Results 71 to 80 of about 245,952 (212)

Development and Applications of a Magnetism‐Aware General‐Purpose Atomic Cluster Expansion Potential for the Fe‐O‐H Ternary System

Advanced Science, EarlyView.
Hydrogen‐driven iron oxide reduction, water‐iron corrosion, and hydrogen embrittlement of metallic iron share the same Fe‐O‐H chemistry. A general‐purpose Atomic Cluster Expansion potential covers this chemistry across the full concentration range with explicit Ising‐like treatment of magnetism.
Baptiste Bienvenu, Mira Todorova, Jörg Neugebauer, Dierk Raabe, Matous Mrovec, Ralf Drautz   +5 more
wiley   +1 more source

Privacy Challenges in Meta-Learning: An Investigation on Model-Agnostic Meta-Learning

CoRR
Meta-learning involves multiple learners, each dedicated to specific tasks, collaborating in a data-constrained setting. In current meta-learning methods, task learners locally learn models from sensitive data, termed support sets. These task learners subsequently share model-related information, such as gradients or loss values, which is computed ...
Mina Rafiei, Mohammadmahdi Maheri, Hamid R. Rabiee 0001   +2 more
openaire   +2 more sources

UCtracker: A Deep Learning–Based DNA Methylation Model for Noninvasive Diagnosis and Recurrence Surveillance of Urothelial Carcinoma in a Prospective Study

Advanced Science, EarlyView.
We developed UCtracker, a urine DNA methylation–based deep learning model, for noninvasive diagnosis and postoperative surveillance of urothelial carcinoma. UCtracker demonstrates high diagnostic accuracy, robustness at ultralow sequencing depth, early recurrence detection, and dynamic risk‐stratified monitoring of molecular residual disease ...
Shengwei Xiong, Gaojie Li, Yucai Wu, Yuan Liang, Heng Guo, Yu Zhang, Gengyan Xiong, Long Tian, Xin Zhang, Ye Tian, Zhengguo Ji, Bin Guo, Yue Shi, Jian Fan, Zhihua Li, Yanqing Gong, Shiming He, Xuesong Li, Weimin Ci, Liqun Zhou   +19 more
wiley   +1 more source

On the ERM Principle in Meta-Learning

CoRR
Classic supervised learning involves algorithms trained on $n$ labeled examples to produce a hypothesis $h \in \mathcal{H}$ aimed at performing well on unseen examples. Meta-learning extends this by training across $n$ tasks, with $m$ examples per task, producing a hypothesis class $\mathcal{H}$ within some meta-class $\mathbb{H}$. This setting applies
Yannay Alon, Steve Hanneke, Shay Moran, Uri Shalit   +3 more
openaire   +2 more sources

Architecture‐Engineered Semi‐Crystalline Microporous Polymer Membranes for Precise Molecular Separation

Advanced Science, EarlyView.
Guided by a Random Forest model, a π–π‐driven ordered stacking strategy deploys functionally distinct substructures to restrict chain segment motion and increase free volume, while preserving the intermolecular interactions that maintain structural integrity.
Zi‐Meng Xu, Hao Zhuo, Song‐Hui Zhi, Xiao‐Feng Zhong, Pan‐Pan Zhang, Si‐Yuan Yang, Wen‐Yong Yang, Guang‐Yong Shen, Ya‐Zhao Li, Yi Li, Ming Xue   +10 more
wiley   +1 more source

Terahertz Channel Modeling, Estimation and Localization in RIS‐Assisted Systems

Advanced Electronic Materials, EarlyView.
Reconfigurable intelligent surfaces have become a recent intensive research focus. Based on practical applications, channel strategies for RIS‐assisted terahertz wireless communication systems are categorized into three different types: channel modeling, channel estimation, and channel localization.
Hongjing Wang, Weipeng Wang, Xuan Cong, Shiyu Wang, Gao Li, Sen Gong, Lin Huang, Hongxin Zeng, Yaxin Zhang, Ziqiang Yang   +9 more
wiley   +1 more source

Recent Advances in Programmable Metasurfaces and Meta‐Devices

Advanced Electronic Materials, EarlyView.
Programmable metasurfaces enable various novel functionalities by dynamically tuning electromagnetic wavefronts. This article provides a comprehensive review of recent advances in microwave and terahertz programmable metasurfaces, covering electrical, thermal, optical, and mechanical control mechanisms.
Linda Shao, Chong He, Nan Wang, Liming Si, Weiren Zhu   +4 more
wiley   +1 more source

Machine Learning‐Assisted Design and Performance Prediction of a Compact Dual‐Band Polarization‐Insensitive THz Metamaterial Absorber for Skin‐Cancer‐Related Refractive‐Index Sensing

Advanced Electronic Materials, EarlyView.
A compact QASRR‐based THz metamaterial absorber enables polarization‐insensitive dual‐band absorption and skin‐cancer‐related refractive‐index sensing through measurable resonance shifts. Field, surface‐current, and circuit analyses clarify the dual‐resonance mechanism, while StackNet‐assisted prediction accurately estimates the simulated absorption ...
Md. Murad Kabir Nipun, S. M. Rifatur Rana, Md. Golam Morshed, Imrose Jahan, F. A. Sabbir Ahamed, Md. Jahedul Islam   +5 more
wiley   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Measuring Nutrition Security Using the Consumer Food Data System Datasets

Applied Economic Perspectives and Policy, EarlyView.
ABSTRACT Nutrition security is an emerging concept lacking a consensus definition, conceptualization, or standardized measure. This perspectives manuscript synthesizes findings from two previously published analyses to assess the feasibility of using available measures of key dimensions of nutrition security from two Consumer Food Data System (CFDS ...
Vibha Bhargava, Jung Sun Lee, Travis A. Smith   +2 more
wiley   +1 more source