Results 261 to 270 of about 75,461 (296)

Data‐Driven Modeling of Composition–Processing–Microstructure Relations for Recycled Aluminum Cast Alloys

Advanced Science, EarlyView.
Interpretable machine learning reveals how composition and processing govern the formation and microstructural burden of Fe‐rich intermetallic compounds in recycled Al–Si–Fe–Mn alloys. By separating morphology selection from morphology‐conditioned burden partitioning, this framework shows that identical Fe contents can yield different intermetallic ...
Jaemin Wang, Waleed Mohammed, Dierk Raabe   +2 more
wiley   +1 more source

Engineering Neuronal Network Connectivity Through Precise and Scalable Electrical Modulation

Advanced Science, EarlyView.
This study presents a scalable all‐electrical method for precise neuronal‐circuit reconfiguration based on high‐density microelectrode arrays. By employing biologically inspired plasticity rules, targeted connectivity changes were successfully induced and quantified across diverse neuronal preparations.
Sreedhar S. Kumar, Yannaël Bossard, Rachel Sava, Tobias Gänswein, Lorenca Sadiraj, Jean‐Samuel Dupré, Manuel Schröter, J. Gray Camp, Fernando Cardes, Julian Bartram, Andreas Hierlemann   +10 more
wiley   +1 more source

SpaMode: A Broadly Applicable Framework for Deciphering Spatial Multi‐Omics Using Multimodal Mixture of Disentangled Experts

Advanced Science, EarlyView.
SpaMode introduces a versatile framework for spatial multi‐omics integration across vertical, horizontal, and mosaic scenarios. By disentangling modality‐invariant and variant features through a mixture‐of‐experts mechanism, it adaptively reconfigures spatially heterogeneous signals.
Xubin Zheng, Xinlei Huang, Xiang Zhou, Dian Meng, Ziyue Qiao, Zhiyuan Yuan, Lixin Cheng   +6 more
wiley   +1 more source

Physics‐Informed Machine Learning for Sustainable Alloy Design: Toward a Recyclable Unified Q&P Steel

Advanced Science, EarlyView.
A physics‐informed property‐bridging framework links high‐throughput hardness screening to tensile performance in quenching and partitioning steels. By transferring metallurgically guided representations across properties, a single alloy composition is designed to achieve multiple strength grades through heat‐treatment tuning alone, offering a ...
Xiaolu Wei, Yong Li, Chenchong Wang, Lingyu Wang, Xiang Song, Keming Mao, Yu Zhang, Wei Xu   +7 more
wiley   +1 more source

Combining Spatial Multi‐Omics Data to Decipher Spatial Domains and Elucidate Cell Heterogeneity Based on Self‐Supervised Graph Learning

Advanced Science, EarlyView.
A self‐supervised multi‐view graph fusion framework integrates spatial multi‐omics, excelling in domain identification and denoising. It reconstructs spatial pseudo‐expression, jointly analyzes multi‐omics data, infers RNA velocity, predicts spatial omics features from single‐cell multi‐omics, and detects spatially dark genes and transcription factors,
Yuejing Lu, Rui Qiao, Ying Li, Junhong Li, Nuoya Yue, Jing Ge, Jiao Wang, Luonan Chen, Peiluan Li   +8 more
wiley   +1 more source

“Membrane‐Guided” Repair Strategy: Precision Delivery of GGT1 Degrader for Targeted Repair and Regeneration of Spinal Cord Neurons

Advanced Science, EarlyView.
This study confirms that GGT1 is a key driver of neuronal ferroptosis following spinal cord injury. We developed NSCm@EA, a biomimetic delivery system coated with neural stem cell membranes, for precise delivery of enocyanin to injured neurons. By combining targeted delivery with ubiquitination degradation mechanisms, this system promotes MGRN1 ...
Tao Yang, Lei Ye, Peigen Xie, Tao Song, Jian Chen, Heng Zhao, Zhengshan Liu, Xi Chen, Jianing Ding, Xintian Ding, Ao Shao, Miaomiao Wu, Fengdong Zhao, Siyue Tao, Tao You   +14 more
wiley   +1 more source

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A review of neural architecture search

Neurocomputing, 2022
Mikhail Burtsev
exaly   +2 more sources

Diversity in Neural Architecture Search

2020 International Joint Conference on Neural Networks (IJCNN), 2020
Neural architecture search (NAS) is usually divided into two phases: model search, where candidate architectures go through an early training for a small number of epochs (e.g., 20) and a search strategy is used to find one or multiple top candidates, and model tuning, where the top candidates are trained fully (e.g., for 600 epochs) and one final best
Wenzheng Hu, Mingyang Li, Changhe Yuan, Changshui Zhang, Jianqiang Wang 0003   +4 more
openaire   +1 more source