained Models for Differential Equation Foundation Model for Dynamical Simulation

Advanced Theory and Simulations, EarlyView.
FMint is introduced as a multi‐modal foundation model that integrates human‐designed solvers and data‐driven methods for fast, accurate simulation of dynamical systems. FMint leverages in‐context learning within a transformer‐based framework to refine coarse numerical solutions.
Zezheng Song, Jiaxin Yuan, Haizhao Yang
wiley   +1 more source

Energy-Efficient Cooperative Transmission in Ultra-Dense Millimeter-Wave Network: Multi-Agent Q-Learning Approach. [PDF]

Sensors (Basel)
Kim SY, Ko H.
europepmc   +1 more source

Q-learning-based UAV-mounted base station positioning in a disaster scenario for connectivity to the users located at unknown positions. [PDF]

J Supercomput, 2023
Mandloi D, Arya R.
europepmc   +1 more source

Performance of Liquid Biopsy‐Based Multi‐Omics Biomarkers for Early Detection of Gynecological Malignancies: A Prospective Study (PERCEIVE‐I)

Advanced Science, EarlyView.
Following a comparative analysis of omics‐based cancer detection models, a novel liquid biopsy‐based multi‐omics combined model is developed for the early detection of gynecological malignancies. By integrating cell‐free DNA methylation and tumor protein markers, the combined model demonstrates high specificity and sensitivity and is uniquely designed ...
Zheng Feng, Huijuan Ge, Jingshu Wang, Yanan Wang, Xiaoran Sun, Bo Yang, Siyu Cao, Chenlian Quan, Qinhao Guo, Yusheng Han, Feidie Duan, Fang liu, Jing Zhao, Guoqiang Wang, Yuzi Zhang, Shangli Cai, Xiaohua Wu, Hao Wen   +17 more
wiley   +1 more source

Calibration Method for Relativistic Navigation System Using Parallel Q-Learning Extended Kalman Filter. [PDF]

Sensors (Basel)
Xiong K, Zhao Q, Yuan L.
europepmc   +1 more source

Detection of Static and Mobile Targets by an Autonomous Agent with Deep Q-Learning Abilities. [PDF]

Entropy (Basel), 2022
Matzliach B, Ben-Gal I, Kagan E.
europepmc   +1 more source

Seek of an Optimal Way by Q-Learning

, 2005
Y. Dahmani, Abdelkader Benyettou
openalex   +1 more source

Interpretable Machine‐Learning and Big Data Mining to Predict the CO2 Separation in Polymer‐MOF Mixed Matrix Membranes

Advanced Science, EarlyView.
To explore the structure‐property relationships of MMM (Mixed Matrix Membranes), the big data mining is employed and a transfer learning model is trained for predicting and selecting the optimal MMM materials. It achieves fast computation for the separation of CO2 in MMM.
Hao Wan, Yue Fang, Min Hu, Shuya Guo, Zhiqiang Sui, Xiaoshan Huang, Zili Liu, Yue Zhao, Hong Liang, Yufang Wu, Hanyu Gao, Zhiwei Qiao   +11 more
wiley   +1 more source

Q-learning improved golden jackal optimization algorithm and its application to reliability optimization of hydraulic system. [PDF]

Sci Rep
Chen D, Wang H, Hu D, Xian Q, Wu B.
europepmc   +1 more source

Deep Q-Learning-Based Buffer-Aided Relay Selection for Reliable and Secure Communications in Two-Hop Wireless Relay Networks. [PDF]

Sensors (Basel), 2023
Zhang C, Liao X, Wu Z, Qiu G, Chen Z, Yu Z.   +5 more
europepmc   +1 more source