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Related Concept Videos

¹H NMR Signal Multiplicity: Splitting Patterns01:13

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When protons A and X are coupled, their nuclear spin energy levels are slightly modified. This is because the energy required to excite proton A to a spin state parallel to proton X is slightly different from the energy required for it to become anti-parallel to spin X. Consequently, there are two possible excitation frequencies for A (A1 and A2), depending on the spin state of X, and vice versa. The mutual nature of coupling implies that the difference between frequencies A1 and A2, indicated...
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The lateral view of the cranium is dominated by temporal, sphenoid, and ethmoid bones.
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¹H NMR: Complex Splitting01:13

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A proton M that is coupled to a proton X results in doublet signals for M. However, NMR-active nuclei can be simultaneously coupled to more than one nonequivalent nucleus. When M is coupled to a second proton A, such as in styrene oxide, each peak in the doublet is split into another doublet.
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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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After budding out from the ER membrane, some COPII vesicles lose their coat and fuse with one another to form larger vesicles and interconnected tubules called vesicular tubular clusters or VTCs. These clusters constitute a compartment at the ER-Golgi interface known as ERGIC (Endoplasmic Reticulum Golgi Intermediate Compartment). The ERGIC is a mobile membrane-bound cargo transport system that sorts proteins secreted from ER and delivers them to the Golgi.
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Related Experiment Video

Updated: Jan 24, 2026

Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics
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Split Multiplicative Multi-view Subspace Clustering.

Zhiyong Yang, Qianqian Xu, Weigang Zhang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |May 17, 2019
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces Split Multiplicative Multi-view Subspace Clustering (SM2SC) for multi-view data. SM2SC effectively extracts consistent structures from diverse views, improving subspace clustering performance.

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    Area of Science:

    • Machine Learning
    • Data Science
    • Computer Vision

    Background:

    • Multi-view subspace clustering methods often struggle with integrating view-specific information into a consensus structure.
    • Existing approaches may fail due to inherent differences between individualized and consensus structures.

    Purpose of the Study:

    • To develop a novel multi-view subspace clustering method that extracts consistent components from view-specific structures.
    • To address the limitations of current methods in handling diverse components within multi-view datasets.

    Main Methods:

    • Proposes Split Multiplicative Multi-view Subspace Clustering (SM2SC).
    • Employs a multiplicative decomposition scheme for structural consistency.
    • Utilizes a variable splitting scheme to extract consistent components from view-specific structures.
    • An alternating optimization algorithm is used to solve the non-convex optimization problem.

    Main Results:

    • The proposed alternating optimization algorithm is proven to converge to a critical point.
    • Empirical studies on real-world datasets demonstrate the practical efficacy of SM2SC.
    • The method successfully extracts valuable, structurally consistent components from multi-view data.

    Conclusions:

    • SM2SC offers an effective strategy for multi-view subspace clustering by ensuring structural consistency.
    • The method outperforms existing approaches in extracting consensus structures from diverse data views.
    • The developed algorithm provides a reliable optimization solution for the proposed model.