Results 111 to 120 of about 513,547 (285)
Advanced Science, EarlyView.Existing machine learning potentials remain limited in their ability to generalize across the broad chemical and structural variability of C–S–H. Here, a representative DFT‐labeled dataset is constructed through large‐scale model generation, redundancy‐controlled screening, first‐principles optimization, and targeted sampling of non‐equilibrium ...
Yunjian Li +4 more
wiley +1 more source
Dianxin kexue, 2017 A face recognition method named WT-CSRC was proposed by using wavelet transform (WT) and a collaboration of double-dictionary's sparse representation-based classification (CSRC).
Fei GONG +4 more
doaj +2 more sources
Super-sparse principal component analyses for high-throughput genomic data
BMC Bioinformatics, 2010 Background Principal component analysis (PCA) has gained popularity as a method for the analysis of high-dimensional genomic data. However, it is often difficult to interpret the results because the principal components are linear combinations of all ...
Lee Youngjo +3 more
doaj +1 more source
Advanced Science, EarlyView.The engineered MMS@TRP microgel enables targeted, acid‐responsive delivery of a miR‐21 inhibitor to pericytes. It effectively suppresses disc fibrosis by inhibiting TGF‐β signaling while protecting nucleus pulposus cells via sustained, antioxidant tannic acid release.
Fei Ma +12 more
wiley +1 more source
A Geometric Dictionary Learning Based Approach for Fluorescence Spectroscopy Image Fusion
Applied Sciences, 2017 In recent years, sparse representation approaches have been integrated into multi-focus image fusion methods. The fused images of sparse-representation-based image fusion methods show great performance.
Zhiqin Zhu +3 more
doaj +1 more source
Expectation-Maximization for Sparse and Non-Negative PCA [PDF]
, 2008 We study the problem of finding the dom-inant eigenvector of the sample covariance matrix, under additional constraints on the vector: a cardinality constraint limits the number of non-zero elements, and non-negativity forces the elements to have equal ...
Christian D. Sigg, Joachim M. Buhmann
core +1 more source
Q‐LEAP: Millisecond Hyperdimensional Optimization for Full‐Spectrum Optical Metamaterials
Advanced Science, EarlyView.Q‐LEAP integrates physics‐informed residual machine learning with factorization‐machine‐encoded quantum annealing to design full‐spectrum optical metamaterials. It explores a 2108 design space and, in a single 2.56 ms annealing step, reaches 85.83% of the theoretical FoM limit, enabling selective 5‐8 µm emission with 3–5 and 8–14 µm suppression and ∼40×
Zikang Guo +3 more
wiley +1 more source
An Integrated Artificial Intelligence Framework for Nanomedicine Engineering
Advanced Science, EarlyView.This review outlines a closed‐loop AI ecosystem for nanomedicine engineering. By synergizing multimodal data harmonization, objective‐driven AI architectures, and automated high‐throughput validation, this “Design‐Make‐Test‐Analyze” (DMTA) workflow transforms traditional heuristic formulation into a predictive, self‐optimizing paradigm, accelerating ...
Meichen Dong +10 more
wiley +1 more source
Oja's Algorithm for Streaming Sparse PCA
Advances in Neural Information Processing Systems 37Oja's algorithm for Streaming Principal Component Analysis (PCA) for $n$ data-points in a $d$ dimensional space achieves the same sin-squared error $O(r_{\mathsf{eff}}/n)$ as the offline algorithm in $O(d)$ space and $O(nd)$ time and a single pass through the datapoints.
Syamantak Kumar, Purnamrita Sarkar
openaire +3 more sources
Robust sparse principal component analysis. [PDF]
A method for principal component analysis is proposed that is sparse and robust at the same time. The sparsity delivers principal components that have loadings on a small number of variables, making them easier to interpret.
Croux, Christophe +2 more
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