Applications of Software Implementations of P Systems

2021 ◽  
pp. 31-69
Author(s):  
Gexiang Zhang ◽  
Mario J. Pérez-Jiménez ◽  
Augustín Riscos-Núñes ◽  
Sergey Verlan ◽  
Savas Konur ◽  
...  
Keyword(s):  
P Systems ◽  
Software Implementations

Algorithms for software implementations of RSA

1989 ◽  
Vol 136 (3) ◽  
pp. 166 ◽  
Author(s):  
A. Selby ◽  
C. Mitchell
Keyword(s):  
Software Implementations

Symmetry Breaking and the Turn-On Fluorescence of Small, Highly Strained Carbon Nanohoops

2019 ◽  
Author(s):  
Terri Lovell ◽  
Curtis Colwell ◽  
Lev N. Zakharov ◽  
Ramesh Jasti
Keyword(s):  
Symmetry Breaking ◽  
Theoretical Work ◽  
P Systems ◽  
Quantum Yields ◽  
Size Dependent ◽  
New Class ◽  
Carbon Carbon Bond ◽  
Conjugated Macrocycles ◽  
Structural Manipulation ◽  
Highly Strained

<p>[<i>n</i>]Cycloparaphenylenes, or “carbon nanohoops,” are unique conjugated macrocycles with radially oriented p-systems similar to those in carbon nanotubes. The centrosymmetric nature and conformational rigidity of these molecules lead to unusual size-dependent photophysical characteristics. To investigate these effects further and expand the family of possible structures, a new class of related carbon nanohoops with broken symmetry is disclosed. In these structures, referred to as <i>meta</i>[<i>n</i>]cycloparaphenylenes, a single carbon-carbon bond is shifted by one position in order to break the centrosymmetric nature of the parent [<i>n</i>]cycloparaphenylenes. Advantageously, the symmetry breaking leads to bright emission in the smaller nanohoops, which are typically non-fluorescent due to optical selection rules. Moreover, this simple structural manipulation retains one of the most unique features of the nanohoop structures-size dependent emissive properties with relatively large extinction coefficents and quantum yields. Inspired by earlier theoretical work by Tretiak and co-workers, this joint synthetic, photophysical, and theoretical study provides further design principles to manipulate the optical properties of this growing class of molecules with radially oriented p-systems.</p>

Symmetry Breaking and the Turn-On Fluorescence of Small, Highly Strained Carbon Nanohoops

2019 ◽  
Author(s):  
Terri Lovell ◽  
Curtis Colwell ◽  
Lev N. Zakharov ◽  
Ramesh Jasti
Keyword(s):  
Symmetry Breaking ◽  
Theoretical Work ◽  
P Systems ◽  
Quantum Yields ◽  
Size Dependent ◽  
New Class ◽  
Carbon Carbon Bond ◽  
Conjugated Macrocycles ◽  
Structural Manipulation ◽  
Highly Strained

<p>[<i>n</i>]Cycloparaphenylenes, or “carbon nanohoops,” are unique conjugated macrocycles with radially oriented p-systems similar to those in carbon nanotubes. The centrosymmetric nature and conformational rigidity of these molecules lead to unusual size-dependent photophysical characteristics. To investigate these effects further and expand the family of possible structures, a new class of related carbon nanohoops with broken symmetry is disclosed. In these structures, referred to as <i>meta</i>[<i>n</i>]cycloparaphenylenes, a single carbon-carbon bond is shifted by one position in order to break the centrosymmetric nature of the parent [<i>n</i>]cycloparaphenylenes. Advantageously, the symmetry breaking leads to bright emission in the smaller nanohoops, which are typically non-fluorescent due to optical selection rules. Moreover, this simple structural manipulation retains one of the most unique features of the nanohoop structures-size dependent emissive properties with relatively large extinction coefficents and quantum yields. Inspired by earlier theoretical work by Tretiak and co-workers, this joint synthetic, photophysical, and theoretical study provides further design principles to manipulate the optical properties of this growing class of molecules with radially oriented p-systems.</p>

Rule synchronization for tissue P systems

2021 ◽  
pp. 104685
Author(s):  
Bosheng Song ◽  
Linqiang Pan
Keyword(s):  
P Systems

Neural-like P Systems with Plasmids

2021 ◽  
pp. 104766
Author(s):  
Francis George C. Cabarle ◽  
Xiangxiang Zeng ◽  
Niall Murphy ◽  
Tao Song ◽  
Alfonso Rodríguez-Patón ◽  
...  
Keyword(s):  
P Systems

Automatic Feature Isolation in Network Protocol Software Implementations

2020 ◽  
Author(s):  
Ze Zhang ◽  
Qingzhao Zhang ◽  
Brandon Nguyen ◽  
Sanjay Sri Vallabh Singapuram ◽  
Z. Morley Mao ◽  
...  
Keyword(s):  
Network Protocol ◽  
Software Implementations

The computational power of monodirectional tissue P systems with symport rules

2021 ◽  
pp. 104751
Author(s):  
Bosheng Song ◽  
Shengye Huang ◽  
Xiangxiang Zeng
Keyword(s):  
P Systems ◽  
Computational Power

scholarly journals Fractional ridge regression: a fast, interpretable reparameterization of ridge regression

GigaScience ◽  
2020 ◽  
Vol 9 (12) ◽  
Author(s):  
Ariel Rokem ◽  
Kendrick Kay
Keyword(s):  
Ridge Regression ◽  
Large Scale ◽  
Imaging Data ◽  
Regularization Technique ◽  
Large Scale Data ◽  
Novel Approach ◽  
Manual Exploration ◽  
L2 Norm ◽  
Software Implementations ◽  
Brain Imaging Data

Abstract Background Ridge regression is a regularization technique that penalizes the L2-norm of the coefficients in linear regression. One of the challenges of using ridge regression is the need to set a hyperparameter (α) that controls the amount of regularization. Cross-validation is typically used to select the best α from a set of candidates. However, efficient and appropriate selection of α can be challenging. This becomes prohibitive when large amounts of data are analyzed. Because the selected α depends on the scale of the data and correlations across predictors, it is also not straightforwardly interpretable. Results The present work addresses these challenges through a novel approach to ridge regression. We propose to reparameterize ridge regression in terms of the ratio γ between the L2-norms of the regularized and unregularized coefficients. We provide an algorithm that efficiently implements this approach, called fractional ridge regression, as well as open-source software implementations in Python and matlab (https://github.com/nrdg/fracridge). We show that the proposed method is fast and scalable for large-scale data problems. In brain imaging data, we demonstrate that this approach delivers results that are straightforward to interpret and compare across models and datasets. Conclusion Fractional ridge regression has several benefits: the solutions obtained for different γ are guaranteed to vary, guarding against wasted calculations; and automatically span the relevant range of regularization, avoiding the need for arduous manual exploration. These properties make fractional ridge regression particularly suitable for analysis of large complex datasets.

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