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Mitochondria‐Targeted Nanomotor: H2S‐Driven Cascade Therapy for Hepatocellular Carcinoma
Advanced Materials, EarlyView.A mitochondrial‐targeted nanomotor integrates photodynamic therapy, mild photothermal therapy, and enzyme dynamic therapy into a self‐amplifying cascade through H2S‐driven metabolic reprogramming. Abstract Despite advances in combination therapies for cancer treatment, most strategies rely on modular‐additive designs that lack dynamic molecular cues to
Chengcheng Li +11 more
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Studies on Algorithms and Their Implementations for Fast and Accurate Singular Value Decomposition
, 2013 Hiroki Toyokawa
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Computation of the Singular Value Decomposition Using Mesh-Connected Processors
, 1983 Richard P. Brent +2 more
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A Systolic Architecture for the Singular Value Decomposition
, 1982 Richard P. Brent, Franklin T. Luk
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2018
In Chap. 4 we learned how to diagonalize a square matrix using the Eigen decomposition. Eigen decomposition has many uses, but it has a limitation: it can only be applied to a square matrix. In this chapter, we will learn how to extend the decomposition to a rectangular matrix using a related method known as a Singular Value Decomposition (SVD ...
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In Chap. 4 we learned how to diagonalize a square matrix using the Eigen decomposition. Eigen decomposition has many uses, but it has a limitation: it can only be applied to a square matrix. In this chapter, we will learn how to extend the decomposition to a rectangular matrix using a related method known as a Singular Value Decomposition (SVD ...
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2020
In Chapter 3, we learned that certain types of matrices, which are referred to as positive semidefinite matrices, can be expressed in the following form: $$\displaystyle A= V \varDelta V^T $$ Here, V is a d × d matrix with orthonormal columns, and Δ is a d × d diagonal matrix with nonnegative eigenvalues of A. The orthogonal matrix V can also be
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In Chapter 3, we learned that certain types of matrices, which are referred to as positive semidefinite matrices, can be expressed in the following form: $$\displaystyle A= V \varDelta V^T $$ Here, V is a d × d matrix with orthonormal columns, and Δ is a d × d diagonal matrix with nonnegative eigenvalues of A. The orthogonal matrix V can also be
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1994
Many numerical methods used in application areas such as signal processing, estimation, and control are based on the singular value decomposition (SVD) of matrices. The SVD is widely used in least squares estimation, systems approximations, and numerical linear algebra.
Uwe Helmke, John B. Moore
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Many numerical methods used in application areas such as signal processing, estimation, and control are based on the singular value decomposition (SVD) of matrices. The SVD is widely used in least squares estimation, systems approximations, and numerical linear algebra.
Uwe Helmke, John B. Moore
openaire +1 more source
Randomized Generalized Singular Value Decomposition
Communications on Applied Mathematics and Computation, 2020zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wei, Wei +3 more
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Nonlinear singular value decomposition
2017Linear functions are widely used and well-understood. For example, to solve f(x) = 0 or f(x) = λx, with linear f, we can rely on matrix decompositions (singular value decomposition (SVD), eigenvalue decomposition (EVD), etc.). On the other hand, having nonlinear multivariate vector functions (multiple input-multiple output static nonlinearities), it is
Ishteva, Mariya Kamenova +1 more
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