Results 231 to 240 of about 234,781 (273)

Strain‐Programmable Luminescent Adhesive Patch With Tartrazine‐Mediated Optical Skin Clearing for Photochemical Tissue Bonding

Advanced Functional Materials, EarlyView.
We propose a suture‐complementary approach that integrates optical skin clearing with a strain‐programmable luminescent adhesive patch. Hyaluronic acid promotes transdermal delivery of tartrazine to improve optical clearing and stabilizes its interaction with a photosensitizer. Optical clearing increases the penetration depth of visible light into skin,
Seong‐Jong Kim, Kyungjoo Jo, Kwanghyeon Park, Jun Hee Kim, Jong Hyeong Cho, Inhoo Choi, Sei Kwang Hahn   +6 more
wiley   +1 more source

Nucleation‐Controlled Reconstruction of CuOx for Selective CO2 Electroreduction

Advanced Functional Materials, EarlyView.
The ratio of oxygen vacancies (Ov) and exposed Cu2O (111)/(200) of CuOx precatalyst is modulated by nucleation control of Cu(OH)2 precursor. Low Ov ratio and high ratio of Cu2O (111)/(200) in slow‐nucleated CuOx reconstructs to high‐coordinated oxide‐derived copper (OD‐Cu) during electrochemical CO2 reduction reaction (CO2RR) and exhibits enhanced ...
Ying Ying Ch'ng, Zhipeng Ma, Ming Zhang, Shujie Zhou, Varun Kundi, Priyank Vijaya Kumar, Wenyu Zhong, Yuwei Yang, Jitraporn Vongsvivut, Qinfen Gu, Xiaoxuan Luo, Lixue Jiang, Rahman Daiyan, Zhaojun Han, Rose Amal   +14 more
wiley   +1 more source

Coarsened Spectral Clustering

Borgnat, Pierre, Vaudaine, Rémi, Lassale, Etienne, Gonçalves, Paulo, Gribonval, Rémi, Karsai, Marton   +5 more
openaire   +1 more source

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Correlational spectral clustering

2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008
We present a new method for spectral clustering with paired data based on kernel canonical correlation analysis, called correlational spectral clustering. Paired data are common in real world data sources, such as images with text captions. Traditional spectral clustering algorithms either assume that data can be represented by a single similarity ...
Blaschko, M., Lampert, C.
openaire   +2 more sources

Self-Constrained Spectral Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
As a leading graph clustering technique, spectral clustering is one of the most widely used clustering methods to capture complex clusters in data. Some additional prior information can help it to further reduce the difference between its clustering results and users' expectations.
Liang Bai, Jiye Liang, Yunxiao Zhao
openaire   +2 more sources

Linear Spectral Clustering Superpixel

IEEE Transactions on Image Processing, 2017
In this paper, we present a superpixel segmentation algorithm called linear spectral clustering (LSC), which is capable of producing superpixels with both high boundary adherence and visual compactness for natural images with low computational costs. In LSC, a normalized cuts-based formulation of image segmentation is adopted using a distance metric ...
Jiansheng Chen, Zhengqin Li, Bo Huang
openaire   +2 more sources

Spectral Cluster Maps Versus Spectral Clustering

2020
The paper investigates several notions of graph Laplacians and graph kernels from the perspective of understanding the graph clustering via the graph embedding into an Euclidean space. We propose hereby a unified view of spectral graph clustering and kernel clustering methods.
Sławomir T. Wierzchoń, Mieczysław A. Kłopotek   +1 more
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Spectral sparsification in spectral clustering

2016 23rd International Conference on Pattern Recognition (ICPR), 2016
Graph spectral clustering algorithms have been shown to be effective in finding clusters and generally outperform traditional clustering algorithms, such as k-means. However, they have scalibility issues in both memory usage and computational time. To overcome these limitations, the common approaches sparsify the similarity matrix by zeroing out some ...
Alireza Chakeri, Hamidreza Farhidzadeh, Lawrence O. Hall   +2 more
openaire   +1 more source

Streaming spectral clustering

2016 IEEE 32nd International Conference on Data Engineering (ICDE), 2016
Clustering is a classical data mining task used for discovering interrelated pattern of similarities in the data. In many modern day domains, data is getting continuously generated as a stream. For scalability reasons, clustering the points in a data stream requires designing single pass, limited memory streaming clustering algorithms.
Shinjae Yoo, Hao Huang, Shiva Prasad Kasiviswanathan   +2 more
openaire   +1 more source