Results 131 to 140 of about 113,531 (269)

MGDP: Mastering a Generalized Depth Perception Model for Quadruped Locomotion

Advanced Science, EarlyView.
ABSTRACT Perception‐based Deep Reinforcement Learning (DRL) controllers demonstrate impressive performance on challenging terrains. However, existing controllers still face core limitations, struggling to achieve both terrain generality and platform transferability, and are constrained by high computational overhead and sensitivity to sensor noise.
Yinzhao Dong, Ji Ma, Yidan Lu, Jiahui Zhang, Wanyue Li, Yeke Chen, Teng Zhang, Xuechao Chen, Zhangguo Yu, Peng Lu   +9 more
wiley   +1 more source

High‐Throughput Data Generation and Transfer Learning Enabled Microstructure‐Property Integrated Design of Nickel‐Based Powder Metallurgy Superalloy

Advanced Science, EarlyView.
An integrated transfer learning framework integrates CALPHAD simulations, diffusion‐multiple experiments, and literature data to predict long‐term microstructural stability and short‐term mechanical properties of Ni‐based powder metallurgy superalloys. Based on these model predictions, a high‐performance, low‐density alloy, USTB‐PM750, is designed from
Zixin Li, Hongtao Zhang, Zichao Peng, Yuheng Zhang, Qian Lv, Yihuan Cao, Xuqing Wang, Huadong Fu, Jianxin Xie   +8 more
wiley   +1 more source

Deciphering Short‐Range Order in 2D Transition Metal Dichalcogenides: From Origin to Multi‐Scale Property Modulation

Advanced Science, EarlyView.
Short‐range order in 2D transition metal dichalcogenides is revealed as a new design paradigm. Driven by chemical affinity and atomic size, it governs properties across scales. Weak ordering tunes site‐resolved magnetism and d‐band centers, while strong ordering eliminates gap states to open band gaps.
Hanyu Liu, Linggang Zhu, Jian Zhou, Zhimei Sun   +3 more
wiley   +1 more source

A two-stage framework with search space pruning for combined algorithm selection and hyperparameter optimization

Journal of King Saud University: Computer and Information Sciences
With the increasing complexity of building and optimizing machine learning models, automated machine learning (AutoML) has attracted much attention over the last decade.
Wu Sun, Hui Li, Panfeng Chen, Mei Chen, Yanhao Wang   +4 more
doaj   +1 more source

The Implementation of Bayesian Optimization for Automatic Parameter Selection in Convolutional Neural Network for Lung Nodule Classification

Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI)
Lung cancer's high mortality rate makes early detection crucial. Machine learning techniques, especially convolutional neural networks (CNN), play a very important role in lung nodule detection.
Kadek Eka Sapta Wijaya, Gede Angga Pradipta, Dadang Hermawan   +2 more
doaj   +1 more source

Efficient Screening of Organic Singlet Fission Molecules Using Graph Neural Networks

Advanced Science, EarlyView.
A high‐throughput screening framework based on graph neural networks (GNNs) and multi‐level validation facilitates the identification of singlet fission (SF) candidates. By efficiently predicting excitation energies across 20 million molecules, and integrating TDDFT calculations, synthetic accessibility assessments, and GW+BSE calculations, this ...
Li Fu, Longfei Lv, Fan Zhang, Si Zhou, Weiwei Gao, Jijun Zhao   +5 more
wiley   +1 more source

Comparative Analysis of Hyperparameter Optimization Using Optuna and Hyperopt for Convolutional Neural Networks

Современные информационные технологии и IT-образование
The process of training a neural network model is controlled by selecting optimal hyperparameters, which have a significant impact on its quality and performance. This impact has been confirmed both theoretically and empirically by numerous studies.
Tatyana Samoilova
doaj   +1 more source

PAIR: Reconstructing Single‐Cell Open‐Chromatin Landscapes for Transcription Factor Regulome Mapping

Advanced Science, EarlyView.
scATAC‐seq analysis is often constrained by limited sequencing depth, extreme sparsity, and pervasive technical missingness. PAIR is a probabilistic framework that restores scATAC‐seq accessibility profiles by directly modeling the native cell–peak bipartite structure of chromatin accessibility.
Yanchi Su, Qi Qi, Yi Fan, Yubo Wang, Gaoyang Hao, Ka‐Chun Wong, Yunhe Wang, Xiangtao Li   +7 more
wiley   +1 more source

High‐Throughput Screening and Interpretable Machine Learning for Rational Design of Bimetallic Catalysts for Methane Activation

Advanced Science, EarlyView.
ABSTRACT Methane's efficient catalytic removal is vital for sustainable development. Bimetallic catalysts, though promising for methane activation, pose a design challenge due to their complex compositional space. This work introduces an integrated framework that combines high‐throughput density functional theory (DFT) and interpretable machine ...
Mingzhang Pan, Tian Zhang, Jiawei Dong, Yubao Xie, Kaifeng Zhong, Wei Guan, Haiqiao Wei, Changcheng Fu, Yaqiong Su   +8 more
wiley   +1 more source

From Cell‐Free Transcriptomes to Single‐Cell Landscapes: Biomarker Discovery and Originating Cell Alteration Analysis via Graph Matrix Factorization

Advanced Science, EarlyView.
CellFreeGMF traces plasma cfRNA to likely originating cell types by integrating single‐cell atlases with graph‐regularized matrix factorization. The method decomposes cfRNA profiles into sample–cell contributions to reconstruct pseudo single‐cell expression.
Wenxiang Zhang, Wenjing Zhang, Hang Wei, Shiyan Liu, Junliang Shang, Weijie Gong, Hanwen Cheng, Xiujuan Lei, Yuhui Kou, Baoguo Jiang   +9 more
wiley   +1 more source