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Advanced Materials, EarlyView.Additively manufactured triply periodic minimal surface (TPMS) membranes offer an architecture‐driven alternative to hollow fiber bundles in artificial lungs. Multiphysics simulations and endothelialized prototypes show that the 3D‐printable membrane architecture improves gas exchange, blood flow distribution, and hemocompatibility, enabling ...
Michael Pflaum +14 more
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From Payload‐First Toward Dual‐Mechanism Antibody–Drug Conjugates
Advanced Materials, EarlyView.Conjugation converts potent antibodies into underexposed carriers. This perspective defines the antibody exposure deficit and maps a mechanism‐first design space from payload‐first to antibody‐first ADC architectures, integrating DAR, linker chemistry, and Fc engineering to guide rational design of constructs that balance targeted cytotoxicity with ...
Xavier Pivot +3 more
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Trading Accuracy for Sparsity in Optimization Problems with Sparsity Constraints
SIAM Journal on Optimization, 2010We study the problem of minimizing the expected loss of a linear predictor while constraining its sparsity, i.e., bounding the number of features used by the predictor. While the resulting optimization problem is generally NP-hard, several approximation algorithms are considered.
Nathan Srebro, Shai Shalev-Shwartz
exaly +3 more sources
2009 11th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, 2009
While much is written about the importance of sparse polynomials in computer algebra, much less is known about the complexity of advanced (i.e. anything more than multiplication!) algorithms for them. This is due to a variety of factors, not least the problems posed by cyclotomic polynomials.
James Harold Davenport, Jacques Carette
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While much is written about the importance of sparse polynomials in computer algebra, much less is known about the complexity of advanced (i.e. anything more than multiplication!) algorithms for them. This is due to a variety of factors, not least the problems posed by cyclotomic polynomials.
James Harold Davenport, Jacques Carette
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2021
Despite its generic title, this thesis is about a specific notion of sparsity, the one introduced by McCullagh and Polson (2018). In that paper, the intuitive idea that sparsity, in a statistical framework, refers to those ''phenomena that are mostly negligible or seldom appreciably large'', has, for the first time, been given a mathematical definition.
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Despite its generic title, this thesis is about a specific notion of sparsity, the one introduced by McCullagh and Polson (2018). In that paper, the intuitive idea that sparsity, in a statistical framework, refers to those ''phenomena that are mostly negligible or seldom appreciably large'', has, for the first time, been given a mathematical definition.
openaire +1 more source
2012
This is the first book devoted to the systematic study of sparse graphs and sparse finite structures. Although the notion of sparsity appears in various contexts and is a typical example of a fuzzy notion, the authors devised an unifying classification of general classes of structures. This approach is very robust and it has many remarkable properties.
Nesetril, Jaroslav +1 more
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This is the first book devoted to the systematic study of sparse graphs and sparse finite structures. Although the notion of sparsity appears in various contexts and is a typical example of a fuzzy notion, the authors devised an unifying classification of general classes of structures. This approach is very robust and it has many remarkable properties.
Nesetril, Jaroslav +1 more
openaire +2 more sources
An examination of decomposition sparsity
Digital Signal Processing, 2004Abstract Sparsity of transform domain coefficients is a critical requirement for algorithms employing wavelet transforms to pre-process signals or images. This paper examines a measure of transform domain sparsity which provides a means of comparing wavelet and subband decomposition performance.
Peter D. Dolan +2 more
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Boosting with structural sparsity
Proceedings of the 26th Annual International Conference on Machine Learning, 2009We derive generalizations of AdaBoost and related gradient-based coordinate descent methods that incorporate sparsity-promoting penalties for the norm of the predictor that is being learned. The end result is a family of coordinate descent algorithms that integrate forward feature induction and back-pruning through regularization and give an automatic ...
John C. Duchi, Yoram Singer
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2012
In this paper, we propose an algorithm encouraging group sparsity under some convex constraint. It stems from some applications where the regression coefficients are subject to constraints, for example nonnegativity and the explanatory variables are not suitable to be orthogonalized within groups.
Yi Guo 0001, Junbin Gao, Xia Hong 0001
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In this paper, we propose an algorithm encouraging group sparsity under some convex constraint. It stems from some applications where the regression coefficients are subject to constraints, for example nonnegativity and the explanatory variables are not suitable to be orthogonalized within groups.
Yi Guo 0001, Junbin Gao, Xia Hong 0001
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Investigations on the sensitivity of sparsity measures to the sparsity of impulsive signals
Mechanical Systems and Signal Processing, 2022Zhike Peng +2 more
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