Results 271 to 280 of about 5,737,630 (301)

Circuit complexity of slice functions and homogeneous functions

Systems and Computers in Japan, 1994
AbstractThis paper discusses the complexity from the viewpoint of the number of circuit elements for the slice functions and the homogeneous function, which belong to the class of monotonic logic functions. The slice function is one of those functions in which the circuit complexity is almost equal to the monotonic circuit complexity.The n‐input k ...
Shoichi Hirose, Shuzo Yajima
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Adaptive slicing for functional slice inverse regression

Statistical Papers
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zheng, Linjuan, Liang, Beiting, Wang, Guochang   +2 more
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Function recovery based on program slicing

1993 Conference on Software Maintenance, 2002
The identification and extraction of two main kinds of components, environment-dependent operations and domain-dependent functionalities, are proposed. A reference information model drives the process by giving expectations of components and their interface data. Two modified definitions of Weiser's slicing are applied to this function recovery problem:
Filippo Lanubile, Giuseppe Visaggio
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Slice Hyperholomorphic Functions

2016
In the first section of this chapter we give a brief survey of the theory of slice hyperholomorphic functions, and we provide the basic results needed in the sequel. The theory is much more developed and we refer the reader to the Introduction to Part II of the present work for references. Then, the Sections from 6.2 to 6.5 focus on the functions slice
Daniel Alpay, Fabrizio Colombo, Irene Sabadini   +2 more
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Negation is Powerless for Boolean Slice Functions

SIAM Journal on Computing, 1986
See the review in Zbl 0616.94020.
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Function of microglia in organotypic slice cultures

Journal of Neuroscience Research, 1999
We have examined the functional characteristics of microglia in an environment where the cytoarchitecture of the brain is preserved. Using organotypic slice culture under serum-free conditions, microglia initially demonstrated a rounded morphology but after 10 days in vitro (DIV), microglia in the slice were highly branched.
M, Czapiga, C A, Colton
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Network Slice Selection Function for Data Plane Slicing in a Mobile Network

2019 20th Asia-Pacific Network Operations and Management Symposium (APNOMS), 2019
Network Function Virtualization (VNF) is one of the main drivers for the next generation of mobile networks. It allows the creation of multiple and diverse functionalities over a single physical infrastructure thus fulfilling the main requirements for 5G mobile networks that focus on serving heterogeneous ecosystems.
Javier Jose Diaz Rivera, Talha Ahmed Khan, Asif Mehmood, Wang-Cheol Song   +3 more
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Understanding function behaviors through program slicing

WPC '96. 4th Workshop on Program Comprehension, 2002
We present conditioned slicing as a general slicing framework for program comprehension. A conditioned slice consists of a subset of program statements which preserves the behavior of the original program with respect to a set of program executions. The set of initial states of the program that characterize these executions is specified in terms of a ...
A. DE LUCIA, FASOLINO, ANNA RITA, M. MUNRO   +2 more
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Implicit Slicing for Functionally Tailored Additive Manufacturing

Volume 1A: 36th Computers and Information in Engineering Conference, 2016
One crucial component of the additive manufacturing software toolchain is a class of geometric algorithms known as “slicers.” The purpose of the slicer is to compute a parametric toolpath defined at the mesoscale and associated g-code commands, which direct an additive manufacturing system to produce a physical realization of a three-dimensional input ...
John C. Steuben, Athanasios Iliopoulos, John G. Michopoulos   +2 more
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Influence Functions for Sliced Inverse Regression

Scandinavian Journal of Statistics, 2005
The sliced inverse regression is a technique of estimation of the best linear index coefficients \(\beta_i\) in a nonparametric regression model \(y=f(\beta_1x,\dots,\beta_k x,\varepsilon)\), where \(y\) is the response, \(x\) is the vector of regressors, \(f\) is an unknown regression function, and \(\varepsilon\) is an error term.
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