Results 71 to 80 of about 533,476 (267)
Nonlinear Subspace Clustering via Adaptive Graph Regularized Autoencoder
IEEE Access, 2019 Most existing subspace clustering methods focus on learning a meaningful (e.g., sparse or low-rank) representation of the data. However, they have the following two problems which greatly limit the performance: 1) They neglect the intrinsic local ...
Qiang Ji +4 more
doaj +1 more source
Unleashing the Power of Machine Learning in Nanomedicine Formulation Development
Advanced Functional Materials, EarlyView.A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore +7 more
wiley +1 more source
Effective Temporal Graph Learning via Personalized PageRank
EntropyGraph representation learning aims to map nodes or edges within a graph using low-dimensional vectors, while preserving as much topological information as possible.
Ziyu Liao, Tao Liu, Yue He, Longlong Lin
doaj +1 more source
Advanced Functional Materials, EarlyView.Fibrous benzenetrispeptide (BTP) hydrogels, fabricated via strain‐promoted azide‐alkyne cycloaddition (SPAAC) crosslinking, form robust, bioinert networks. These hydrogels can support 3D cell culture, where cell viability and colony growth depend on the fiber content.
Ceren C. Pihlamagi +5 more
wiley +1 more source
Predicting Atomic Charges in MOFs by Topological Charge Equilibration
Advanced Functional Materials, EarlyView.An atomic charge prediction method is presented that is able to accurately reproduce ab‐initio‐derived reference charges for a large number of metal–organic frameworks. Based on a topological charge equilibration scheme, static charges that fulfill overall neutrality are quickly generated.
Babak Farhadi Jahromi +2 more
wiley +1 more source
IET Image Processing, 2022 Benefiting from the good physical interpretations and low computational complexity, non‐negative matrix factorization (NMF) has attracted wide attentions in data representation learning tasks.
Yanfeng Sun +4 more
doaj +1 more source
Artificial Intelligence as the Next Visionary in Liquid Crystal Research
Advanced Functional Materials, EarlyView.The functions of AI in the research laboratory are becoming increasingly sophisticated, allowing the entire process of hypothesis formulation, material design, synthesis, experimental design, and reiterative testing to be automated. In our work, we conceive how the incorporation of AI in the laboratory environment will transform the role and ...
Mert O. Astam +2 more
wiley +1 more source
Deep graph representation learning: methods, applications, and challenges
大数据Graph representation learning has emerged as a crucial research area in recent years, aiming to generate vector representations that accurately capture the structure and features of graphs. These vectors play a vital role in downstream tasks such as node
ZHANG Xulong +3 more
doaj +2 more sources
In Situ 3D Bioprinting: Impact of Cross‐Linking on the Adhesive Properties of Hydrogels
Advanced Functional Materials, EarlyView.In situ 3D bioprinting enables the direct deposition of cell‐laden, adhesive biomaterials for on‐site tissue regeneration. This review provides a comprehensive overview of how cross‐linking influences the bioadhesive properties of hydrogels used in 3D bioprinting, highlighting cross‐linking triggers, bioadhesion mechanisms, polymer interpenetration ...
Odile Romero Fernandez +4 more
wiley +1 more source
A simple yet effective baseline for non-attributed graph classification
, 2019 Graphs are complex objects that do not lend themselves easily to typical learning tasks. Recently, a range of approaches based on graph kernels or graph neural networks have been developed for graph classification and for representation learning on ...
Cai, Chen, Wang, Yusu
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